Data Pipeline vs ETL: Understanding the Key Differences and Use Cases

Data pipelines and ETL (Extract, Transform, Load) are two similar concepts that are related to data movement and processing. However, ETL is a narrower thing and is actually a specific type of data pipeline. Data pipeline, in turn, is a broad concept that includes different types of data movement and processing activities, including ETL, real-time streaming, etc. ETL, as may be seen from the name, focuses solely on extracting, transforming, and loading data to make it usable for efficient data storage and analytics.

In this post, we’ll dig deeper into the difference between data pipelines and ETL, and we will illustrate some of their use cases.

What Is a Data Pipeline?

A data pipeline is a sequence of data processing steps used to safely transfer data from one system to another. Data pipelines facilitate smooth data movement from different sources to destinations like data warehouses, databases, or data lakes.

Simply put, a data pipeline is a roadmap that helps your data safely get from point A to point B in a smooth, uninterrupted way.

Data pipelines automate data handling and transformation, ensuring consistency, reliability, and timeliness. This automation supports real-time analytics, prompt decision-making, and effective data management. Without well-structured data pipelines, businesses may face challenges related to data management and integrity, which can lead to operational bottlenecks and analytical errors.

What Is an ETL Pipeline?

An ETL (Extract, Transform, Load) pipeline is a special type of data pipeline made up of three crucial steps: extracting data from various sources, transforming it into an appropriate format, and loading it into a destination system. ETL is extremely important for effective data analytics, comprehensive reporting, and strategic business intelligence.

In our example, ETL is not a point A to point B highway, but a lifecycle of consumable goods. Raw materials from different sources are taken to a factory where they are transformed into understandable products, which are then moved to a store to be consumed by buyers.

ETL is still a type of data pipeline, but its purpose is to transform the initial raw data. General data pipelines, by contrast, may include simpler processes, such as direct data transfers, without any transformation.

The three stages of an ETL pipeline look like this: The three stages of an ETL pipeline look like this:

\n
• Extract: Raw data is collected from multiple sources, including databases, applications, or flat files. The objective of this stage is to simply collect the data.
\n
• Transform: The extracted data undergoes various operations to ensure data cleanliness, accuracy, and compatibility with the destination system. Typical processes at this stage ensure that data fits the requirements of a target system and include data filtering, enrichment, aggregation, computational logic, and type conversions.
\n
• Load: The transformed data is loaded into the destination location, whether it is a data warehouse, database, or data lake. This phase can be done either incrementally in batches or continuously in real-time, depending on what type suits your business needs and operations.
\n

Key Differences Between Data Pipeline and ETL

Data pipelines and ETL are obviously different, despite sharing some conceptual similarities. Let’s take a closer look at the main differences between these two:

Purpose

ETL pipelines specialize in extracting, transforming, and loading data into target systems like data warehouses or cloud platforms, explicitly preparing data for analytics. In contrast, data pipelines transfer data directly from one system to another, often without significant transformations, facilitating smooth integration across various sources and destinations.

Data transformation

Data transformation is a core part of ETL pipelines, involving extensive data cleaning, enriching, and reformatting to ensure high-quality and meaningful results. Data pipelines may bypass these transformations entirely, simply transferring data in its original form, focusing more on seamless data movement. ETL pipelines integrate data; data pipelines generally deliver it.

Process complexity

ETL pipelines are inherently more intricate, driven by the depth of their transformation processes, which is optimal for data warehousing, business intelligence, and complex analytical tasks. On the other hand, data pipelines are typically less complex, which makes them ideal for simpler, real-time data streaming or straightforward integration scenarios that don’t require heavy data preparation.

Processing methods

ETL pipelines commonly rely on batch processing for scheduled handling of large datasets, though real-time processing is also possible. This structured approach suits periodic, substantial data updates. Data pipelines, however, comfortably accommodate both batch and real-time processing, effectively supporting applications that demand continuous and immediate data flow.

Scalability

Due to their intensive data transformation requirements, ETL pipelines tend to be less flexible and demand more resources, potentially complicating scalability but prioritizing quality. In contrast, data pipelines are more flexible, scaling easily and efficiently to manage dynamic data volumes and diverse data types.

Use cases

ETL pipelines are ideal for integrating, preparing, and centralizing data from various sources, such as disparate locations of legacy enterprise systems, into a consolidated data system (say, SAP Cloud ERP) for analytical purposes. Meanwhile, data pipelines swiftly move data across systems, such as streaming activity logs to real-time analytics platforms for immediate insights.

Data quality

Ensuring data quality and governance is fundamental to ETL pipelines, incorporating thorough data validation, cleansing, and consistency checks during transformation. Data pipelines, in turn, may prioritize speed over rigorous data quality checks, primarily ensuring rapid and efficient data transfers without extensive validation.

ETL vs Data Pipelines: Use Cases

Let’s examine how data pipelines and ETL are applied in companies to streamline processes, increase agility, allow for competitive analytics, and make relevant decisions.

Use cases of data pipelines

Real-time analytics

Data pipelines stream data from sources like websites, applications, or user interactions into analytics platforms. This allows instant updates of analytics dashboards that enable businesses to monitor performance, user behavior, and system status continuously and in real-time.

For example, this may be seen in tracking real-time user interactions on E-commerce websites to adjust recommendations dynamically.

IoT and sensor data processing

IoT devices generate vast amounts of continuous, real-time data. Data pipelines efficiently capture, move, and process this sensor data to facilitate immediate alerts, predictive maintenance, or timely operational insights.

A good example is real-time monitoring of industrial equipment to prevent downtime through proactive maintenance.

Machine learning model training

Machine learning (ML) requires consistent and continuous data streams. Data pipelines automate data ingestion into ML environments, enabling frequent training, re-training, and deployment of accurate predictive models.

Automatic ingesting of transactional and user data into ML platforms to continuously improve recommendation models is a good illustration of this use case.

Multi-cloud or SaaS integration

Data pipelines simplify integration across multiple cloud platforms or SaaS applications, efficiently synchronizing data and ensuring real-time interoperability.

For example, data pipelines ensure seamless real-time data synchronization between ERP systems and CRM platforms (e.g., SAP Cloud ERP and Salesforce integration).

ETL Pipeline Use Cases

Enterprise data warehousing and reporting

ETL pipelines consolidate data from disparate enterprise sources into centralized data warehouses, ensuring comprehensive, high-quality datasets suitable for business intelligence, detailed reporting, and long-term analytical queries.

This use case can be illustrated by combining sales, HR, and inventory data into a central data warehouse for detailed cross-departmental analytics.

Regulatory compliance and auditing

ETL pipelines ensure regulatory compliance by systematically extracting, validating, transforming, and securely storing data necessary for audit trails and regulatory reporting.

Preparing financial transaction data for quarterly audits and regulatory reporting in financial institutions is handled with ETL.

SAP data consolidation

ETL pipelines handle ERP data from various SAP and non-SAP systems, consolidating complex financial, supply chain, and operational datasets for easier, more consistent analysis and reporting.

For example, ETL helps integrate SAP data from regional offices to provide global consolidated financial statements and supply chain analytics.

Combining ETL and data pipelines

In some cases, businesses can use both ETL and data pipelines in collaboration. This approach allows each pipeline to perform its specific tasks, benefitting companies with both ETL pipelines and data pipelines.

Structured ERP/financial data (ETL)

ETL pipelines perform rigorous transformations and quality checks for structured, sensitive, and transactional ERP or financial data when moving from various sources to SAP systems, ensuring accurate, reliable insights.

Use case: Processing and integrating monthly financial data from disparate sources into SAP Cloud ERP systems to support complex reporting and budgeting analyses.

Customer behavior, IoT, and log data (data pipelines)

Real-time data pipelines stream data directly from customer interactions, sensors, or application logs, ensuring timely insights and responsiveness to changing market trends or user demands.

Use case: Capturing real-time customer interactions on mobile apps or website logs, providing instant feedback to marketing teams, and improving user satisfaction dynamically by avoiding stockouts.

How DataLark Streamlines ETL and Data Pipelines

Both ETL and data pipelines need supervision to fix operational issues in a timely manner, as well as automation to streamline data movements and increase data processing speed for timely decision-making and prompt market response.

DataLark, a versatile data pipeline automation platform, will be a good choice when it comes to pipeline automation. The solution offers a robust and unified approach to simplifying ETL and data pipeline management with the help of its no-code, intuitive drag-and-drop interface. This allows users to create, orchestrate, and manage intricate data workflows without extensive technical expertise, decreasing the IT burden. Additionally, the visualized data mapping significantly reduces implementation time, enabling businesses to quickly automate their data flows.

DataLark can be deployed on-premise, in the cloud, or in hybrid environments, which makes the solution suitable for a broad range of businesses.

DataLark’s comprehensive integration capabilities support a vast range of connectors, notably deep SAP integration (SAP ECC, S/4HANA, and others), allowing seamless bidirectional data synchronization across SAP and non-SAP systems. This is especially beneficial in ETL scenarios where structured data from various systems and legacy applications must be consolidated reliably and securely into the ERP system for further analytics and processing.

DataLark supports trigger-based and schedule-based automation, so businesses can choose the option that suits them better and set up automation easily. Additionally, comprehensive data monitoring and automated alerts provide transparency of the data pipeline and ETL processes, allowing for continuous data flow monitoring and timely issue resolution.

DataLark’s Hybrid Approach in Action: Combining ETL and Data Pipelines

Project: SAP S/4HANA Migration with Ongoing Operations

Challenge: A large enterprise migrating to S/4HANA while maintaining business operations requires both batch data migration and real-time operational data flow.

ETL component (historical data migration with complex transformations):

\n
• Extract 10 years of transactional data from SAP ECC
\n
• Transform to S/4HANA data model (Universal Journal, new table structures)
\n
• Load in controlled batches with extensive validation
\n
• Process 500M+ records over 6-month migration period
\n

Data pipeline component (real-time operational data during migration):

\n
• Stream current business transactions to both ECC and S/4HANA systems
\n
• Ensure business continuity during migration phases
\n
• Real-time synchronization of master data changes
\n
• Handle 50,000+ daily transactions with zero business disruption
\n

Business Impact:

\n
• Migration completed 40% faster than with traditional approaches
\n
• Zero business downtime during migration
\n
• 99.8% data accuracy achieved in target S/4HANA system
\n

Conclusion

Data pipelines and ETL are similar yet different. While data pipelines encompass broader and less specific types of data movements, ETL is focused on accumulating data from multiple sources, cleansing and transforming it according to the format of a target system, and successfully loading the data into the destination database.

We hope this guide helps you better understand the difference between ETL and data pipelines and determine when to use each (or both) and how to automate both processes for real-time data analysis, streamlined decision-making, and quick reactions to whatever market or operational changes occur.

Read about key differences between a data pipeline and ETL (Extract, Transform, Load), the purpose for each, and the main use cases of both concepts.

Read about key differences between a data pipeline and ETL (Extract, Transform, Load), the purpose for each, and the main use cases of both concepts.

Data Pipeline vs ETL: Understanding the Key Differences and Use Cases

Data pipelines and ETL (Extract, Transform, Load) are two similar concepts that are related to data movement and processing. However, ETL is a narrower thing and is actually a specific type of data pipeline. Data pipeline, in turn, is a broad concept that includes different types of data movement and processing activities, including ETL, real-time streaming, etc. ETL, as may be seen from the name, focuses solely on extracting, transforming, and loading data to make it usable for efficient data storage and analytics.

In this post, we’ll dig deeper into the difference between data pipelines and ETL, and we will illustrate some of their use cases.

What Is a Data Pipeline?

A data pipeline is a sequence of data processing steps used to safely transfer data from one system to another. Data pipelines facilitate smooth data movement from different sources to destinations like data warehouses, databases, or data lakes.

Simply put, a data pipeline is a roadmap that helps your data safely get from point A to point B in a smooth, uninterrupted way.

Data pipelines automate data handling and transformation, ensuring consistency, reliability, and timeliness. This automation supports real-time analytics, prompt decision-making, and effective data management. Without well-structured data pipelines, businesses may face challenges related to data management and integrity, which can lead to operational bottlenecks and analytical errors.

What Is an ETL Pipeline?

An ETL (Extract, Transform, Load) pipeline is a special type of data pipeline made up of three crucial steps: extracting data from various sources, transforming it into an appropriate format, and loading it into a destination system. ETL is extremely important for effective data analytics, comprehensive reporting, and strategic business intelligence.

In our example, ETL is not a point A to point B highway, but a lifecycle of consumable goods. Raw materials from different sources are taken to a factory where they are transformed into understandable products, which are then moved to a store to be consumed by buyers.

ETL is still a type of data pipeline, but its purpose is to transform the initial raw data. General data pipelines, by contrast, may include simpler processes, such as direct data transfers, without any transformation.

The three stages of an ETL pipeline look like this: The three stages of an ETL pipeline look like this:

\n
• Extract: Raw data is collected from multiple sources, including databases, applications, or flat files. The objective of this stage is to simply collect the data.
\n
• Transform: The extracted data undergoes various operations to ensure data cleanliness, accuracy, and compatibility with the destination system. Typical processes at this stage ensure that data fits the requirements of a target system and include data filtering, enrichment, aggregation, computational logic, and type conversions.
\n
• Load: The transformed data is loaded into the destination location, whether it is a data warehouse, database, or data lake. This phase can be done either incrementally in batches or continuously in real-time, depending on what type suits your business needs and operations.
\n

Key Differences Between Data Pipeline and ETL

Data pipelines and ETL are obviously different, despite sharing some conceptual similarities. Let’s take a closer look at the main differences between these two:

Purpose

ETL pipelines specialize in extracting, transforming, and loading data into target systems like data warehouses or cloud platforms, explicitly preparing data for analytics. In contrast, data pipelines transfer data directly from one system to another, often without significant transformations, facilitating smooth integration across various sources and destinations.

Data transformation

Data transformation is a core part of ETL pipelines, involving extensive data cleaning, enriching, and reformatting to ensure high-quality and meaningful results. Data pipelines may bypass these transformations entirely, simply transferring data in its original form, focusing more on seamless data movement. ETL pipelines integrate data; data pipelines generally deliver it.

Process complexity

ETL pipelines are inherently more intricate, driven by the depth of their transformation processes, which is optimal for data warehousing, business intelligence, and complex analytical tasks. On the other hand, data pipelines are typically less complex, which makes them ideal for simpler, real-time data streaming or straightforward integration scenarios that don’t require heavy data preparation.

Processing methods

ETL pipelines commonly rely on batch processing for scheduled handling of large datasets, though real-time processing is also possible. This structured approach suits periodic, substantial data updates. Data pipelines, however, comfortably accommodate both batch and real-time processing, effectively supporting applications that demand continuous and immediate data flow.

Scalability

Due to their intensive data transformation requirements, ETL pipelines tend to be less flexible and demand more resources, potentially complicating scalability but prioritizing quality. In contrast, data pipelines are more flexible, scaling easily and efficiently to manage dynamic data volumes and diverse data types.

Use cases

ETL pipelines are ideal for integrating, preparing, and centralizing data from various sources, such as disparate locations of legacy enterprise systems, into a consolidated data system (say, SAP Cloud ERP) for analytical purposes. Meanwhile, data pipelines swiftly move data across systems, such as streaming activity logs to real-time analytics platforms for immediate insights.

Data quality

Ensuring data quality and governance is fundamental to ETL pipelines, incorporating thorough data validation, cleansing, and consistency checks during transformation. Data pipelines, in turn, may prioritize speed over rigorous data quality checks, primarily ensuring rapid and efficient data transfers without extensive validation.

ETL vs Data Pipelines: Use Cases

Let’s examine how data pipelines and ETL are applied in companies to streamline processes, increase agility, allow for competitive analytics, and make relevant decisions.

Use cases of data pipelines

Real-time analytics

Data pipelines stream data from sources like websites, applications, or user interactions into analytics platforms. This allows instant updates of analytics dashboards that enable businesses to monitor performance, user behavior, and system status continuously and in real-time.

For example, this may be seen in tracking real-time user interactions on E-commerce websites to adjust recommendations dynamically.

IoT and sensor data processing

IoT devices generate vast amounts of continuous, real-time data. Data pipelines efficiently capture, move, and process this sensor data to facilitate immediate alerts, predictive maintenance, or timely operational insights.

A good example is real-time monitoring of industrial equipment to prevent downtime through proactive maintenance.

Machine learning model training

Machine learning (ML) requires consistent and continuous data streams. Data pipelines automate data ingestion into ML environments, enabling frequent training, re-training, and deployment of accurate predictive models.

Automatic ingesting of transactional and user data into ML platforms to continuously improve recommendation models is a good illustration of this use case.

Multi-cloud or SaaS integration

Data pipelines simplify integration across multiple cloud platforms or SaaS applications, efficiently synchronizing data and ensuring real-time interoperability.

For example, data pipelines ensure seamless real-time data synchronization between ERP systems and CRM platforms (e.g., SAP Cloud ERP and Salesforce integration).

ETL Pipeline Use Cases

Enterprise data warehousing and reporting

ETL pipelines consolidate data from disparate enterprise sources into centralized data warehouses, ensuring comprehensive, high-quality datasets suitable for business intelligence, detailed reporting, and long-term analytical queries.

This use case can be illustrated by combining sales, HR, and inventory data into a central data warehouse for detailed cross-departmental analytics.

Regulatory compliance and auditing

ETL pipelines ensure regulatory compliance by systematically extracting, validating, transforming, and securely storing data necessary for audit trails and regulatory reporting.

Preparing financial transaction data for quarterly audits and regulatory reporting in financial institutions is handled with ETL.

SAP data consolidation

ETL pipelines handle ERP data from various SAP and non-SAP systems, consolidating complex financial, supply chain, and operational datasets for easier, more consistent analysis and reporting.

For example, ETL helps integrate SAP data from regional offices to provide global consolidated financial statements and supply chain analytics.

Combining ETL and data pipelines

In some cases, businesses can use both ETL and data pipelines in collaboration. This approach allows each pipeline to perform its specific tasks, benefitting companies with both ETL pipelines and data pipelines.

Structured ERP/financial data (ETL)

ETL pipelines perform rigorous transformations and quality checks for structured, sensitive, and transactional ERP or financial data when moving from various sources to SAP systems, ensuring accurate, reliable insights.

Use case: Processing and integrating monthly financial data from disparate sources into SAP Cloud ERP systems to support complex reporting and budgeting analyses.

Customer behavior, IoT, and log data (data pipelines)

Real-time data pipelines stream data directly from customer interactions, sensors, or application logs, ensuring timely insights and responsiveness to changing market trends or user demands.

Use case: Capturing real-time customer interactions on mobile apps or website logs, providing instant feedback to marketing teams, and improving user satisfaction dynamically by avoiding stockouts.

How DataLark Streamlines ETL and Data Pipelines

Both ETL and data pipelines need supervision to fix operational issues in a timely manner, as well as automation to streamline data movements and increase data processing speed for timely decision-making and prompt market response.

DataLark, a versatile data pipeline automation platform, will be a good choice when it comes to pipeline automation. The solution offers a robust and unified approach to simplifying ETL and data pipeline management with the help of its no-code, intuitive drag-and-drop interface. This allows users to create, orchestrate, and manage intricate data workflows without extensive technical expertise, decreasing the IT burden. Additionally, the visualized data mapping significantly reduces implementation time, enabling businesses to quickly automate their data flows.

DataLark can be deployed on-premise, in the cloud, or in hybrid environments, which makes the solution suitable for a broad range of businesses.

DataLark’s comprehensive integration capabilities support a vast range of connectors, notably deep SAP integration (SAP ECC, S/4HANA, and others), allowing seamless bidirectional data synchronization across SAP and non-SAP systems. This is especially beneficial in ETL scenarios where structured data from various systems and legacy applications must be consolidated reliably and securely into the ERP system for further analytics and processing.

DataLark supports trigger-based and schedule-based automation, so businesses can choose the option that suits them better and set up automation easily. Additionally, comprehensive data monitoring and automated alerts provide transparency of the data pipeline and ETL processes, allowing for continuous data flow monitoring and timely issue resolution.

DataLark’s Hybrid Approach in Action: Combining ETL and Data Pipelines

Project: SAP S/4HANA Migration with Ongoing Operations

Challenge: A large enterprise migrating to S/4HANA while maintaining business operations requires both batch data migration and real-time operational data flow.

ETL component (historical data migration with complex transformations):

\n
• Extract 10 years of transactional data from SAP ECC
\n
• Transform to S/4HANA data model (Universal Journal, new table structures)
\n
• Load in controlled batches with extensive validation
\n
• Process 500M+ records over 6-month migration period
\n

Data pipeline component (real-time operational data during migration):

\n
• Stream current business transactions to both ECC and S/4HANA systems
\n
• Ensure business continuity during migration phases
\n
• Real-time synchronization of master data changes
\n
• Handle 50,000+ daily transactions with zero business disruption
\n

Business Impact:

\n
• Migration completed 40% faster than with traditional approaches
\n
• Zero business downtime during migration
\n
• 99.8% data accuracy achieved in target S/4HANA system
\n

Conclusion

Data pipelines and ETL are similar yet different. While data pipelines encompass broader and less specific types of data movements, ETL is focused on accumulating data from multiple sources, cleansing and transforming it according to the format of a target system, and successfully loading the data into the destination database.

We hope this guide helps you better understand the difference between ETL and data pipelines and determine when to use each (or both) and how to automate both processes for real-time data analysis, streamlined decision-making, and quick reactions to whatever market or operational changes occur.

Read about key differences between a data pipeline and ETL (Extract, Transform, Load), the purpose for each, and the main use cases of both concepts.

Read about key differences between a data pipeline and ETL (Extract, Transform, Load), the purpose for each, and the main use cases of both concepts.

Data Pipeline vs ETL: Understanding the Key Differences and Use Cases

Data pipelines and ETL (Extract, Transform, Load) are two similar concepts that are related to data movement and processing. However, ETL is a narrower thing and is actually a specific type of data pipeline. Data pipeline, in turn, is a broad concept that includes different types of data movement and processing activities, including ETL, real-time streaming, etc. ETL, as may be seen from the name, focuses solely on extracting, transforming, and loading data to make it usable for efficient data storage and analytics.

In this post, we’ll dig deeper into the difference between data pipelines and ETL, and we will illustrate some of their use cases.

What Is a Data Pipeline?

A data pipeline is a sequence of data processing steps used to safely transfer data from one system to another. Data pipelines facilitate smooth data movement from different sources to destinations like data warehouses, databases, or data lakes.

Simply put, a data pipeline is a roadmap that helps your data safely get from point A to point B in a smooth, uninterrupted way.

Data pipelines automate data handling and transformation, ensuring consistency, reliability, and timeliness. This automation supports real-time analytics, prompt decision-making, and effective data management. Without well-structured data pipelines, businesses may face challenges related to data management and integrity, which can lead to operational bottlenecks and analytical errors.

What Is an ETL Pipeline?

An ETL (Extract, Transform, Load) pipeline is a special type of data pipeline made up of three crucial steps: extracting data from various sources, transforming it into an appropriate format, and loading it into a destination system. ETL is extremely important for effective data analytics, comprehensive reporting, and strategic business intelligence.

In our example, ETL is not a point A to point B highway, but a lifecycle of consumable goods. Raw materials from different sources are taken to a factory where they are transformed into understandable products, which are then moved to a store to be consumed by buyers.

ETL is still a type of data pipeline, but its purpose is to transform the initial raw data. General data pipelines, by contrast, may include simpler processes, such as direct data transfers, without any transformation.

The three stages of an ETL pipeline look like this: The three stages of an ETL pipeline look like this:

\n
• Extract: Raw data is collected from multiple sources, including databases, applications, or flat files. The objective of this stage is to simply collect the data.
\n
• Transform: The extracted data undergoes various operations to ensure data cleanliness, accuracy, and compatibility with the destination system. Typical processes at this stage ensure that data fits the requirements of a target system and include data filtering, enrichment, aggregation, computational logic, and type conversions.
\n
• Load: The transformed data is loaded into the destination location, whether it is a data warehouse, database, or data lake. This phase can be done either incrementally in batches or continuously in real-time, depending on what type suits your business needs and operations.
\n

Key Differences Between Data Pipeline and ETL

Data pipelines and ETL are obviously different, despite sharing some conceptual similarities. Let’s take a closer look at the main differences between these two:

Purpose

ETL pipelines specialize in extracting, transforming, and loading data into target systems like data warehouses or cloud platforms, explicitly preparing data for analytics. In contrast, data pipelines transfer data directly from one system to another, often without significant transformations, facilitating smooth integration across various sources and destinations.

Data transformation

Data transformation is a core part of ETL pipelines, involving extensive data cleaning, enriching, and reformatting to ensure high-quality and meaningful results. Data pipelines may bypass these transformations entirely, simply transferring data in its original form, focusing more on seamless data movement. ETL pipelines integrate data; data pipelines generally deliver it.

Process complexity

ETL pipelines are inherently more intricate, driven by the depth of their transformation processes, which is optimal for data warehousing, business intelligence, and complex analytical tasks. On the other hand, data pipelines are typically less complex, which makes them ideal for simpler, real-time data streaming or straightforward integration scenarios that don’t require heavy data preparation.

Processing methods

ETL pipelines commonly rely on batch processing for scheduled handling of large datasets, though real-time processing is also possible. This structured approach suits periodic, substantial data updates. Data pipelines, however, comfortably accommodate both batch and real-time processing, effectively supporting applications that demand continuous and immediate data flow.

Scalability

Due to their intensive data transformation requirements, ETL pipelines tend to be less flexible and demand more resources, potentially complicating scalability but prioritizing quality. In contrast, data pipelines are more flexible, scaling easily and efficiently to manage dynamic data volumes and diverse data types.

Use cases

ETL pipelines are ideal for integrating, preparing, and centralizing data from various sources, such as disparate locations of legacy enterprise systems, into a consolidated data system (say, SAP Cloud ERP) for analytical purposes. Meanwhile, data pipelines swiftly move data across systems, such as streaming activity logs to real-time analytics platforms for immediate insights.

Data quality

Ensuring data quality and governance is fundamental to ETL pipelines, incorporating thorough data validation, cleansing, and consistency checks during transformation. Data pipelines, in turn, may prioritize speed over rigorous data quality checks, primarily ensuring rapid and efficient data transfers without extensive validation.

ETL vs Data Pipelines: Use Cases

Let’s examine how data pipelines and ETL are applied in companies to streamline processes, increase agility, allow for competitive analytics, and make relevant decisions.

Use cases of data pipelines

Real-time analytics

Data pipelines stream data from sources like websites, applications, or user interactions into analytics platforms. This allows instant updates of analytics dashboards that enable businesses to monitor performance, user behavior, and system status continuously and in real-time.

For example, this may be seen in tracking real-time user interactions on E-commerce websites to adjust recommendations dynamically.

IoT and sensor data processing

IoT devices generate vast amounts of continuous, real-time data. Data pipelines efficiently capture, move, and process this sensor data to facilitate immediate alerts, predictive maintenance, or timely operational insights.

A good example is real-time monitoring of industrial equipment to prevent downtime through proactive maintenance.

Machine learning model training

Machine learning (ML) requires consistent and continuous data streams. Data pipelines automate data ingestion into ML environments, enabling frequent training, re-training, and deployment of accurate predictive models.

Automatic ingesting of transactional and user data into ML platforms to continuously improve recommendation models is a good illustration of this use case.

Multi-cloud or SaaS integration

Data pipelines simplify integration across multiple cloud platforms or SaaS applications, efficiently synchronizing data and ensuring real-time interoperability.

For example, data pipelines ensure seamless real-time data synchronization between ERP systems and CRM platforms (e.g., SAP Cloud ERP and Salesforce integration).

ETL Pipeline Use Cases

Enterprise data warehousing and reporting

ETL pipelines consolidate data from disparate enterprise sources into centralized data warehouses, ensuring comprehensive, high-quality datasets suitable for business intelligence, detailed reporting, and long-term analytical queries.

This use case can be illustrated by combining sales, HR, and inventory data into a central data warehouse for detailed cross-departmental analytics.

Regulatory compliance and auditing

ETL pipelines ensure regulatory compliance by systematically extracting, validating, transforming, and securely storing data necessary for audit trails and regulatory reporting.

Preparing financial transaction data for quarterly audits and regulatory reporting in financial institutions is handled with ETL.

SAP data consolidation

ETL pipelines handle ERP data from various SAP and non-SAP systems, consolidating complex financial, supply chain, and operational datasets for easier, more consistent analysis and reporting.

For example, ETL helps integrate SAP data from regional offices to provide global consolidated financial statements and supply chain analytics.

Combining ETL and data pipelines

In some cases, businesses can use both ETL and data pipelines in collaboration. This approach allows each pipeline to perform its specific tasks, benefitting companies with both ETL pipelines and data pipelines.

Structured ERP/financial data (ETL)

ETL pipelines perform rigorous transformations and quality checks for structured, sensitive, and transactional ERP or financial data when moving from various sources to SAP systems, ensuring accurate, reliable insights.

Use case: Processing and integrating monthly financial data from disparate sources into SAP Cloud ERP systems to support complex reporting and budgeting analyses.

Customer behavior, IoT, and log data (data pipelines)

Real-time data pipelines stream data directly from customer interactions, sensors, or application logs, ensuring timely insights and responsiveness to changing market trends or user demands.

Use case: Capturing real-time customer interactions on mobile apps or website logs, providing instant feedback to marketing teams, and improving user satisfaction dynamically by avoiding stockouts.

How DataLark Streamlines ETL and Data Pipelines

Both ETL and data pipelines need supervision to fix operational issues in a timely manner, as well as automation to streamline data movements and increase data processing speed for timely decision-making and prompt market response.

DataLark, a versatile data pipeline automation platform, will be a good choice when it comes to pipeline automation. The solution offers a robust and unified approach to simplifying ETL and data pipeline management with the help of its no-code, intuitive drag-and-drop interface. This allows users to create, orchestrate, and manage intricate data workflows without extensive technical expertise, decreasing the IT burden. Additionally, the visualized data mapping significantly reduces implementation time, enabling businesses to quickly automate their data flows.

DataLark can be deployed on-premise, in the cloud, or in hybrid environments, which makes the solution suitable for a broad range of businesses.

DataLark’s comprehensive integration capabilities support a vast range of connectors, notably deep SAP integration (SAP ECC, S/4HANA, and others), allowing seamless bidirectional data synchronization across SAP and non-SAP systems. This is especially beneficial in ETL scenarios where structured data from various systems and legacy applications must be consolidated reliably and securely into the ERP system for further analytics and processing.

DataLark supports trigger-based and schedule-based automation, so businesses can choose the option that suits them better and set up automation easily. Additionally, comprehensive data monitoring and automated alerts provide transparency of the data pipeline and ETL processes, allowing for continuous data flow monitoring and timely issue resolution.

DataLark’s Hybrid Approach in Action: Combining ETL and Data Pipelines

Project: SAP S/4HANA Migration with Ongoing Operations

Challenge: A large enterprise migrating to S/4HANA while maintaining business operations requires both batch data migration and real-time operational data flow.

ETL component (historical data migration with complex transformations):

\n
• Extract 10 years of transactional data from SAP ECC
\n
• Transform to S/4HANA data model (Universal Journal, new table structures)
\n
• Load in controlled batches with extensive validation
\n
• Process 500M+ records over 6-month migration period
\n

Data pipeline component (real-time operational data during migration):

\n
• Stream current business transactions to both ECC and S/4HANA systems
\n
• Ensure business continuity during migration phases
\n
• Real-time synchronization of master data changes
\n
• Handle 50,000+ daily transactions with zero business disruption
\n

Business Impact:

\n
• Migration completed 40% faster than with traditional approaches
\n
• Zero business downtime during migration
\n
• 99.8% data accuracy achieved in target S/4HANA system
\n

Conclusion

Data pipelines and ETL are similar yet different. While data pipelines encompass broader and less specific types of data movements, ETL is focused on accumulating data from multiple sources, cleansing and transforming it according to the format of a target system, and successfully loading the data into the destination database.

We hope this guide helps you better understand the difference between ETL and data pipelines and determine when to use each (or both) and how to automate both processes for real-time data analysis, streamlined decision-making, and quick reactions to whatever market or operational changes occur.

Read about key differences between a data pipeline and ETL (Extract, Transform, Load), the purpose for each, and the main use cases of both concepts.

Data Pipeline vs ETL: Understanding the Key Differences and Use Cases

Data pipelines and ETL (Extract, Transform, Load) are two similar concepts that are related to data movement and processing. However, ETL is a narrower thing and is actually a specific type of data pipeline. Data pipeline, in turn, is a broad concept that includes different types of data movement and processing activities, including ETL, real-time streaming, etc. ETL, as may be seen from the name, focuses solely on extracting, transforming, and loading data to make it usable for efficient data storage and analytics.

In this post, we’ll dig deeper into the difference between data pipelines and ETL, and we will illustrate some of their use cases.

What Is a Data Pipeline?

A data pipeline is a sequence of data processing steps used to safely transfer data from one system to another. Data pipelines facilitate smooth data movement from different sources to destinations like data warehouses, databases, or data lakes.

Simply put, a data pipeline is a roadmap that helps your data safely get from point A to point B in a smooth, uninterrupted way.

Data pipelines automate data handling and transformation, ensuring consistency, reliability, and timeliness. This automation supports real-time analytics, prompt decision-making, and effective data management. Without well-structured data pipelines, businesses may face challenges related to data management and integrity, which can lead to operational bottlenecks and analytical errors.

What Is an ETL Pipeline?

An ETL (Extract, Transform, Load) pipeline is a special type of data pipeline made up of three crucial steps: extracting data from various sources, transforming it into an appropriate format, and loading it into a destination system. ETL is extremely important for effective data analytics, comprehensive reporting, and strategic business intelligence.

In our example, ETL is not a point A to point B highway, but a lifecycle of consumable goods. Raw materials from different sources are taken to a factory where they are transformed into understandable products, which are then moved to a store to be consumed by buyers.

ETL is still a type of data pipeline, but its purpose is to transform the initial raw data. General data pipelines, by contrast, may include simpler processes, such as direct data transfers, without any transformation.

The three stages of an ETL pipeline look like this: The three stages of an ETL pipeline look like this:

\n
• Extract: Raw data is collected from multiple sources, including databases, applications, or flat files. The objective of this stage is to simply collect the data.
\n
• Transform: The extracted data undergoes various operations to ensure data cleanliness, accuracy, and compatibility with the destination system. Typical processes at this stage ensure that data fits the requirements of a target system and include data filtering, enrichment, aggregation, computational logic, and type conversions.
\n
• Load: The transformed data is loaded into the destination location, whether it is a data warehouse, database, or data lake. This phase can be done either incrementally in batches or continuously in real-time, depending on what type suits your business needs and operations.
\n

Key Differences Between Data Pipeline and ETL

Data pipelines and ETL are obviously different, despite sharing some conceptual similarities. Let’s take a closer look at the main differences between these two:

Purpose

ETL pipelines specialize in extracting, transforming, and loading data into target systems like data warehouses or cloud platforms, explicitly preparing data for analytics. In contrast, data pipelines transfer data directly from one system to another, often without significant transformations, facilitating smooth integration across various sources and destinations.

Data transformation

Data transformation is a core part of ETL pipelines, involving extensive data cleaning, enriching, and reformatting to ensure high-quality and meaningful results. Data pipelines may bypass these transformations entirely, simply transferring data in its original form, focusing more on seamless data movement. ETL pipelines integrate data; data pipelines generally deliver it.

Process complexity

ETL pipelines are inherently more intricate, driven by the depth of their transformation processes, which is optimal for data warehousing, business intelligence, and complex analytical tasks. On the other hand, data pipelines are typically less complex, which makes them ideal for simpler, real-time data streaming or straightforward integration scenarios that don’t require heavy data preparation.

Processing methods

ETL pipelines commonly rely on batch processing for scheduled handling of large datasets, though real-time processing is also possible. This structured approach suits periodic, substantial data updates. Data pipelines, however, comfortably accommodate both batch and real-time processing, effectively supporting applications that demand continuous and immediate data flow.

Scalability

Due to their intensive data transformation requirements, ETL pipelines tend to be less flexible and demand more resources, potentially complicating scalability but prioritizing quality. In contrast, data pipelines are more flexible, scaling easily and efficiently to manage dynamic data volumes and diverse data types.

Use cases

ETL pipelines are ideal for integrating, preparing, and centralizing data from various sources, such as disparate locations of legacy enterprise systems, into a consolidated data system (say, SAP Cloud ERP) for analytical purposes. Meanwhile, data pipelines swiftly move data across systems, such as streaming activity logs to real-time analytics platforms for immediate insights.

Data quality

Ensuring data quality and governance is fundamental to ETL pipelines, incorporating thorough data validation, cleansing, and consistency checks during transformation. Data pipelines, in turn, may prioritize speed over rigorous data quality checks, primarily ensuring rapid and efficient data transfers without extensive validation.

ETL vs Data Pipelines: Use Cases

Let’s examine how data pipelines and ETL are applied in companies to streamline processes, increase agility, allow for competitive analytics, and make relevant decisions.

Use cases of data pipelines

Real-time analytics

Data pipelines stream data from sources like websites, applications, or user interactions into analytics platforms. This allows instant updates of analytics dashboards that enable businesses to monitor performance, user behavior, and system status continuously and in real-time.

For example, this may be seen in tracking real-time user interactions on E-commerce websites to adjust recommendations dynamically.

IoT and sensor data processing

IoT devices generate vast amounts of continuous, real-time data. Data pipelines efficiently capture, move, and process this sensor data to facilitate immediate alerts, predictive maintenance, or timely operational insights.

A good example is real-time monitoring of industrial equipment to prevent downtime through proactive maintenance.

Machine learning model training

Machine learning (ML) requires consistent and continuous data streams. Data pipelines automate data ingestion into ML environments, enabling frequent training, re-training, and deployment of accurate predictive models.

Automatic ingesting of transactional and user data into ML platforms to continuously improve recommendation models is a good illustration of this use case.

Multi-cloud or SaaS integration

Data pipelines simplify integration across multiple cloud platforms or SaaS applications, efficiently synchronizing data and ensuring real-time interoperability.

For example, data pipelines ensure seamless real-time data synchronization between ERP systems and CRM platforms (e.g., SAP Cloud ERP and Salesforce integration).

ETL Pipeline Use Cases

Enterprise data warehousing and reporting

ETL pipelines consolidate data from disparate enterprise sources into centralized data warehouses, ensuring comprehensive, high-quality datasets suitable for business intelligence, detailed reporting, and long-term analytical queries.

This use case can be illustrated by combining sales, HR, and inventory data into a central data warehouse for detailed cross-departmental analytics.

Regulatory compliance and auditing

ETL pipelines ensure regulatory compliance by systematically extracting, validating, transforming, and securely storing data necessary for audit trails and regulatory reporting.

Preparing financial transaction data for quarterly audits and regulatory reporting in financial institutions is handled with ETL.

SAP data consolidation

ETL pipelines handle ERP data from various SAP and non-SAP systems, consolidating complex financial, supply chain, and operational datasets for easier, more consistent analysis and reporting.

For example, ETL helps integrate SAP data from regional offices to provide global consolidated financial statements and supply chain analytics.

Combining ETL and data pipelines

In some cases, businesses can use both ETL and data pipelines in collaboration. This approach allows each pipeline to perform its specific tasks, benefitting companies with both ETL pipelines and data pipelines.

Structured ERP/financial data (ETL)

ETL pipelines perform rigorous transformations and quality checks for structured, sensitive, and transactional ERP or financial data when moving from various sources to SAP systems, ensuring accurate, reliable insights.

Use case: Processing and integrating monthly financial data from disparate sources into SAP Cloud ERP systems to support complex reporting and budgeting analyses.

Customer behavior, IoT, and log data (data pipelines)

Real-time data pipelines stream data directly from customer interactions, sensors, or application logs, ensuring timely insights and responsiveness to changing market trends or user demands.

Use case: Capturing real-time customer interactions on mobile apps or website logs, providing instant feedback to marketing teams, and improving user satisfaction dynamically by avoiding stockouts.

How DataLark Streamlines ETL and Data Pipelines

Both ETL and data pipelines need supervision to fix operational issues in a timely manner, as well as automation to streamline data movements and increase data processing speed for timely decision-making and prompt market response.

DataLark, a versatile data pipeline automation platform, will be a good choice when it comes to pipeline automation. The solution offers a robust and unified approach to simplifying ETL and data pipeline management with the help of its no-code, intuitive drag-and-drop interface. This allows users to create, orchestrate, and manage intricate data workflows without extensive technical expertise, decreasing the IT burden. Additionally, the visualized data mapping significantly reduces implementation time, enabling businesses to quickly automate their data flows.

DataLark can be deployed on-premise, in the cloud, or in hybrid environments, which makes the solution suitable for a broad range of businesses.

DataLark’s comprehensive integration capabilities support a vast range of connectors, notably deep SAP integration (SAP ECC, S/4HANA, and others), allowing seamless bidirectional data synchronization across SAP and non-SAP systems. This is especially beneficial in ETL scenarios where structured data from various systems and legacy applications must be consolidated reliably and securely into the ERP system for further analytics and processing.

DataLark supports trigger-based and schedule-based automation, so businesses can choose the option that suits them better and set up automation easily. Additionally, comprehensive data monitoring and automated alerts provide transparency of the data pipeline and ETL processes, allowing for continuous data flow monitoring and timely issue resolution.

DataLark’s Hybrid Approach in Action: Combining ETL and Data Pipelines

Project: SAP S/4HANA Migration with Ongoing Operations

Challenge: A large enterprise migrating to S/4HANA while maintaining business operations requires both batch data migration and real-time operational data flow.

ETL component (historical data migration with complex transformations):

\n
• Extract 10 years of transactional data from SAP ECC
\n
• Transform to S/4HANA data model (Universal Journal, new table structures)
\n
• Load in controlled batches with extensive validation
\n
• Process 500M+ records over 6-month migration period
\n

Data pipeline component (real-time operational data during migration):

\n
• Stream current business transactions to both ECC and S/4HANA systems
\n
• Ensure business continuity during migration phases
\n
• Real-time synchronization of master data changes
\n
• Handle 50,000+ daily transactions with zero business disruption
\n

Business Impact:

\n
• Migration completed 40% faster than with traditional approaches
\n
• Zero business downtime during migration
\n
• 99.8% data accuracy achieved in target S/4HANA system
\n

Conclusion

Data pipelines and ETL are similar yet different. While data pipelines encompass broader and less specific types of data movements, ETL is focused on accumulating data from multiple sources, cleansing and transforming it according to the format of a target system, and successfully loading the data into the destination database.

We hope this guide helps you better understand the difference between ETL and data pipelines and determine when to use each (or both) and how to automate both processes for real-time data analysis, streamlined decision-making, and quick reactions to whatever market or operational changes occur.

Read about key differences between a data pipeline and ETL (Extract, Transform, Load), the purpose for each, and the main use cases of both concepts.

Read about key differences between a data pipeline and ETL (Extract, Transform, Load), the purpose for each, and the main use cases of both concepts.

Read about key differences between a data pipeline and ETL (Extract, Transform, Load), the purpose for each, and the main use cases of both concepts.

Data Pipeline vs ETL: Understanding the Key Differences and Use Cases

Data pipelines and ETL (Extract, Transform, Load) are two similar concepts that are related to data movement and processing. However, ETL is a narrower thing and is actually a specific type of data pipeline. Data pipeline, in turn, is a broad concept that includes different types of data movement and processing activities, including ETL, real-time streaming, etc. ETL, as may be seen from the name, focuses solely on extracting, transforming, and loading data to make it usable for efficient data storage and analytics.

In this post, we’ll dig deeper into the difference between data pipelines and ETL, and we will illustrate some of their use cases.

What Is a Data Pipeline?

A data pipeline is a sequence of data processing steps used to safely transfer data from one system to another. Data pipelines facilitate smooth data movement from different sources to destinations like data warehouses, databases, or data lakes.

Simply put, a data pipeline is a roadmap that helps your data safely get from point A to point B in a smooth, uninterrupted way.

Data pipelines automate data handling and transformation, ensuring consistency, reliability, and timeliness. This automation supports real-time analytics, prompt decision-making, and effective data management. Without well-structured data pipelines, businesses may face challenges related to data management and integrity, which can lead to operational bottlenecks and analytical errors.

What Is an ETL Pipeline?

An ETL (Extract, Transform, Load) pipeline is a special type of data pipeline made up of three crucial steps: extracting data from various sources, transforming it into an appropriate format, and loading it into a destination system. ETL is extremely important for effective data analytics, comprehensive reporting, and strategic business intelligence.

In our example, ETL is not a point A to point B highway, but a lifecycle of consumable goods. Raw materials from different sources are taken to a factory where they are transformed into understandable products, which are then moved to a store to be consumed by buyers.

ETL is still a type of data pipeline, but its purpose is to transform the initial raw data. General data pipelines, by contrast, may include simpler processes, such as direct data transfers, without any transformation.

The three stages of an ETL pipeline look like this: The three stages of an ETL pipeline look like this:

\n
• Extract: Raw data is collected from multiple sources, including databases, applications, or flat files. The objective of this stage is to simply collect the data.
\n
• Transform: The extracted data undergoes various operations to ensure data cleanliness, accuracy, and compatibility with the destination system. Typical processes at this stage ensure that data fits the requirements of a target system and include data filtering, enrichment, aggregation, computational logic, and type conversions.
\n
• Load: The transformed data is loaded into the destination location, whether it is a data warehouse, database, or data lake. This phase can be done either incrementally in batches or continuously in real-time, depending on what type suits your business needs and operations.
\n

Key Differences Between Data Pipeline and ETL

Data pipelines and ETL are obviously different, despite sharing some conceptual similarities. Let’s take a closer look at the main differences between these two:

Purpose

ETL pipelines specialize in extracting, transforming, and loading data into target systems like data warehouses or cloud platforms, explicitly preparing data for analytics. In contrast, data pipelines transfer data directly from one system to another, often without significant transformations, facilitating smooth integration across various sources and destinations.

Data transformation

Data transformation is a core part of ETL pipelines, involving extensive data cleaning, enriching, and reformatting to ensure high-quality and meaningful results. Data pipelines may bypass these transformations entirely, simply transferring data in its original form, focusing more on seamless data movement. ETL pipelines integrate data; data pipelines generally deliver it.

Process complexity

ETL pipelines are inherently more intricate, driven by the depth of their transformation processes, which is optimal for data warehousing, business intelligence, and complex analytical tasks. On the other hand, data pipelines are typically less complex, which makes them ideal for simpler, real-time data streaming or straightforward integration scenarios that don’t require heavy data preparation.

Processing methods

ETL pipelines commonly rely on batch processing for scheduled handling of large datasets, though real-time processing is also possible. This structured approach suits periodic, substantial data updates. Data pipelines, however, comfortably accommodate both batch and real-time processing, effectively supporting applications that demand continuous and immediate data flow.

Scalability

Due to their intensive data transformation requirements, ETL pipelines tend to be less flexible and demand more resources, potentially complicating scalability but prioritizing quality. In contrast, data pipelines are more flexible, scaling easily and efficiently to manage dynamic data volumes and diverse data types.

Use cases

ETL pipelines are ideal for integrating, preparing, and centralizing data from various sources, such as disparate locations of legacy enterprise systems, into a consolidated data system (say, SAP Cloud ERP) for analytical purposes. Meanwhile, data pipelines swiftly move data across systems, such as streaming activity logs to real-time analytics platforms for immediate insights.

Data quality

Ensuring data quality and governance is fundamental to ETL pipelines, incorporating thorough data validation, cleansing, and consistency checks during transformation. Data pipelines, in turn, may prioritize speed over rigorous data quality checks, primarily ensuring rapid and efficient data transfers without extensive validation.

ETL vs Data Pipelines: Use Cases

Let’s examine how data pipelines and ETL are applied in companies to streamline processes, increase agility, allow for competitive analytics, and make relevant decisions.

Use cases of data pipelines

Real-time analytics

Data pipelines stream data from sources like websites, applications, or user interactions into analytics platforms. This allows instant updates of analytics dashboards that enable businesses to monitor performance, user behavior, and system status continuously and in real-time.

For example, this may be seen in tracking real-time user interactions on E-commerce websites to adjust recommendations dynamically.

IoT and sensor data processing

IoT devices generate vast amounts of continuous, real-time data. Data pipelines efficiently capture, move, and process this sensor data to facilitate immediate alerts, predictive maintenance, or timely operational insights.

A good example is real-time monitoring of industrial equipment to prevent downtime through proactive maintenance.

Machine learning model training

Machine learning (ML) requires consistent and continuous data streams. Data pipelines automate data ingestion into ML environments, enabling frequent training, re-training, and deployment of accurate predictive models.

Automatic ingesting of transactional and user data into ML platforms to continuously improve recommendation models is a good illustration of this use case.

Multi-cloud or SaaS integration

Data pipelines simplify integration across multiple cloud platforms or SaaS applications, efficiently synchronizing data and ensuring real-time interoperability.

For example, data pipelines ensure seamless real-time data synchronization between ERP systems and CRM platforms (e.g., SAP Cloud ERP and Salesforce integration).

ETL Pipeline Use Cases

Enterprise data warehousing and reporting

ETL pipelines consolidate data from disparate enterprise sources into centralized data warehouses, ensuring comprehensive, high-quality datasets suitable for business intelligence, detailed reporting, and long-term analytical queries.

This use case can be illustrated by combining sales, HR, and inventory data into a central data warehouse for detailed cross-departmental analytics.

Regulatory compliance and auditing

ETL pipelines ensure regulatory compliance by systematically extracting, validating, transforming, and securely storing data necessary for audit trails and regulatory reporting.

Preparing financial transaction data for quarterly audits and regulatory reporting in financial institutions is handled with ETL.

SAP data consolidation

ETL pipelines handle ERP data from various SAP and non-SAP systems, consolidating complex financial, supply chain, and operational datasets for easier, more consistent analysis and reporting.

For example, ETL helps integrate SAP data from regional offices to provide global consolidated financial statements and supply chain analytics.

Combining ETL and data pipelines

In some cases, businesses can use both ETL and data pipelines in collaboration. This approach allows each pipeline to perform its specific tasks, benefitting companies with both ETL pipelines and data pipelines.

Structured ERP/financial data (ETL)

ETL pipelines perform rigorous transformations and quality checks for structured, sensitive, and transactional ERP or financial data when moving from various sources to SAP systems, ensuring accurate, reliable insights.

Use case: Processing and integrating monthly financial data from disparate sources into SAP Cloud ERP systems to support complex reporting and budgeting analyses.

Customer behavior, IoT, and log data (data pipelines)

Real-time data pipelines stream data directly from customer interactions, sensors, or application logs, ensuring timely insights and responsiveness to changing market trends or user demands.

Use case: Capturing real-time customer interactions on mobile apps or website logs, providing instant feedback to marketing teams, and improving user satisfaction dynamically by avoiding stockouts.

How DataLark Streamlines ETL and Data Pipelines

Both ETL and data pipelines need supervision to fix operational issues in a timely manner, as well as automation to streamline data movements and increase data processing speed for timely decision-making and prompt market response.

DataLark, a versatile data pipeline automation platform, will be a good choice when it comes to pipeline automation. The solution offers a robust and unified approach to simplifying ETL and data pipeline management with the help of its no-code, intuitive drag-and-drop interface. This allows users to create, orchestrate, and manage intricate data workflows without extensive technical expertise, decreasing the IT burden. Additionally, the visualized data mapping significantly reduces implementation time, enabling businesses to quickly automate their data flows.

DataLark can be deployed on-premise, in the cloud, or in hybrid environments, which makes the solution suitable for a broad range of businesses.

DataLark’s comprehensive integration capabilities support a vast range of connectors, notably deep SAP integration (SAP ECC, S/4HANA, and others), allowing seamless bidirectional data synchronization across SAP and non-SAP systems. This is especially beneficial in ETL scenarios where structured data from various systems and legacy applications must be consolidated reliably and securely into the ERP system for further analytics and processing.

DataLark supports trigger-based and schedule-based automation, so businesses can choose the option that suits them better and set up automation easily. Additionally, comprehensive data monitoring and automated alerts provide transparency of the data pipeline and ETL processes, allowing for continuous data flow monitoring and timely issue resolution.

DataLark’s Hybrid Approach in Action: Combining ETL and Data Pipelines

Project: SAP S/4HANA Migration with Ongoing Operations

Challenge: A large enterprise migrating to S/4HANA while maintaining business operations requires both batch data migration and real-time operational data flow.

ETL component (historical data migration with complex transformations):

\n
• Extract 10 years of transactional data from SAP ECC
\n
• Transform to S/4HANA data model (Universal Journal, new table structures)
\n
• Load in controlled batches with extensive validation
\n
• Process 500M+ records over 6-month migration period
\n

Data pipeline component (real-time operational data during migration):

\n
• Stream current business transactions to both ECC and S/4HANA systems
\n
• Ensure business continuity during migration phases
\n
• Real-time synchronization of master data changes
\n
• Handle 50,000+ daily transactions with zero business disruption
\n

Business Impact:

\n
• Migration completed 40% faster than with traditional approaches
\n
• Zero business downtime during migration
\n
• 99.8% data accuracy achieved in target S/4HANA system
\n

Conclusion

Data pipelines and ETL are similar yet different. While data pipelines encompass broader and less specific types of data movements, ETL is focused on accumulating data from multiple sources, cleansing and transforming it according to the format of a target system, and successfully loading the data into the destination database.

We hope this guide helps you better understand the difference between ETL and data pipelines and determine when to use each (or both) and how to automate both processes for real-time data analysis, streamlined decision-making, and quick reactions to whatever market or operational changes occur.

Read about key differences between a data pipeline and ETL (Extract, Transform, Load), the purpose for each, and the main use cases of both concepts.

Read about key differences between a data pipeline and ETL (Extract, Transform, Load), the purpose for each, and the main use cases of both concepts.

Read about key differences between a data pipeline and ETL (Extract, Transform, Load), the purpose for each, and the main use cases of both concepts.

Data Pipeline vs ETL: Understanding the Key Differences and Use Cases

Data pipelines and ETL (Extract, Transform, Load) are two similar concepts that are related to data movement and processing. However, ETL is a narrower thing and is actually a specific type of data pipeline. Data pipeline, in turn, is a broad concept that includes different types of data movement and processing activities, including ETL, real-time streaming, etc. ETL, as may be seen from the name, focuses solely on extracting, transforming, and loading data to make it usable for efficient data storage and analytics.

In this post, we’ll dig deeper into the difference between data pipelines and ETL, and we will illustrate some of their use cases.

What Is a Data Pipeline?

A data pipeline is a sequence of data processing steps used to safely transfer data from one system to another. Data pipelines facilitate smooth data movement from different sources to destinations like data warehouses, databases, or data lakes.

Simply put, a data pipeline is a roadmap that helps your data safely get from point A to point B in a smooth, uninterrupted way.

Data pipelines automate data handling and transformation, ensuring consistency, reliability, and timeliness. This automation supports real-time analytics, prompt decision-making, and effective data management. Without well-structured data pipelines, businesses may face challenges related to data management and integrity, which can lead to operational bottlenecks and analytical errors.

What Is an ETL Pipeline?

An ETL (Extract, Transform, Load) pipeline is a special type of data pipeline made up of three crucial steps: extracting data from various sources, transforming it into an appropriate format, and loading it into a destination system. ETL is extremely important for effective data analytics, comprehensive reporting, and strategic business intelligence.

In our example, ETL is not a point A to point B highway, but a lifecycle of consumable goods. Raw materials from different sources are taken to a factory where they are transformed into understandable products, which are then moved to a store to be consumed by buyers.

ETL is still a type of data pipeline, but its purpose is to transform the initial raw data. General data pipelines, by contrast, may include simpler processes, such as direct data transfers, without any transformation.

The three stages of an ETL pipeline look like this: The three stages of an ETL pipeline look like this:

\n
• Extract: Raw data is collected from multiple sources, including databases, applications, or flat files. The objective of this stage is to simply collect the data.
\n
• Transform: The extracted data undergoes various operations to ensure data cleanliness, accuracy, and compatibility with the destination system. Typical processes at this stage ensure that data fits the requirements of a target system and include data filtering, enrichment, aggregation, computational logic, and type conversions.
\n
• Load: The transformed data is loaded into the destination location, whether it is a data warehouse, database, or data lake. This phase can be done either incrementally in batches or continuously in real-time, depending on what type suits your business needs and operations.
\n

Key Differences Between Data Pipeline and ETL

Data pipelines and ETL are obviously different, despite sharing some conceptual similarities. Let’s take a closer look at the main differences between these two:

Purpose

ETL pipelines specialize in extracting, transforming, and loading data into target systems like data warehouses or cloud platforms, explicitly preparing data for analytics. In contrast, data pipelines transfer data directly from one system to another, often without significant transformations, facilitating smooth integration across various sources and destinations.

Data transformation

Data transformation is a core part of ETL pipelines, involving extensive data cleaning, enriching, and reformatting to ensure high-quality and meaningful results. Data pipelines may bypass these transformations entirely, simply transferring data in its original form, focusing more on seamless data movement. ETL pipelines integrate data; data pipelines generally deliver it.

Process complexity

ETL pipelines are inherently more intricate, driven by the depth of their transformation processes, which is optimal for data warehousing, business intelligence, and complex analytical tasks. On the other hand, data pipelines are typically less complex, which makes them ideal for simpler, real-time data streaming or straightforward integration scenarios that don’t require heavy data preparation.

Processing methods

ETL pipelines commonly rely on batch processing for scheduled handling of large datasets, though real-time processing is also possible. This structured approach suits periodic, substantial data updates. Data pipelines, however, comfortably accommodate both batch and real-time processing, effectively supporting applications that demand continuous and immediate data flow.

Scalability

Due to their intensive data transformation requirements, ETL pipelines tend to be less flexible and demand more resources, potentially complicating scalability but prioritizing quality. In contrast, data pipelines are more flexible, scaling easily and efficiently to manage dynamic data volumes and diverse data types.

Use cases

ETL pipelines are ideal for integrating, preparing, and centralizing data from various sources, such as disparate locations of legacy enterprise systems, into a consolidated data system (say, SAP Cloud ERP) for analytical purposes. Meanwhile, data pipelines swiftly move data across systems, such as streaming activity logs to real-time analytics platforms for immediate insights.

Data quality

Ensuring data quality and governance is fundamental to ETL pipelines, incorporating thorough data validation, cleansing, and consistency checks during transformation. Data pipelines, in turn, may prioritize speed over rigorous data quality checks, primarily ensuring rapid and efficient data transfers without extensive validation.

ETL vs Data Pipelines: Use Cases

Let’s examine how data pipelines and ETL are applied in companies to streamline processes, increase agility, allow for competitive analytics, and make relevant decisions.

Use cases of data pipelines

Real-time analytics

Data pipelines stream data from sources like websites, applications, or user interactions into analytics platforms. This allows instant updates of analytics dashboards that enable businesses to monitor performance, user behavior, and system status continuously and in real-time.

For example, this may be seen in tracking real-time user interactions on E-commerce websites to adjust recommendations dynamically.

IoT and sensor data processing

IoT devices generate vast amounts of continuous, real-time data. Data pipelines efficiently capture, move, and process this sensor data to facilitate immediate alerts, predictive maintenance, or timely operational insights.

A good example is real-time monitoring of industrial equipment to prevent downtime through proactive maintenance.

Machine learning model training

Machine learning (ML) requires consistent and continuous data streams. Data pipelines automate data ingestion into ML environments, enabling frequent training, re-training, and deployment of accurate predictive models.

Automatic ingesting of transactional and user data into ML platforms to continuously improve recommendation models is a good illustration of this use case.

Multi-cloud or SaaS integration

Data pipelines simplify integration across multiple cloud platforms or SaaS applications, efficiently synchronizing data and ensuring real-time interoperability.

For example, data pipelines ensure seamless real-time data synchronization between ERP systems and CRM platforms (e.g., SAP Cloud ERP and Salesforce integration).

ETL Pipeline Use Cases

Enterprise data warehousing and reporting

ETL pipelines consolidate data from disparate enterprise sources into centralized data warehouses, ensuring comprehensive, high-quality datasets suitable for business intelligence, detailed reporting, and long-term analytical queries.

This use case can be illustrated by combining sales, HR, and inventory data into a central data warehouse for detailed cross-departmental analytics.

Regulatory compliance and auditing

ETL pipelines ensure regulatory compliance by systematically extracting, validating, transforming, and securely storing data necessary for audit trails and regulatory reporting.

Preparing financial transaction data for quarterly audits and regulatory reporting in financial institutions is handled with ETL.

SAP data consolidation

ETL pipelines handle ERP data from various SAP and non-SAP systems, consolidating complex financial, supply chain, and operational datasets for easier, more consistent analysis and reporting.

For example, ETL helps integrate SAP data from regional offices to provide global consolidated financial statements and supply chain analytics.

Combining ETL and data pipelines

In some cases, businesses can use both ETL and data pipelines in collaboration. This approach allows each pipeline to perform its specific tasks, benefitting companies with both ETL pipelines and data pipelines.

Structured ERP/financial data (ETL)

ETL pipelines perform rigorous transformations and quality checks for structured, sensitive, and transactional ERP or financial data when moving from various sources to SAP systems, ensuring accurate, reliable insights.

Use case: Processing and integrating monthly financial data from disparate sources into SAP Cloud ERP systems to support complex reporting and budgeting analyses.

Customer behavior, IoT, and log data (data pipelines)

Real-time data pipelines stream data directly from customer interactions, sensors, or application logs, ensuring timely insights and responsiveness to changing market trends or user demands.

Use case: Capturing real-time customer interactions on mobile apps or website logs, providing instant feedback to marketing teams, and improving user satisfaction dynamically by avoiding stockouts.

How DataLark Streamlines ETL and Data Pipelines

Both ETL and data pipelines need supervision to fix operational issues in a timely manner, as well as automation to streamline data movements and increase data processing speed for timely decision-making and prompt market response.

DataLark, a versatile data pipeline automation platform, will be a good choice when it comes to pipeline automation. The solution offers a robust and unified approach to simplifying ETL and data pipeline management with the help of its no-code, intuitive drag-and-drop interface. This allows users to create, orchestrate, and manage intricate data workflows without extensive technical expertise, decreasing the IT burden. Additionally, the visualized data mapping significantly reduces implementation time, enabling businesses to quickly automate their data flows.

DataLark can be deployed on-premise, in the cloud, or in hybrid environments, which makes the solution suitable for a broad range of businesses.

DataLark’s comprehensive integration capabilities support a vast range of connectors, notably deep SAP integration (SAP ECC, S/4HANA, and others), allowing seamless bidirectional data synchronization across SAP and non-SAP systems. This is especially beneficial in ETL scenarios where structured data from various systems and legacy applications must be consolidated reliably and securely into the ERP system for further analytics and processing.

DataLark supports trigger-based and schedule-based automation, so businesses can choose the option that suits them better and set up automation easily. Additionally, comprehensive data monitoring and automated alerts provide transparency of the data pipeline and ETL processes, allowing for continuous data flow monitoring and timely issue resolution.

DataLark’s Hybrid Approach in Action: Combining ETL and Data Pipelines

Project: SAP S/4HANA Migration with Ongoing Operations

Challenge: A large enterprise migrating to S/4HANA while maintaining business operations requires both batch data migration and real-time operational data flow.

ETL component (historical data migration with complex transformations):

\n
• Extract 10 years of transactional data from SAP ECC
\n
• Transform to S/4HANA data model (Universal Journal, new table structures)
\n
• Load in controlled batches with extensive validation
\n
• Process 500M+ records over 6-month migration period
\n

Data pipeline component (real-time operational data during migration):

\n
• Stream current business transactions to both ECC and S/4HANA systems
\n
• Ensure business continuity during migration phases
\n
• Real-time synchronization of master data changes
\n
• Handle 50,000+ daily transactions with zero business disruption
\n

Business Impact:

\n
• Migration completed 40% faster than with traditional approaches
\n
• Zero business downtime during migration
\n
• 99.8% data accuracy achieved in target S/4HANA system
\n

Conclusion

Data pipelines and ETL are similar yet different. While data pipelines encompass broader and less specific types of data movements, ETL is focused on accumulating data from multiple sources, cleansing and transforming it according to the format of a target system, and successfully loading the data into the destination database.

We hope this guide helps you better understand the difference between ETL and data pipelines and determine when to use each (or both) and how to automate both processes for real-time data analysis, streamlined decision-making, and quick reactions to whatever market or operational changes occur.

Read about key differences between a data pipeline and ETL (Extract, Transform, Load), the purpose for each, and the main use cases of both concepts.

Data Observability vs. Data Quality: Key Differences and Purposes Explained

Data observability and data quality are two similar yet different concepts. The key thing is that both of them are important for informative, valuable insights from the collected data.

In this post, we’ll discuss the differences between data observability and data quality, learn how they contribute to each other, and show how to handle both successfully in SAP systems.

What Is Data Quality

Data quality refers to how accurate, complete, relevant, valuable, compliant, and reliable your data is, and how effectively it can be used in your business. Certainly, data should be high-quality, empowering companies to make relevant, informed decisions, maintain operational efficiency, and comply with regulatory requirements.

Data quality encompasses six key dimensions:

\n
• Accuracy: This dimension assesses how many errors the collected data contains. Inaccurate data can lead to mistakes in service offerings, decision-making, miscommunication, and increased costs on fixes.
\n
• Completeness: Completeness evaluates whether all required data is present. Missing information can cause difficulties in data analysis, providing a distorted understanding of the real situation and leading to wrong decisions as a result.
\n
• Consistency: Consistency ensures that data remains uniform across different systems and datasets. Inconsistencies, like varying formats for dates or discrepancies across platforms, can cause confusion and errors in data processing.
\n
• Timeliness: Timeliness measures whether data is up-to-date and available when needed. Outdated information can lead to being unable to make prompt decisions and, as a result, falling behind the market competition.
\n
• Uniqueness: This dimension checks that each entity is represented only once within a dataset. Duplicate records can result in system computing capacities overload, slowed processes, and skewed analytics.
\n
• Validity: Validity examines whether data conforms to defined formats and standards. Deviations in data formats might lead to errors, data unreadability, and disruptions in system operations.
\n

What Is Data Observability

Data observability is the ability to monitor, understand, and maintain the health and reliability of data systems. This is ensured by comprehensive visibility into data pipelines, infrastructure, and usage. Data observability allows organizations to proactively detect, diagnose, and resolve data issues, making sure that all the company’s data remains trustworthy and systems operate efficiently.

Data observability includes the following five pillars:

\n
• Freshness: Freshness ensures that data is up-to-date and reflects the most recent information.
\n
• Distribution: This aspect shows whether data goes up or falls below the trustworthy range and detects anomalies.
\n
• Volume: Volume indicates whether the amount of data flowing through systems is full and complete, allowing businesses to spot issues in data sources due to inconsistent flows.
\n
• Schema: Schema tracks and records changes to data structures, as well as who made them and when.
\n
• Lineage: This pillar is a historical visibility into the data path like source, transformation, and end purpose.
\n

Real-World SAP Scenarios: Data Quality and Observability in Action

Understanding these concepts becomes clearer when we examine specific SAP scenarios where data quality and observability challenges commonly arise:

Case 1 — Master Data Management.

In SAP Material Master (MM), data quality issues often emerge when multiple plants create similar materials with slight variations in descriptions or specifications. For example, \"Steel Pipe 10mm\" vs \"Steel Pipe 10 mm\" vs \"Steel Pipe (10mm)\" represent the same item but appear as different materials, leading to inventory inefficiencies and procurement errors.

Data observability helps by monitoring material creation patterns, detecting duplicate entries in real-time, and tracking data lineage from creation through usage across different SAP modules (MM, SD, PP).

Case 2 — Financial Close Process.

During month-end financial close in SAP FI/CO, data quality problems can delay reporting when cost center assignments are incomplete or GL account mappings contain errors. Missing or inconsistent profit center data can make consolidation impossible.

Data observability provides visibility into the financial data pipeline, monitoring posting patterns, detecting anomalies in account balances, and ensuring all required data flows from operational modules (SD, MM, PP) to financial reporting are complete and timely.

Case 3 — Supply Chain Integration.

When integrating SAP with external suppliers through EDI or APIs, data quality issues arise from format inconsistencies, missing mandatory fields, or invalid reference numbers. A single incorrect material number can disrupt the entire procurement process.

Data observability tracks these integration points, monitors data volume and freshness from external sources, detects schema changes in supplier data formats, and provides lineage visibility from external systems through SAP processing to final business outcomes.

How Data Quality and Data Observability Are Related

Both data quality and data observability relate closely to data management, and both serve to ensure data unity, accuracy, and reliability. Still, in terms of “what was first”, data observability is a second-level concept that is based on data quality. In other words, data quality ensures the health of the initial data that enters an enterprise’s system, while data observability is aimed at continuous monitoring and analysis of this data throughout its whole lifecycle.

Because they are closely interconnected, data observability and data quality have some similarities:

\n
• Single purpose: Data observability and data quality help establish and maintain data health, reliability, and usability for data-driven, informative decision-making.
\n
• Advanced toolkit: Both require sophisticated solutions for proper work to automate complex processes.
\n
• Teamwork: Data observability and data quality need coordinated teamwork to ensure that the whole ecosystem of data is functioning properly.
\n
• Decision-making drivers: Data observability and data quality help businesses make accurate and relevant decisions due to real-time data delivery and completeness.
\n
• Continuity insurance: Both data observability and data quality facilitate seamless processes and uninterrupted operations by providing data cleanliness and maintaining data health within pipelines.
\n

Data Quality vs. Data Observability Differences

Yet, data quality and data observability are different. Despite their core goal being the same, they target different aspects of data management.

First, data quality and data observability have different objectives. Data quality focuses on ensuring data itself is accurate, complete, reliable, consistent, and valid, while data observability emphasizes visibility into the health and reliability of data systems, monitoring pipelines, infrastructure, and usage patterns to detect and fix problems proactively.

Besides, data quality is primarily concerned with data accuracy, completeness, validity, uniqueness, consistency, and timeliness. Data observability expands monitoring to the overall data ecosystem, tracking freshness, distribution, volume, schema changes, lineage, pipeline health, infrastructure performance, and user behavior.

Data quality and data observability also have different approaches. Data quality takes a reactive approach, correcting data once an issue is identified, while data observability usually takes a proactive approach, foreseeing, diagnosing, and addressing data issues before they significantly impact operations.

As may be seen from the name, data quality focuses on the quality and accuracy of data records themselves, while data observability is focused on visibility into the entire data infrastructure: pipeline stability, data movement, transformations, system performance, and usage patterns.

Data quality directly impacts decision accuracy by ensuring that the data is trustworthy. Data observability impacts operational efficiency by reducing system downtime, quickly diagnosing failures, and maintaining continuous data flows.

These two also rely on different techniques. Data quality uses validation rules, cleansing techniques, quality audits, and data profiling, while data observability relies on continuous monitoring, alerts, metrics tracking, anomaly detection, and automated root-cause analysis.

The summary table below will help you determine the key differences between data observability vs. data quality.

How DataLark Handles Data Quality and Data Observability in SAP Systems

Data quality and data observability issues can occur in many data systems, and SAP is no exception. Finding the right solution to the problem is the key to keeping your SAP environment operational capacity stable and accurate, allowing you to make data-based decisions, and stay ahead of the market.

DataLark, a sophisticated data management solution, focuses specifically on data quality and data observability in SAP and non-SAP systems. Its data management capabilities and user-friendly interface allow companies to successfully maintain data quality and ensure data observability, even without a coding background.

DataLark ensures exceptional data quality within SAP environments due to:

\n
• Automated data profiling and validation: DataLark integrates automated data profiling and data validation methods that analyze SAP data structures and highlight inconsistencies, anomalies, and missing data.
\n
• Rules-based cleansing and enrichment: Using tailored rulesets explicitly designed for SAP datasets, DataLark systematically cleanses incorrect or redundant data.
\n
• Continuous quality monitoring: DataLark continuously monitors data quality metrics, offering real-time insights and alerting SAP administrators to any deterioration in quality metrics, which enables prompt corrective actions.
\n

As for the data observability within SAP systems, DataLark offers the following:

\n
• Pipeline health and flow monitoring: DataLark ensures smooth and uninterrupted SAP data pipelines by continuously tracking their health, performance, and data flow to identify bottlenecks or issues in real time.
\n
• Anomaly detection: The platform utilizes advanced anomaly detection mechanisms that quickly identify unusual patterns or unexpected changes within SAP data systems, proactively mitigating risks.
\n
• Data lineage and traceability: DataLark offers comprehensive visibility into the origin and transformation of data across SAP systems, facilitating transparency and enabling efficient issue resolution and compliance.
\n
• Schema change detection: DataLark can monitor and immediately detect any schema changes in SAP systems, providing alerts to maintain system integrity and avoid disruptions.
\n

Conclusion

Data observability and data quality are different, yet they have a common goal: to ensure data consistency, compatibility, and health, and to make SAP systems process only valuable data that drive results.

We hope that this has helped you to understand the differences and key ideas of data quality and data observability, as well as to find a solution to possible data management issues.

Learn how data quality is different from data observability, see the basics of both concepts, and understand how to handle them successfully in SAP systems.

Learn how data quality is different from data observability, see the basics of both concepts, and understand how to handle them successfully in SAP systems.

Data Observability vs. Data Quality: Key Differences and Purposes Explained

Data observability and data quality are two similar yet different concepts. The key thing is that both of them are important for informative, valuable insights from the collected data.

In this post, we’ll discuss the differences between data observability and data quality, learn how they contribute to each other, and show how to handle both successfully in SAP systems.

What Is Data Quality

Data quality refers to how accurate, complete, relevant, valuable, compliant, and reliable your data is, and how effectively it can be used in your business. Certainly, data should be high-quality, empowering companies to make relevant, informed decisions, maintain operational efficiency, and comply with regulatory requirements.

Data quality encompasses six key dimensions:

\n
• Accuracy: This dimension assesses how many errors the collected data contains. Inaccurate data can lead to mistakes in service offerings, decision-making, miscommunication, and increased costs on fixes.
\n
• Completeness: Completeness evaluates whether all required data is present. Missing information can cause difficulties in data analysis, providing a distorted understanding of the real situation and leading to wrong decisions as a result.
\n
• Consistency: Consistency ensures that data remains uniform across different systems and datasets. Inconsistencies, like varying formats for dates or discrepancies across platforms, can cause confusion and errors in data processing.
\n
• Timeliness: Timeliness measures whether data is up-to-date and available when needed. Outdated information can lead to being unable to make prompt decisions and, as a result, falling behind the market competition.
\n
• Uniqueness: This dimension checks that each entity is represented only once within a dataset. Duplicate records can result in system computing capacities overload, slowed processes, and skewed analytics.
\n
• Validity: Validity examines whether data conforms to defined formats and standards. Deviations in data formats might lead to errors, data unreadability, and disruptions in system operations.
\n

What Is Data Observability

Data observability is the ability to monitor, understand, and maintain the health and reliability of data systems. This is ensured by comprehensive visibility into data pipelines, infrastructure, and usage. Data observability allows organizations to proactively detect, diagnose, and resolve data issues, making sure that all the company’s data remains trustworthy and systems operate efficiently.

Data observability includes the following five pillars:

\n
• Freshness: Freshness ensures that data is up-to-date and reflects the most recent information.
\n
• Distribution: This aspect shows whether data goes up or falls below the trustworthy range and detects anomalies.
\n
• Volume: Volume indicates whether the amount of data flowing through systems is full and complete, allowing businesses to spot issues in data sources due to inconsistent flows.
\n
• Schema: Schema tracks and records changes to data structures, as well as who made them and when.
\n
• Lineage: This pillar is a historical visibility into the data path like source, transformation, and end purpose.
\n

Real-World SAP Scenarios: Data Quality and Observability in Action

Understanding these concepts becomes clearer when we examine specific SAP scenarios where data quality and observability challenges commonly arise:

Case 1 — Master Data Management.

In SAP Material Master (MM), data quality issues often emerge when multiple plants create similar materials with slight variations in descriptions or specifications. For example, \"Steel Pipe 10mm\" vs \"Steel Pipe 10 mm\" vs \"Steel Pipe (10mm)\" represent the same item but appear as different materials, leading to inventory inefficiencies and procurement errors.

Data observability helps by monitoring material creation patterns, detecting duplicate entries in real-time, and tracking data lineage from creation through usage across different SAP modules (MM, SD, PP).

Case 2 — Financial Close Process.

During month-end financial close in SAP FI/CO, data quality problems can delay reporting when cost center assignments are incomplete or GL account mappings contain errors. Missing or inconsistent profit center data can make consolidation impossible.

Data observability provides visibility into the financial data pipeline, monitoring posting patterns, detecting anomalies in account balances, and ensuring all required data flows from operational modules (SD, MM, PP) to financial reporting are complete and timely.

Case 3 — Supply Chain Integration.

When integrating SAP with external suppliers through EDI or APIs, data quality issues arise from format inconsistencies, missing mandatory fields, or invalid reference numbers. A single incorrect material number can disrupt the entire procurement process.

Data observability tracks these integration points, monitors data volume and freshness from external sources, detects schema changes in supplier data formats, and provides lineage visibility from external systems through SAP processing to final business outcomes.

How Data Quality and Data Observability Are Related

Both data quality and data observability relate closely to data management, and both serve to ensure data unity, accuracy, and reliability. Still, in terms of “what was first”, data observability is a second-level concept that is based on data quality. In other words, data quality ensures the health of the initial data that enters an enterprise’s system, while data observability is aimed at continuous monitoring and analysis of this data throughout its whole lifecycle.

Because they are closely interconnected, data observability and data quality have some similarities:

\n
• Single purpose: Data observability and data quality help establish and maintain data health, reliability, and usability for data-driven, informative decision-making.
\n
• Advanced toolkit: Both require sophisticated solutions for proper work to automate complex processes.
\n
• Teamwork: Data observability and data quality need coordinated teamwork to ensure that the whole ecosystem of data is functioning properly.
\n
• Decision-making drivers: Data observability and data quality help businesses make accurate and relevant decisions due to real-time data delivery and completeness.
\n
• Continuity insurance: Both data observability and data quality facilitate seamless processes and uninterrupted operations by providing data cleanliness and maintaining data health within pipelines.
\n

Data Quality vs. Data Observability Differences

Yet, data quality and data observability are different. Despite their core goal being the same, they target different aspects of data management.

First, data quality and data observability have different objectives. Data quality focuses on ensuring data itself is accurate, complete, reliable, consistent, and valid, while data observability emphasizes visibility into the health and reliability of data systems, monitoring pipelines, infrastructure, and usage patterns to detect and fix problems proactively.

Besides, data quality is primarily concerned with data accuracy, completeness, validity, uniqueness, consistency, and timeliness. Data observability expands monitoring to the overall data ecosystem, tracking freshness, distribution, volume, schema changes, lineage, pipeline health, infrastructure performance, and user behavior.

Data quality and data observability also have different approaches. Data quality takes a reactive approach, correcting data once an issue is identified, while data observability usually takes a proactive approach, foreseeing, diagnosing, and addressing data issues before they significantly impact operations.

As may be seen from the name, data quality focuses on the quality and accuracy of data records themselves, while data observability is focused on visibility into the entire data infrastructure: pipeline stability, data movement, transformations, system performance, and usage patterns.

Data quality directly impacts decision accuracy by ensuring that the data is trustworthy. Data observability impacts operational efficiency by reducing system downtime, quickly diagnosing failures, and maintaining continuous data flows.

These two also rely on different techniques. Data quality uses validation rules, cleansing techniques, quality audits, and data profiling, while data observability relies on continuous monitoring, alerts, metrics tracking, anomaly detection, and automated root-cause analysis.

The summary table below will help you determine the key differences between data observability vs. data quality.

How DataLark Handles Data Quality and Data Observability in SAP Systems

Data quality and data observability issues can occur in many data systems, and SAP is no exception. Finding the right solution to the problem is the key to keeping your SAP environment operational capacity stable and accurate, allowing you to make data-based decisions, and stay ahead of the market.

DataLark, a sophisticated data management solution, focuses specifically on data quality and data observability in SAP and non-SAP systems. Its data management capabilities and user-friendly interface allow companies to successfully maintain data quality and ensure data observability, even without a coding background.

DataLark ensures exceptional data quality within SAP environments due to:

\n
• Automated data profiling and validation: DataLark integrates automated data profiling and data validation methods that analyze SAP data structures and highlight inconsistencies, anomalies, and missing data.
\n
• Rules-based cleansing and enrichment: Using tailored rulesets explicitly designed for SAP datasets, DataLark systematically cleanses incorrect or redundant data.
\n
• Continuous quality monitoring: DataLark continuously monitors data quality metrics, offering real-time insights and alerting SAP administrators to any deterioration in quality metrics, which enables prompt corrective actions.
\n

As for the data observability within SAP systems, DataLark offers the following:

\n
• Pipeline health and flow monitoring: DataLark ensures smooth and uninterrupted SAP data pipelines by continuously tracking their health, performance, and data flow to identify bottlenecks or issues in real time.
\n
• Anomaly detection: The platform utilizes advanced anomaly detection mechanisms that quickly identify unusual patterns or unexpected changes within SAP data systems, proactively mitigating risks.
\n
• Data lineage and traceability: DataLark offers comprehensive visibility into the origin and transformation of data across SAP systems, facilitating transparency and enabling efficient issue resolution and compliance.
\n
• Schema change detection: DataLark can monitor and immediately detect any schema changes in SAP systems, providing alerts to maintain system integrity and avoid disruptions.
\n

Conclusion

Data observability and data quality are different, yet they have a common goal: to ensure data consistency, compatibility, and health, and to make SAP systems process only valuable data that drive results.

We hope that this has helped you to understand the differences and key ideas of data quality and data observability, as well as to find a solution to possible data management issues.

Learn how data quality is different from data observability, see the basics of both concepts, and understand how to handle them successfully in SAP systems.

Learn how data quality is different from data observability, see the basics of both concepts, and understand how to handle them successfully in SAP systems.

Data Observability vs. Data Quality: Key Differences and Purposes Explained

Data observability and data quality are two similar yet different concepts. The key thing is that both of them are important for informative, valuable insights from the collected data.

In this post, we’ll discuss the differences between data observability and data quality, learn how they contribute to each other, and show how to handle both successfully in SAP systems.

What Is Data Quality

Data quality refers to how accurate, complete, relevant, valuable, compliant, and reliable your data is, and how effectively it can be used in your business. Certainly, data should be high-quality, empowering companies to make relevant, informed decisions, maintain operational efficiency, and comply with regulatory requirements.

Data quality encompasses six key dimensions:

\n
• Accuracy: This dimension assesses how many errors the collected data contains. Inaccurate data can lead to mistakes in service offerings, decision-making, miscommunication, and increased costs on fixes.
\n
• Completeness: Completeness evaluates whether all required data is present. Missing information can cause difficulties in data analysis, providing a distorted understanding of the real situation and leading to wrong decisions as a result.
\n
• Consistency: Consistency ensures that data remains uniform across different systems and datasets. Inconsistencies, like varying formats for dates or discrepancies across platforms, can cause confusion and errors in data processing.
\n
• Timeliness: Timeliness measures whether data is up-to-date and available when needed. Outdated information can lead to being unable to make prompt decisions and, as a result, falling behind the market competition.
\n
• Uniqueness: This dimension checks that each entity is represented only once within a dataset. Duplicate records can result in system computing capacities overload, slowed processes, and skewed analytics.
\n
• Validity: Validity examines whether data conforms to defined formats and standards. Deviations in data formats might lead to errors, data unreadability, and disruptions in system operations.
\n

What Is Data Observability

Data observability is the ability to monitor, understand, and maintain the health and reliability of data systems. This is ensured by comprehensive visibility into data pipelines, infrastructure, and usage. Data observability allows organizations to proactively detect, diagnose, and resolve data issues, making sure that all the company’s data remains trustworthy and systems operate efficiently.

Data observability includes the following five pillars:

\n
• Freshness: Freshness ensures that data is up-to-date and reflects the most recent information.
\n
• Distribution: This aspect shows whether data goes up or falls below the trustworthy range and detects anomalies.
\n
• Volume: Volume indicates whether the amount of data flowing through systems is full and complete, allowing businesses to spot issues in data sources due to inconsistent flows.
\n
• Schema: Schema tracks and records changes to data structures, as well as who made them and when.
\n
• Lineage: This pillar is a historical visibility into the data path like source, transformation, and end purpose.
\n

Real-World SAP Scenarios: Data Quality and Observability in Action

Understanding these concepts becomes clearer when we examine specific SAP scenarios where data quality and observability challenges commonly arise:

Case 1 — Master Data Management.

In SAP Material Master (MM), data quality issues often emerge when multiple plants create similar materials with slight variations in descriptions or specifications. For example, \"Steel Pipe 10mm\" vs \"Steel Pipe 10 mm\" vs \"Steel Pipe (10mm)\" represent the same item but appear as different materials, leading to inventory inefficiencies and procurement errors.

Data observability helps by monitoring material creation patterns, detecting duplicate entries in real-time, and tracking data lineage from creation through usage across different SAP modules (MM, SD, PP).

Case 2 — Financial Close Process.

During month-end financial close in SAP FI/CO, data quality problems can delay reporting when cost center assignments are incomplete or GL account mappings contain errors. Missing or inconsistent profit center data can make consolidation impossible.

Data observability provides visibility into the financial data pipeline, monitoring posting patterns, detecting anomalies in account balances, and ensuring all required data flows from operational modules (SD, MM, PP) to financial reporting are complete and timely.

Case 3 — Supply Chain Integration.

When integrating SAP with external suppliers through EDI or APIs, data quality issues arise from format inconsistencies, missing mandatory fields, or invalid reference numbers. A single incorrect material number can disrupt the entire procurement process.

Data observability tracks these integration points, monitors data volume and freshness from external sources, detects schema changes in supplier data formats, and provides lineage visibility from external systems through SAP processing to final business outcomes.

How Data Quality and Data Observability Are Related

Both data quality and data observability relate closely to data management, and both serve to ensure data unity, accuracy, and reliability. Still, in terms of “what was first”, data observability is a second-level concept that is based on data quality. In other words, data quality ensures the health of the initial data that enters an enterprise’s system, while data observability is aimed at continuous monitoring and analysis of this data throughout its whole lifecycle.

Because they are closely interconnected, data observability and data quality have some similarities:

\n
• Single purpose: Data observability and data quality help establish and maintain data health, reliability, and usability for data-driven, informative decision-making.
\n
• Advanced toolkit: Both require sophisticated solutions for proper work to automate complex processes.
\n
• Teamwork: Data observability and data quality need coordinated teamwork to ensure that the whole ecosystem of data is functioning properly.
\n
• Decision-making drivers: Data observability and data quality help businesses make accurate and relevant decisions due to real-time data delivery and completeness.
\n
• Continuity insurance: Both data observability and data quality facilitate seamless processes and uninterrupted operations by providing data cleanliness and maintaining data health within pipelines.
\n

Data Quality vs. Data Observability Differences

Yet, data quality and data observability are different. Despite their core goal being the same, they target different aspects of data management.

First, data quality and data observability have different objectives. Data quality focuses on ensuring data itself is accurate, complete, reliable, consistent, and valid, while data observability emphasizes visibility into the health and reliability of data systems, monitoring pipelines, infrastructure, and usage patterns to detect and fix problems proactively.

Besides, data quality is primarily concerned with data accuracy, completeness, validity, uniqueness, consistency, and timeliness. Data observability expands monitoring to the overall data ecosystem, tracking freshness, distribution, volume, schema changes, lineage, pipeline health, infrastructure performance, and user behavior.

Data quality and data observability also have different approaches. Data quality takes a reactive approach, correcting data once an issue is identified, while data observability usually takes a proactive approach, foreseeing, diagnosing, and addressing data issues before they significantly impact operations.

As may be seen from the name, data quality focuses on the quality and accuracy of data records themselves, while data observability is focused on visibility into the entire data infrastructure: pipeline stability, data movement, transformations, system performance, and usage patterns.

Data quality directly impacts decision accuracy by ensuring that the data is trustworthy. Data observability impacts operational efficiency by reducing system downtime, quickly diagnosing failures, and maintaining continuous data flows.

These two also rely on different techniques. Data quality uses validation rules, cleansing techniques, quality audits, and data profiling, while data observability relies on continuous monitoring, alerts, metrics tracking, anomaly detection, and automated root-cause analysis.

The summary table below will help you determine the key differences between data observability vs. data quality.

How DataLark Handles Data Quality and Data Observability in SAP Systems

Data quality and data observability issues can occur in many data systems, and SAP is no exception. Finding the right solution to the problem is the key to keeping your SAP environment operational capacity stable and accurate, allowing you to make data-based decisions, and stay ahead of the market.

DataLark, a sophisticated data management solution, focuses specifically on data quality and data observability in SAP and non-SAP systems. Its data management capabilities and user-friendly interface allow companies to successfully maintain data quality and ensure data observability, even without a coding background.

DataLark ensures exceptional data quality within SAP environments due to:

\n
• Automated data profiling and validation: DataLark integrates automated data profiling and data validation methods that analyze SAP data structures and highlight inconsistencies, anomalies, and missing data.
\n
• Rules-based cleansing and enrichment: Using tailored rulesets explicitly designed for SAP datasets, DataLark systematically cleanses incorrect or redundant data.
\n
• Continuous quality monitoring: DataLark continuously monitors data quality metrics, offering real-time insights and alerting SAP administrators to any deterioration in quality metrics, which enables prompt corrective actions.
\n

As for the data observability within SAP systems, DataLark offers the following:

\n
• Pipeline health and flow monitoring: DataLark ensures smooth and uninterrupted SAP data pipelines by continuously tracking their health, performance, and data flow to identify bottlenecks or issues in real time.
\n
• Anomaly detection: The platform utilizes advanced anomaly detection mechanisms that quickly identify unusual patterns or unexpected changes within SAP data systems, proactively mitigating risks.
\n
• Data lineage and traceability: DataLark offers comprehensive visibility into the origin and transformation of data across SAP systems, facilitating transparency and enabling efficient issue resolution and compliance.
\n
• Schema change detection: DataLark can monitor and immediately detect any schema changes in SAP systems, providing alerts to maintain system integrity and avoid disruptions.
\n

Conclusion

Data observability and data quality are different, yet they have a common goal: to ensure data consistency, compatibility, and health, and to make SAP systems process only valuable data that drive results.

We hope that this has helped you to understand the differences and key ideas of data quality and data observability, as well as to find a solution to possible data management issues.

Learn how data quality is different from data observability, see the basics of both concepts, and understand how to handle them successfully in SAP systems.

Data Observability vs. Data Quality: Key Differences and Purposes Explained

Data observability and data quality are two similar yet different concepts. The key thing is that both of them are important for informative, valuable insights from the collected data.

In this post, we’ll discuss the differences between data observability and data quality, learn how they contribute to each other, and show how to handle both successfully in SAP systems.

What Is Data Quality

Data quality refers to how accurate, complete, relevant, valuable, compliant, and reliable your data is, and how effectively it can be used in your business. Certainly, data should be high-quality, empowering companies to make relevant, informed decisions, maintain operational efficiency, and comply with regulatory requirements.

Data quality encompasses six key dimensions:

\n
• Accuracy: This dimension assesses how many errors the collected data contains. Inaccurate data can lead to mistakes in service offerings, decision-making, miscommunication, and increased costs on fixes.
\n
• Completeness: Completeness evaluates whether all required data is present. Missing information can cause difficulties in data analysis, providing a distorted understanding of the real situation and leading to wrong decisions as a result.
\n
• Consistency: Consistency ensures that data remains uniform across different systems and datasets. Inconsistencies, like varying formats for dates or discrepancies across platforms, can cause confusion and errors in data processing.
\n
• Timeliness: Timeliness measures whether data is up-to-date and available when needed. Outdated information can lead to being unable to make prompt decisions and, as a result, falling behind the market competition.
\n
• Uniqueness: This dimension checks that each entity is represented only once within a dataset. Duplicate records can result in system computing capacities overload, slowed processes, and skewed analytics.
\n
• Validity: Validity examines whether data conforms to defined formats and standards. Deviations in data formats might lead to errors, data unreadability, and disruptions in system operations.
\n

What Is Data Observability

Data observability is the ability to monitor, understand, and maintain the health and reliability of data systems. This is ensured by comprehensive visibility into data pipelines, infrastructure, and usage. Data observability allows organizations to proactively detect, diagnose, and resolve data issues, making sure that all the company’s data remains trustworthy and systems operate efficiently.

Data observability includes the following five pillars:

\n
• Freshness: Freshness ensures that data is up-to-date and reflects the most recent information.
\n
• Distribution: This aspect shows whether data goes up or falls below the trustworthy range and detects anomalies.
\n
• Volume: Volume indicates whether the amount of data flowing through systems is full and complete, allowing businesses to spot issues in data sources due to inconsistent flows.
\n
• Schema: Schema tracks and records changes to data structures, as well as who made them and when.
\n
• Lineage: This pillar is a historical visibility into the data path like source, transformation, and end purpose.
\n

Real-World SAP Scenarios: Data Quality and Observability in Action

Understanding these concepts becomes clearer when we examine specific SAP scenarios where data quality and observability challenges commonly arise:

Case 1 — Master Data Management.

In SAP Material Master (MM), data quality issues often emerge when multiple plants create similar materials with slight variations in descriptions or specifications. For example, \"Steel Pipe 10mm\" vs \"Steel Pipe 10 mm\" vs \"Steel Pipe (10mm)\" represent the same item but appear as different materials, leading to inventory inefficiencies and procurement errors.

Data observability helps by monitoring material creation patterns, detecting duplicate entries in real-time, and tracking data lineage from creation through usage across different SAP modules (MM, SD, PP).

Case 2 — Financial Close Process.

During month-end financial close in SAP FI/CO, data quality problems can delay reporting when cost center assignments are incomplete or GL account mappings contain errors. Missing or inconsistent profit center data can make consolidation impossible.

Data observability provides visibility into the financial data pipeline, monitoring posting patterns, detecting anomalies in account balances, and ensuring all required data flows from operational modules (SD, MM, PP) to financial reporting are complete and timely.

Case 3 — Supply Chain Integration.

When integrating SAP with external suppliers through EDI or APIs, data quality issues arise from format inconsistencies, missing mandatory fields, or invalid reference numbers. A single incorrect material number can disrupt the entire procurement process.

Data observability tracks these integration points, monitors data volume and freshness from external sources, detects schema changes in supplier data formats, and provides lineage visibility from external systems through SAP processing to final business outcomes.

How Data Quality and Data Observability Are Related

Both data quality and data observability relate closely to data management, and both serve to ensure data unity, accuracy, and reliability. Still, in terms of “what was first”, data observability is a second-level concept that is based on data quality. In other words, data quality ensures the health of the initial data that enters an enterprise’s system, while data observability is aimed at continuous monitoring and analysis of this data throughout its whole lifecycle.

Because they are closely interconnected, data observability and data quality have some similarities:

\n
• Single purpose: Data observability and data quality help establish and maintain data health, reliability, and usability for data-driven, informative decision-making.
\n
• Advanced toolkit: Both require sophisticated solutions for proper work to automate complex processes.
\n
• Teamwork: Data observability and data quality need coordinated teamwork to ensure that the whole ecosystem of data is functioning properly.
\n
• Decision-making drivers: Data observability and data quality help businesses make accurate and relevant decisions due to real-time data delivery and completeness.
\n
• Continuity insurance: Both data observability and data quality facilitate seamless processes and uninterrupted operations by providing data cleanliness and maintaining data health within pipelines.
\n

Data Quality vs. Data Observability Differences

Yet, data quality and data observability are different. Despite their core goal being the same, they target different aspects of data management.

First, data quality and data observability have different objectives. Data quality focuses on ensuring data itself is accurate, complete, reliable, consistent, and valid, while data observability emphasizes visibility into the health and reliability of data systems, monitoring pipelines, infrastructure, and usage patterns to detect and fix problems proactively.

Besides, data quality is primarily concerned with data accuracy, completeness, validity, uniqueness, consistency, and timeliness. Data observability expands monitoring to the overall data ecosystem, tracking freshness, distribution, volume, schema changes, lineage, pipeline health, infrastructure performance, and user behavior.

Data quality and data observability also have different approaches. Data quality takes a reactive approach, correcting data once an issue is identified, while data observability usually takes a proactive approach, foreseeing, diagnosing, and addressing data issues before they significantly impact operations.

As may be seen from the name, data quality focuses on the quality and accuracy of data records themselves, while data observability is focused on visibility into the entire data infrastructure: pipeline stability, data movement, transformations, system performance, and usage patterns.

Data quality directly impacts decision accuracy by ensuring that the data is trustworthy. Data observability impacts operational efficiency by reducing system downtime, quickly diagnosing failures, and maintaining continuous data flows.

These two also rely on different techniques. Data quality uses validation rules, cleansing techniques, quality audits, and data profiling, while data observability relies on continuous monitoring, alerts, metrics tracking, anomaly detection, and automated root-cause analysis.

The summary table below will help you determine the key differences between data observability vs. data quality.

How DataLark Handles Data Quality and Data Observability in SAP Systems

Data quality and data observability issues can occur in many data systems, and SAP is no exception. Finding the right solution to the problem is the key to keeping your SAP environment operational capacity stable and accurate, allowing you to make data-based decisions, and stay ahead of the market.

DataLark, a sophisticated data management solution, focuses specifically on data quality and data observability in SAP and non-SAP systems. Its data management capabilities and user-friendly interface allow companies to successfully maintain data quality and ensure data observability, even without a coding background.

DataLark ensures exceptional data quality within SAP environments due to:

\n
• Automated data profiling and validation: DataLark integrates automated data profiling and data validation methods that analyze SAP data structures and highlight inconsistencies, anomalies, and missing data.
\n
• Rules-based cleansing and enrichment: Using tailored rulesets explicitly designed for SAP datasets, DataLark systematically cleanses incorrect or redundant data.
\n
• Continuous quality monitoring: DataLark continuously monitors data quality metrics, offering real-time insights and alerting SAP administrators to any deterioration in quality metrics, which enables prompt corrective actions.
\n

As for the data observability within SAP systems, DataLark offers the following:

\n
• Pipeline health and flow monitoring: DataLark ensures smooth and uninterrupted SAP data pipelines by continuously tracking their health, performance, and data flow to identify bottlenecks or issues in real time.
\n
• Anomaly detection: The platform utilizes advanced anomaly detection mechanisms that quickly identify unusual patterns or unexpected changes within SAP data systems, proactively mitigating risks.
\n
• Data lineage and traceability: DataLark offers comprehensive visibility into the origin and transformation of data across SAP systems, facilitating transparency and enabling efficient issue resolution and compliance.
\n
• Schema change detection: DataLark can monitor and immediately detect any schema changes in SAP systems, providing alerts to maintain system integrity and avoid disruptions.
\n

Conclusion

Data observability and data quality are different, yet they have a common goal: to ensure data consistency, compatibility, and health, and to make SAP systems process only valuable data that drive results.

We hope that this has helped you to understand the differences and key ideas of data quality and data observability, as well as to find a solution to possible data management issues.

Learn how data quality is different from data observability, see the basics of both concepts, and understand how to handle them successfully in SAP systems.

Learn how data quality is different from data observability, see the basics of both concepts, and understand how to handle them successfully in SAP systems.

Learn how data quality is different from data observability, see the basics of both concepts, and understand how to handle them successfully in SAP systems.

Data Observability vs. Data Quality: Key Differences and Purposes Explained

Data observability and data quality are two similar yet different concepts. The key thing is that both of them are important for informative, valuable insights from the collected data.

In this post, we’ll discuss the differences between data observability and data quality, learn how they contribute to each other, and show how to handle both successfully in SAP systems.

What Is Data Quality

Data quality refers to how accurate, complete, relevant, valuable, compliant, and reliable your data is, and how effectively it can be used in your business. Certainly, data should be high-quality, empowering companies to make relevant, informed decisions, maintain operational efficiency, and comply with regulatory requirements.

Data quality encompasses six key dimensions:

\n
• Accuracy: This dimension assesses how many errors the collected data contains. Inaccurate data can lead to mistakes in service offerings, decision-making, miscommunication, and increased costs on fixes.
\n
• Completeness: Completeness evaluates whether all required data is present. Missing information can cause difficulties in data analysis, providing a distorted understanding of the real situation and leading to wrong decisions as a result.
\n
• Consistency: Consistency ensures that data remains uniform across different systems and datasets. Inconsistencies, like varying formats for dates or discrepancies across platforms, can cause confusion and errors in data processing.
\n
• Timeliness: Timeliness measures whether data is up-to-date and available when needed. Outdated information can lead to being unable to make prompt decisions and, as a result, falling behind the market competition.
\n
• Uniqueness: This dimension checks that each entity is represented only once within a dataset. Duplicate records can result in system computing capacities overload, slowed processes, and skewed analytics.
\n
• Validity: Validity examines whether data conforms to defined formats and standards. Deviations in data formats might lead to errors, data unreadability, and disruptions in system operations.
\n

What Is Data Observability

Data observability is the ability to monitor, understand, and maintain the health and reliability of data systems. This is ensured by comprehensive visibility into data pipelines, infrastructure, and usage. Data observability allows organizations to proactively detect, diagnose, and resolve data issues, making sure that all the company’s data remains trustworthy and systems operate efficiently.

Data observability includes the following five pillars:

\n
• Freshness: Freshness ensures that data is up-to-date and reflects the most recent information.
\n
• Distribution: This aspect shows whether data goes up or falls below the trustworthy range and detects anomalies.
\n
• Volume: Volume indicates whether the amount of data flowing through systems is full and complete, allowing businesses to spot issues in data sources due to inconsistent flows.
\n
• Schema: Schema tracks and records changes to data structures, as well as who made them and when.
\n
• Lineage: This pillar is a historical visibility into the data path like source, transformation, and end purpose.
\n

Real-World SAP Scenarios: Data Quality and Observability in Action

Understanding these concepts becomes clearer when we examine specific SAP scenarios where data quality and observability challenges commonly arise:

Case 1 — Master Data Management.

In SAP Material Master (MM), data quality issues often emerge when multiple plants create similar materials with slight variations in descriptions or specifications. For example, \"Steel Pipe 10mm\" vs \"Steel Pipe 10 mm\" vs \"Steel Pipe (10mm)\" represent the same item but appear as different materials, leading to inventory inefficiencies and procurement errors.

Data observability helps by monitoring material creation patterns, detecting duplicate entries in real-time, and tracking data lineage from creation through usage across different SAP modules (MM, SD, PP).

Case 2 — Financial Close Process.

During month-end financial close in SAP FI/CO, data quality problems can delay reporting when cost center assignments are incomplete or GL account mappings contain errors. Missing or inconsistent profit center data can make consolidation impossible.

Data observability provides visibility into the financial data pipeline, monitoring posting patterns, detecting anomalies in account balances, and ensuring all required data flows from operational modules (SD, MM, PP) to financial reporting are complete and timely.

Case 3 — Supply Chain Integration.

When integrating SAP with external suppliers through EDI or APIs, data quality issues arise from format inconsistencies, missing mandatory fields, or invalid reference numbers. A single incorrect material number can disrupt the entire procurement process.

Data observability tracks these integration points, monitors data volume and freshness from external sources, detects schema changes in supplier data formats, and provides lineage visibility from external systems through SAP processing to final business outcomes.

How Data Quality and Data Observability Are Related

Both data quality and data observability relate closely to data management, and both serve to ensure data unity, accuracy, and reliability. Still, in terms of “what was first”, data observability is a second-level concept that is based on data quality. In other words, data quality ensures the health of the initial data that enters an enterprise’s system, while data observability is aimed at continuous monitoring and analysis of this data throughout its whole lifecycle.

Because they are closely interconnected, data observability and data quality have some similarities:

\n
• Single purpose: Data observability and data quality help establish and maintain data health, reliability, and usability for data-driven, informative decision-making.
\n
• Advanced toolkit: Both require sophisticated solutions for proper work to automate complex processes.
\n
• Teamwork: Data observability and data quality need coordinated teamwork to ensure that the whole ecosystem of data is functioning properly.
\n
• Decision-making drivers: Data observability and data quality help businesses make accurate and relevant decisions due to real-time data delivery and completeness.
\n
• Continuity insurance: Both data observability and data quality facilitate seamless processes and uninterrupted operations by providing data cleanliness and maintaining data health within pipelines.
\n

Data Quality vs. Data Observability Differences

Yet, data quality and data observability are different. Despite their core goal being the same, they target different aspects of data management.

First, data quality and data observability have different objectives. Data quality focuses on ensuring data itself is accurate, complete, reliable, consistent, and valid, while data observability emphasizes visibility into the health and reliability of data systems, monitoring pipelines, infrastructure, and usage patterns to detect and fix problems proactively.

Besides, data quality is primarily concerned with data accuracy, completeness, validity, uniqueness, consistency, and timeliness. Data observability expands monitoring to the overall data ecosystem, tracking freshness, distribution, volume, schema changes, lineage, pipeline health, infrastructure performance, and user behavior.

Data quality and data observability also have different approaches. Data quality takes a reactive approach, correcting data once an issue is identified, while data observability usually takes a proactive approach, foreseeing, diagnosing, and addressing data issues before they significantly impact operations.

As may be seen from the name, data quality focuses on the quality and accuracy of data records themselves, while data observability is focused on visibility into the entire data infrastructure: pipeline stability, data movement, transformations, system performance, and usage patterns.

Data quality directly impacts decision accuracy by ensuring that the data is trustworthy. Data observability impacts operational efficiency by reducing system downtime, quickly diagnosing failures, and maintaining continuous data flows.

These two also rely on different techniques. Data quality uses validation rules, cleansing techniques, quality audits, and data profiling, while data observability relies on continuous monitoring, alerts, metrics tracking, anomaly detection, and automated root-cause analysis.

The summary table below will help you determine the key differences between data observability vs. data quality.

How DataLark Handles Data Quality and Data Observability in SAP Systems

Data quality and data observability issues can occur in many data systems, and SAP is no exception. Finding the right solution to the problem is the key to keeping your SAP environment operational capacity stable and accurate, allowing you to make data-based decisions, and stay ahead of the market.

DataLark, a sophisticated data management solution, focuses specifically on data quality and data observability in SAP and non-SAP systems. Its data management capabilities and user-friendly interface allow companies to successfully maintain data quality and ensure data observability, even without a coding background.

DataLark ensures exceptional data quality within SAP environments due to:

\n
• Automated data profiling and validation: DataLark integrates automated data profiling and data validation methods that analyze SAP data structures and highlight inconsistencies, anomalies, and missing data.
\n
• Rules-based cleansing and enrichment: Using tailored rulesets explicitly designed for SAP datasets, DataLark systematically cleanses incorrect or redundant data.
\n
• Continuous quality monitoring: DataLark continuously monitors data quality metrics, offering real-time insights and alerting SAP administrators to any deterioration in quality metrics, which enables prompt corrective actions.
\n

As for the data observability within SAP systems, DataLark offers the following:

\n
• Pipeline health and flow monitoring: DataLark ensures smooth and uninterrupted SAP data pipelines by continuously tracking their health, performance, and data flow to identify bottlenecks or issues in real time.
\n
• Anomaly detection: The platform utilizes advanced anomaly detection mechanisms that quickly identify unusual patterns or unexpected changes within SAP data systems, proactively mitigating risks.
\n
• Data lineage and traceability: DataLark offers comprehensive visibility into the origin and transformation of data across SAP systems, facilitating transparency and enabling efficient issue resolution and compliance.
\n
• Schema change detection: DataLark can monitor and immediately detect any schema changes in SAP systems, providing alerts to maintain system integrity and avoid disruptions.
\n

Conclusion

Data observability and data quality are different, yet they have a common goal: to ensure data consistency, compatibility, and health, and to make SAP systems process only valuable data that drive results.

We hope that this has helped you to understand the differences and key ideas of data quality and data observability, as well as to find a solution to possible data management issues.

Learn how data quality is different from data observability, see the basics of both concepts, and understand how to handle them successfully in SAP systems.

Learn how data quality is different from data observability, see the basics of both concepts, and understand how to handle them successfully in SAP systems.

Learn how data quality is different from data observability, see the basics of both concepts, and understand how to handle them successfully in SAP systems.

Data Observability vs. Data Quality: Key Differences and Purposes Explained

Data observability and data quality are two similar yet different concepts. The key thing is that both of them are important for informative, valuable insights from the collected data.

In this post, we’ll discuss the differences between data observability and data quality, learn how they contribute to each other, and show how to handle both successfully in SAP systems.

What Is Data Quality

Data quality refers to how accurate, complete, relevant, valuable, compliant, and reliable your data is, and how effectively it can be used in your business. Certainly, data should be high-quality, empowering companies to make relevant, informed decisions, maintain operational efficiency, and comply with regulatory requirements.

Data quality encompasses six key dimensions:

\n
• Accuracy: This dimension assesses how many errors the collected data contains. Inaccurate data can lead to mistakes in service offerings, decision-making, miscommunication, and increased costs on fixes.
\n
• Completeness: Completeness evaluates whether all required data is present. Missing information can cause difficulties in data analysis, providing a distorted understanding of the real situation and leading to wrong decisions as a result.
\n
• Consistency: Consistency ensures that data remains uniform across different systems and datasets. Inconsistencies, like varying formats for dates or discrepancies across platforms, can cause confusion and errors in data processing.
\n
• Timeliness: Timeliness measures whether data is up-to-date and available when needed. Outdated information can lead to being unable to make prompt decisions and, as a result, falling behind the market competition.
\n
• Uniqueness: This dimension checks that each entity is represented only once within a dataset. Duplicate records can result in system computing capacities overload, slowed processes, and skewed analytics.
\n
• Validity: Validity examines whether data conforms to defined formats and standards. Deviations in data formats might lead to errors, data unreadability, and disruptions in system operations.
\n

What Is Data Observability

Data observability is the ability to monitor, understand, and maintain the health and reliability of data systems. This is ensured by comprehensive visibility into data pipelines, infrastructure, and usage. Data observability allows organizations to proactively detect, diagnose, and resolve data issues, making sure that all the company’s data remains trustworthy and systems operate efficiently.

Data observability includes the following five pillars:

\n
• Freshness: Freshness ensures that data is up-to-date and reflects the most recent information.
\n
• Distribution: This aspect shows whether data goes up or falls below the trustworthy range and detects anomalies.
\n
• Volume: Volume indicates whether the amount of data flowing through systems is full and complete, allowing businesses to spot issues in data sources due to inconsistent flows.
\n
• Schema: Schema tracks and records changes to data structures, as well as who made them and when.
\n
• Lineage: This pillar is a historical visibility into the data path like source, transformation, and end purpose.
\n

Real-World SAP Scenarios: Data Quality and Observability in Action

Understanding these concepts becomes clearer when we examine specific SAP scenarios where data quality and observability challenges commonly arise:

Case 1 — Master Data Management.

In SAP Material Master (MM), data quality issues often emerge when multiple plants create similar materials with slight variations in descriptions or specifications. For example, \"Steel Pipe 10mm\" vs \"Steel Pipe 10 mm\" vs \"Steel Pipe (10mm)\" represent the same item but appear as different materials, leading to inventory inefficiencies and procurement errors.

Data observability helps by monitoring material creation patterns, detecting duplicate entries in real-time, and tracking data lineage from creation through usage across different SAP modules (MM, SD, PP).

Case 2 — Financial Close Process.

During month-end financial close in SAP FI/CO, data quality problems can delay reporting when cost center assignments are incomplete or GL account mappings contain errors. Missing or inconsistent profit center data can make consolidation impossible.

Data observability provides visibility into the financial data pipeline, monitoring posting patterns, detecting anomalies in account balances, and ensuring all required data flows from operational modules (SD, MM, PP) to financial reporting are complete and timely.

Case 3 — Supply Chain Integration.

When integrating SAP with external suppliers through EDI or APIs, data quality issues arise from format inconsistencies, missing mandatory fields, or invalid reference numbers. A single incorrect material number can disrupt the entire procurement process.

Data observability tracks these integration points, monitors data volume and freshness from external sources, detects schema changes in supplier data formats, and provides lineage visibility from external systems through SAP processing to final business outcomes.

How Data Quality and Data Observability Are Related

Both data quality and data observability relate closely to data management, and both serve to ensure data unity, accuracy, and reliability. Still, in terms of “what was first”, data observability is a second-level concept that is based on data quality. In other words, data quality ensures the health of the initial data that enters an enterprise’s system, while data observability is aimed at continuous monitoring and analysis of this data throughout its whole lifecycle.

Because they are closely interconnected, data observability and data quality have some similarities:

\n
• Single purpose: Data observability and data quality help establish and maintain data health, reliability, and usability for data-driven, informative decision-making.
\n
• Advanced toolkit: Both require sophisticated solutions for proper work to automate complex processes.
\n
• Teamwork: Data observability and data quality need coordinated teamwork to ensure that the whole ecosystem of data is functioning properly.
\n
• Decision-making drivers: Data observability and data quality help businesses make accurate and relevant decisions due to real-time data delivery and completeness.
\n
• Continuity insurance: Both data observability and data quality facilitate seamless processes and uninterrupted operations by providing data cleanliness and maintaining data health within pipelines.
\n

Data Quality vs. Data Observability Differences

Yet, data quality and data observability are different. Despite their core goal being the same, they target different aspects of data management.

First, data quality and data observability have different objectives. Data quality focuses on ensuring data itself is accurate, complete, reliable, consistent, and valid, while data observability emphasizes visibility into the health and reliability of data systems, monitoring pipelines, infrastructure, and usage patterns to detect and fix problems proactively.

Besides, data quality is primarily concerned with data accuracy, completeness, validity, uniqueness, consistency, and timeliness. Data observability expands monitoring to the overall data ecosystem, tracking freshness, distribution, volume, schema changes, lineage, pipeline health, infrastructure performance, and user behavior.

Data quality and data observability also have different approaches. Data quality takes a reactive approach, correcting data once an issue is identified, while data observability usually takes a proactive approach, foreseeing, diagnosing, and addressing data issues before they significantly impact operations.

As may be seen from the name, data quality focuses on the quality and accuracy of data records themselves, while data observability is focused on visibility into the entire data infrastructure: pipeline stability, data movement, transformations, system performance, and usage patterns.

Data quality directly impacts decision accuracy by ensuring that the data is trustworthy. Data observability impacts operational efficiency by reducing system downtime, quickly diagnosing failures, and maintaining continuous data flows.

These two also rely on different techniques. Data quality uses validation rules, cleansing techniques, quality audits, and data profiling, while data observability relies on continuous monitoring, alerts, metrics tracking, anomaly detection, and automated root-cause analysis.

The summary table below will help you determine the key differences between data observability vs. data quality.

How DataLark Handles Data Quality and Data Observability in SAP Systems

Data quality and data observability issues can occur in many data systems, and SAP is no exception. Finding the right solution to the problem is the key to keeping your SAP environment operational capacity stable and accurate, allowing you to make data-based decisions, and stay ahead of the market.

DataLark, a sophisticated data management solution, focuses specifically on data quality and data observability in SAP and non-SAP systems. Its data management capabilities and user-friendly interface allow companies to successfully maintain data quality and ensure data observability, even without a coding background.

DataLark ensures exceptional data quality within SAP environments due to:

\n
• Automated data profiling and validation: DataLark integrates automated data profiling and data validation methods that analyze SAP data structures and highlight inconsistencies, anomalies, and missing data.
\n
• Rules-based cleansing and enrichment: Using tailored rulesets explicitly designed for SAP datasets, DataLark systematically cleanses incorrect or redundant data.
\n
• Continuous quality monitoring: DataLark continuously monitors data quality metrics, offering real-time insights and alerting SAP administrators to any deterioration in quality metrics, which enables prompt corrective actions.
\n

As for the data observability within SAP systems, DataLark offers the following:

\n
• Pipeline health and flow monitoring: DataLark ensures smooth and uninterrupted SAP data pipelines by continuously tracking their health, performance, and data flow to identify bottlenecks or issues in real time.
\n
• Anomaly detection: The platform utilizes advanced anomaly detection mechanisms that quickly identify unusual patterns or unexpected changes within SAP data systems, proactively mitigating risks.
\n
• Data lineage and traceability: DataLark offers comprehensive visibility into the origin and transformation of data across SAP systems, facilitating transparency and enabling efficient issue resolution and compliance.
\n
• Schema change detection: DataLark can monitor and immediately detect any schema changes in SAP systems, providing alerts to maintain system integrity and avoid disruptions.
\n

Conclusion

Data observability and data quality are different, yet they have a common goal: to ensure data consistency, compatibility, and health, and to make SAP systems process only valuable data that drive results.

We hope that this has helped you to understand the differences and key ideas of data quality and data observability, as well as to find a solution to possible data management issues.

Learn how data quality is different from data observability, see the basics of both concepts, and understand how to handle them successfully in SAP systems.

IoT and SAP Integration Explained: Benefits, Challenges, and Application Areas

With data-driven approaches to literally every aspect of a business taking over the world, integrating your ERP with IoT technology is just a matter of time. Integrating IoT with SAP allows enterprises to effectively transform field data into actionable insights, benefiting the business’s agility, competitiveness, efficiency, and profitability.

In this article, we’ll dig deeper into the benefits of integrating IoT and SAP, cover the main challenges one can face during integration, list the major areas of application (spoiler: they are useful for every business), and review available IoT integration solutions.

IoT and SAP Integration Benefits

Let’s take a look at the core benefits of IoT and SAP integration for businesses. All of them allow companies to outperform their competitors and keep up with the ever-evolving business landscape.

Enhanced decision-making

Data retrieved from IoT allows businesses to enhance their decision-making process and introduce a proactive approach to operations. For example, this can be clearly seen in the manufacturing area, where the integration of SAP and IoT allows companies to perform predictive maintenance by using machine learning (ML) capabilities. This helps to maintain the equipment before the actual failure occurs, greatly reducing operational downtime and cutting costs associated with standstills and equipment repairs.

Process automation

IoT with SAP integration enables the automation of processes with the help of real-time triggers, which enhances the overall efficiency of a business. For example, logistics companies can easily adjust routings on the go based on real-time information about traffic situations, eliminating potential extra fuel costs and improving delivery times.

Hyper-personalized customer experience

SAP and IoT integration enables businesses to track user behavior and offer unique, personalized customer experiences, which boosts user retention, loyalty, and average revenue per user. One of the examples here is IoT sensors installed on store shelves; these sensors send signals to SAP inventory management systems, identifying what goods need to be replenished to fulfill demand.

Smarter supply chains

SAP supply chain management systems integrated with IoT enable real-time analytics, predictive supply management, and route optimization, empowering companies to successfully adjust to changes like seasonal shifts or global trends. For instance, embedding SAP-connected IoT sensors into warehouses and trucks ensures logistics agility and increases overall efficiency.

IoT With SAP Integration Use Cases

Integration of SAP and IoT can strengthen and improve many aspects of business operations. Here are the most prominent examples:

Predictive maintenance

IoT sensors located on machines continuously send data to SAP EAM (Enterprise Asset Management). SAP EAM then processes the retrieved data using its predictive analytics capabilities and detects anomalies (too hot, slow, etc.) to anticipate equipment failure and schedule maintenance before the machine is out of order. This enables the reduction of downtime, lowers maintenance costs, and increases overall manufacturing efficiency.

Asset performance management

IoT devices continuously scan asset conditions and usage patterns to further send real-time data to SAP Business Network Asset Collaboration (BNAC) and SAP Asset Performance Management (APM). SAP systems then consolidate the acquired data, making it easy for employees in charge to evaluate asset utilization, optimize asset lifecycle costs, and track overall asset efficiency. IoT and SAP integration results in optimized asset usage, better visibility of asset condition and performance, and reduced maintenance costs.

Smart manufacturing

IoT sensors integrated directly into the manufacturing process collect real-time operational data (production rate, quality parameters, environmental conditions, etc.), sending these insights directly to SAP Digital Manufacturing (DMC) and SAP Cloud ERP. This enables SAP solutions to automatically shift production schedules, optimize resource allocation, and improve quality control. As a result, enterprises benefit from enhanced productivity, improved agility, and better product quality.

Digital twins

IoT sensors continuously capture operational data details from equipment, sending it to SAP BTP (Business Technology Platform) to create a digital twin of physical assets. In SAP BTP, these digital twins empower simulations and testing, which allows businesses to use the insights into asset behavior for proactive, data-driven decision-making. Doing so enables proactive risk management and ongoing system innovation.

Real-time inventory tracking

IoT-powered tracking devices like GPS sensors and smart shelves monitor inventory and continuously report on consumption rates and replenishment needs into SAP EWM (Extended Warehouse Management) and SAP IBP (Integrated Business Planning). With these real-time insights, you can automate inventory records updates and adjust delivery plans accordingly in your SAP system. Additionally, you can benefit from improved customer experiences due to fulfilled demands, reduction in stockouts, and better responsiveness to shifting market trends.

SAP and IoT Integration Challenges

IoT and SAP integration is a challenging process that requires careful planning and preparation. Let’s take a look at the main challenges that often happen during the process and learn how to overcome them.

High data volumes with low data relevance

A common practice for IoT devices is producing large amounts of data, while only a small part can actually bring insights and value. Keeping this raw data unsupervised can easily overload SAP systems, making decision-making processes complicated, wasting computing resources on useless background noise, reducing analytical efficiency, and increasing operational costs.

Comprehensive data validation and cleansing provided by DataLark help mitigate this issue, so that only meaningful and relevant data gets into your SAP system.

Unfiltered and too high-frequency telemetry for SAP systems

SAP solutions do their best when operating with well-structured, transactional data and batch processing, while endless streams of unfiltered, high-frequency IoT telemetry data may overwhelm the system. Leaving this problem unresolved may downgrade the performance of the SAP system, slowing real-time responsiveness and increasing risks of system instability.

Intermediary IoT platforms or edge computing solutions help prepare and accumulate data for the SAP ecosystem by reducing data frequency and ensuring better compatibility.

Latency, queueing, and processing constraints

Real-time data from IoT sensors usually requires immediate processing. Yet, SAP systems may have inherent latency, queueing mechanisms, as well as batch-processing constraints that don’t align with the demands of real-time IoT scenarios. As a result, a business may face delayed and irrelevant insights and slower response times, which can be a dealbreaker for time-sensitive processes.

Robust message queuing solutions, event-driven architectures, and edge computing techniques handle latency-sensitive processes outside SAP, providing the system with processed and timely insights.

Data compatibility issues

IoT sensors may generate raw data in various formats. This approach may cause compatibility issues, as the raw data format may not align with the SAP APIs and data requirements. This causes integration complexity, increased integration costs, high probability of data loss and corruption, and imposes overheads in data compatibility maintenance.

Sophisticated integration platforms enable businesses to automate data formatting, normalization, and mapping, to ensure data compatibility with SAP native formats and simplify the overall integration workflow.

Existing SAP and IoT Solutions

SAP offers solutions specifically designed for IoT and IIoT (Industrial Internet of Things), catering to enterprises that heavily rely on IoT-retrieved data in their operations.

SAP Integration Suite, a comprehensive cloud-based platform, is designed to connect and integrate applications, processes, data, and devices across diverse environments. SAP Integration Suite helps leverage IoT data to enhance decision-making, automate processes, and innovate operational workflows. The platform provides the infrastructure and tools required to connect and configure IoT devices and sensor networks, manage massive data streams, and apply real-time analytics and insights directly into core business processes. Besides, SAP Integration Suite allows seamless data connection from IoT devices to core SAP Cloud ERP solutions, S/4HANA and SAP Datasphere

SAP Business Network Asset Collaboration (BNAC) serves to facilitate collaboration among asset manufacturers, operators, service providers, and related entities. SAP BNAC creates a centralized platform for storing and sharing asset data, enables collaborative maintenance and real-time monitoring, supports operational information safety, and allows for digital twin creation for enhanced management and forecasting.

SAP and IoT integration solutions from Hyperscalers

In addition to SAP-native IoT integration options, businesses can choose solutions from Hyperscalers, such as AWS IoT Core and Microsoft Azure IoT Hub.

AWS IoT Core allows for an easy and safe connection between IoT devices and SAP Cloud ERP, enabling continuous data streams, streamlined management, and efficient processing of the data retrieved from IoT devices. AWS IoT Core supports robust device connectivity, message routing, and integrates seamlessly with SAP solutions, letting SAP systems analyze data in real-time and make relevant, timely decisions.

Microsoft Azure IoT Hub is a scalable cloud-hosted solution for secure management of IoT device connectivity, data ingestion, and real-time monitoring. Azure IoT Hub provides strong IoT device management capabilities, supports bidirectional communication, and easily integrates with SAP solutions for enhanced analytics, AI empowerment, and precise data processing services.

Limitations of existing SAP and IoT solutions

Both SAP Integration Suite and SAP BNAC help businesses:

\n
• Enhance operational efficiency
\n
• Introduce data-driven decision-making
\n
• Enable predictive maintenance
\n
• Cut costs associated with downtime and equipment failure
\n
• Prolong asset lifecycle through real-time monitoring, agility, and process transparency
\n

Yet, they are not perfect, and businesses that use or plan to use them should be aware of their limitations in order to plan IoT and SAP integration strategies more carefully.

High cost and complexity

SAP solutions for IoT are expensive, usually requiring tangible upfront investments in infrastructure, licenses, and qualified specialists. This often imposes high entry barriers that stop smaller businesses from using IoT’s benefits, prolong implementation timelines, and increase TCO (total cost of ownership).

Security & privacy vulnerabilities

Like all IoT ecosystems, SAP solutions for IoT are exposed to risks from insecure endpoint devices. Compromised sensors can be used to attack the SAP backend, making robust encryption, authentication, and secure device management critical.

Extensive configuration is needed for every device type

Each type of IoT device (heat sensors, GPS, smart shelves, etc.) requires profound, extensive configuration to integrate with SAP seamlessly. Various device protocols, data formats, and communication models dramatically increase integration complexity, which results in slow deployment, higher risks of integration errors or data format inconsistency, and imposes more maintenance expenses.

How DataLark Simplifies IoT and SAP Integration

DataLark, an SAP-centric data management platform, is designed specifically to streamline data integration and management in cases where standard solutions cannot cope. IoT and SAP integration is exactly that case, and disparate data formats, heavy volumes of data streams, and compatibility issues all prove this.

DataLark’s data quality management functionality helps standardize and unify IoT data before it reaches SAP, decreasing the processing burden and allowing users to get the full benefits of SAP’s predictive analytics capabilities.

DataLark performs data cleansing and validation, wiping out unnecessary or duplicate data. This ensures that irrelevant data will not fog the final result, leading to sharper forecasting and faster data processing within SAP systems.

Being a smart and secure SAP connector, DataLark can connect data from various IoT sources to SAP S/4HANA (batch production records, E-commerce and manufacturing warehouses, enterprise asset data, etc.) and facilitate data mapping with its intuitive, no-code interface. These features help provide seamless, easy IoT and SAP integration, avoiding costly IT overheads, prolonged process timelines, or data issues resolution post-integration.

Conclusion

IoT with SAP integration is a challenging yet rewarding journey that opens new opportunities for businesses’ digital transformation, market growth, and revenue generation by offering applicable data “seasoned” with real-time analytics and predictive AI capabilities.

We hope that this piece will help you decide about IoT and SAP integration if you are contemplating it, or find a solution to your integration challenges if you have already started the process.

Learn the benefits and opportunities of IoT and SAP integration for your business, navigate existing solutions, and see the pitfalls to avoid.

Learn the benefits and opportunities of IoT and SAP integration for your business, navigate existing solutions, and see the pitfalls to avoid.

IoT and SAP Integration Explained: Benefits, Challenges, and Application Areas

With data-driven approaches to literally every aspect of a business taking over the world, integrating your ERP with IoT technology is just a matter of time. Integrating IoT with SAP allows enterprises to effectively transform field data into actionable insights, benefiting the business’s agility, competitiveness, efficiency, and profitability.

In this article, we’ll dig deeper into the benefits of integrating IoT and SAP, cover the main challenges one can face during integration, list the major areas of application (spoiler: they are useful for every business), and review available IoT integration solutions.

IoT and SAP Integration Benefits

Let’s take a look at the core benefits of IoT and SAP integration for businesses. All of them allow companies to outperform their competitors and keep up with the ever-evolving business landscape.

Enhanced decision-making

Data retrieved from IoT allows businesses to enhance their decision-making process and introduce a proactive approach to operations. For example, this can be clearly seen in the manufacturing area, where the integration of SAP and IoT allows companies to perform predictive maintenance by using machine learning (ML) capabilities. This helps to maintain the equipment before the actual failure occurs, greatly reducing operational downtime and cutting costs associated with standstills and equipment repairs.

Process automation

IoT with SAP integration enables the automation of processes with the help of real-time triggers, which enhances the overall efficiency of a business. For example, logistics companies can easily adjust routings on the go based on real-time information about traffic situations, eliminating potential extra fuel costs and improving delivery times.

Hyper-personalized customer experience

SAP and IoT integration enables businesses to track user behavior and offer unique, personalized customer experiences, which boosts user retention, loyalty, and average revenue per user. One of the examples here is IoT sensors installed on store shelves; these sensors send signals to SAP inventory management systems, identifying what goods need to be replenished to fulfill demand.

Smarter supply chains

SAP supply chain management systems integrated with IoT enable real-time analytics, predictive supply management, and route optimization, empowering companies to successfully adjust to changes like seasonal shifts or global trends. For instance, embedding SAP-connected IoT sensors into warehouses and trucks ensures logistics agility and increases overall efficiency.

IoT With SAP Integration Use Cases

Integration of SAP and IoT can strengthen and improve many aspects of business operations. Here are the most prominent examples:

Predictive maintenance

IoT sensors located on machines continuously send data to SAP EAM (Enterprise Asset Management). SAP EAM then processes the retrieved data using its predictive analytics capabilities and detects anomalies (too hot, slow, etc.) to anticipate equipment failure and schedule maintenance before the machine is out of order. This enables the reduction of downtime, lowers maintenance costs, and increases overall manufacturing efficiency.

Asset performance management

IoT devices continuously scan asset conditions and usage patterns to further send real-time data to SAP Business Network Asset Collaboration (BNAC) and SAP Asset Performance Management (APM). SAP systems then consolidate the acquired data, making it easy for employees in charge to evaluate asset utilization, optimize asset lifecycle costs, and track overall asset efficiency. IoT and SAP integration results in optimized asset usage, better visibility of asset condition and performance, and reduced maintenance costs.

Smart manufacturing

IoT sensors integrated directly into the manufacturing process collect real-time operational data (production rate, quality parameters, environmental conditions, etc.), sending these insights directly to SAP Digital Manufacturing (DMC) and SAP Cloud ERP. This enables SAP solutions to automatically shift production schedules, optimize resource allocation, and improve quality control. As a result, enterprises benefit from enhanced productivity, improved agility, and better product quality.

Digital twins

IoT sensors continuously capture operational data details from equipment, sending it to SAP BTP (Business Technology Platform) to create a digital twin of physical assets. In SAP BTP, these digital twins empower simulations and testing, which allows businesses to use the insights into asset behavior for proactive, data-driven decision-making. Doing so enables proactive risk management and ongoing system innovation.

Real-time inventory tracking

IoT-powered tracking devices like GPS sensors and smart shelves monitor inventory and continuously report on consumption rates and replenishment needs into SAP EWM (Extended Warehouse Management) and SAP IBP (Integrated Business Planning). With these real-time insights, you can automate inventory records updates and adjust delivery plans accordingly in your SAP system. Additionally, you can benefit from improved customer experiences due to fulfilled demands, reduction in stockouts, and better responsiveness to shifting market trends.

SAP and IoT Integration Challenges

IoT and SAP integration is a challenging process that requires careful planning and preparation. Let’s take a look at the main challenges that often happen during the process and learn how to overcome them.

High data volumes with low data relevance

A common practice for IoT devices is producing large amounts of data, while only a small part can actually bring insights and value. Keeping this raw data unsupervised can easily overload SAP systems, making decision-making processes complicated, wasting computing resources on useless background noise, reducing analytical efficiency, and increasing operational costs.

Comprehensive data validation and cleansing provided by DataLark help mitigate this issue, so that only meaningful and relevant data gets into your SAP system.

Unfiltered and too high-frequency telemetry for SAP systems

SAP solutions do their best when operating with well-structured, transactional data and batch processing, while endless streams of unfiltered, high-frequency IoT telemetry data may overwhelm the system. Leaving this problem unresolved may downgrade the performance of the SAP system, slowing real-time responsiveness and increasing risks of system instability.

Intermediary IoT platforms or edge computing solutions help prepare and accumulate data for the SAP ecosystem by reducing data frequency and ensuring better compatibility.

Latency, queueing, and processing constraints

Real-time data from IoT sensors usually requires immediate processing. Yet, SAP systems may have inherent latency, queueing mechanisms, as well as batch-processing constraints that don’t align with the demands of real-time IoT scenarios. As a result, a business may face delayed and irrelevant insights and slower response times, which can be a dealbreaker for time-sensitive processes.

Robust message queuing solutions, event-driven architectures, and edge computing techniques handle latency-sensitive processes outside SAP, providing the system with processed and timely insights.

Data compatibility issues

IoT sensors may generate raw data in various formats. This approach may cause compatibility issues, as the raw data format may not align with the SAP APIs and data requirements. This causes integration complexity, increased integration costs, high probability of data loss and corruption, and imposes overheads in data compatibility maintenance.

Sophisticated integration platforms enable businesses to automate data formatting, normalization, and mapping, to ensure data compatibility with SAP native formats and simplify the overall integration workflow.

Existing SAP and IoT Solutions

SAP offers solutions specifically designed for IoT and IIoT (Industrial Internet of Things), catering to enterprises that heavily rely on IoT-retrieved data in their operations.

SAP Integration Suite, a comprehensive cloud-based platform, is designed to connect and integrate applications, processes, data, and devices across diverse environments. SAP Integration Suite helps leverage IoT data to enhance decision-making, automate processes, and innovate operational workflows. The platform provides the infrastructure and tools required to connect and configure IoT devices and sensor networks, manage massive data streams, and apply real-time analytics and insights directly into core business processes. Besides, SAP Integration Suite allows seamless data connection from IoT devices to core SAP Cloud ERP solutions, S/4HANA and SAP Datasphere

SAP Business Network Asset Collaboration (BNAC) serves to facilitate collaboration among asset manufacturers, operators, service providers, and related entities. SAP BNAC creates a centralized platform for storing and sharing asset data, enables collaborative maintenance and real-time monitoring, supports operational information safety, and allows for digital twin creation for enhanced management and forecasting.

SAP and IoT integration solutions from Hyperscalers

In addition to SAP-native IoT integration options, businesses can choose solutions from Hyperscalers, such as AWS IoT Core and Microsoft Azure IoT Hub.

AWS IoT Core allows for an easy and safe connection between IoT devices and SAP Cloud ERP, enabling continuous data streams, streamlined management, and efficient processing of the data retrieved from IoT devices. AWS IoT Core supports robust device connectivity, message routing, and integrates seamlessly with SAP solutions, letting SAP systems analyze data in real-time and make relevant, timely decisions.

Microsoft Azure IoT Hub is a scalable cloud-hosted solution for secure management of IoT device connectivity, data ingestion, and real-time monitoring. Azure IoT Hub provides strong IoT device management capabilities, supports bidirectional communication, and easily integrates with SAP solutions for enhanced analytics, AI empowerment, and precise data processing services.

Limitations of existing SAP and IoT solutions

Both SAP Integration Suite and SAP BNAC help businesses:

\n
• Enhance operational efficiency
\n
• Introduce data-driven decision-making
\n
• Enable predictive maintenance
\n
• Cut costs associated with downtime and equipment failure
\n
• Prolong asset lifecycle through real-time monitoring, agility, and process transparency
\n

Yet, they are not perfect, and businesses that use or plan to use them should be aware of their limitations in order to plan IoT and SAP integration strategies more carefully.

High cost and complexity

SAP solutions for IoT are expensive, usually requiring tangible upfront investments in infrastructure, licenses, and qualified specialists. This often imposes high entry barriers that stop smaller businesses from using IoT’s benefits, prolong implementation timelines, and increase TCO (total cost of ownership).

Security & privacy vulnerabilities

Like all IoT ecosystems, SAP solutions for IoT are exposed to risks from insecure endpoint devices. Compromised sensors can be used to attack the SAP backend, making robust encryption, authentication, and secure device management critical.

Extensive configuration is needed for every device type

Each type of IoT device (heat sensors, GPS, smart shelves, etc.) requires profound, extensive configuration to integrate with SAP seamlessly. Various device protocols, data formats, and communication models dramatically increase integration complexity, which results in slow deployment, higher risks of integration errors or data format inconsistency, and imposes more maintenance expenses.

How DataLark Simplifies IoT and SAP Integration

DataLark, an SAP-centric data management platform, is designed specifically to streamline data integration and management in cases where standard solutions cannot cope. IoT and SAP integration is exactly that case, and disparate data formats, heavy volumes of data streams, and compatibility issues all prove this.

DataLark’s data quality management functionality helps standardize and unify IoT data before it reaches SAP, decreasing the processing burden and allowing users to get the full benefits of SAP’s predictive analytics capabilities.

DataLark performs data cleansing and validation, wiping out unnecessary or duplicate data. This ensures that irrelevant data will not fog the final result, leading to sharper forecasting and faster data processing within SAP systems.

Being a smart and secure SAP connector, DataLark can connect data from various IoT sources to SAP S/4HANA (batch production records, E-commerce and manufacturing warehouses, enterprise asset data, etc.) and facilitate data mapping with its intuitive, no-code interface. These features help provide seamless, easy IoT and SAP integration, avoiding costly IT overheads, prolonged process timelines, or data issues resolution post-integration.

Conclusion

IoT with SAP integration is a challenging yet rewarding journey that opens new opportunities for businesses’ digital transformation, market growth, and revenue generation by offering applicable data “seasoned” with real-time analytics and predictive AI capabilities.

We hope that this piece will help you decide about IoT and SAP integration if you are contemplating it, or find a solution to your integration challenges if you have already started the process.

Learn the benefits and opportunities of IoT and SAP integration for your business, navigate existing solutions, and see the pitfalls to avoid.

Learn the benefits and opportunities of IoT and SAP integration for your business, navigate existing solutions, and see the pitfalls to avoid.

IoT and SAP Integration Explained: Benefits, Challenges, and Application Areas

With data-driven approaches to literally every aspect of a business taking over the world, integrating your ERP with IoT technology is just a matter of time. Integrating IoT with SAP allows enterprises to effectively transform field data into actionable insights, benefiting the business’s agility, competitiveness, efficiency, and profitability.

In this article, we’ll dig deeper into the benefits of integrating IoT and SAP, cover the main challenges one can face during integration, list the major areas of application (spoiler: they are useful for every business), and review available IoT integration solutions.

IoT and SAP Integration Benefits

Let’s take a look at the core benefits of IoT and SAP integration for businesses. All of them allow companies to outperform their competitors and keep up with the ever-evolving business landscape.

Enhanced decision-making

Data retrieved from IoT allows businesses to enhance their decision-making process and introduce a proactive approach to operations. For example, this can be clearly seen in the manufacturing area, where the integration of SAP and IoT allows companies to perform predictive maintenance by using machine learning (ML) capabilities. This helps to maintain the equipment before the actual failure occurs, greatly reducing operational downtime and cutting costs associated with standstills and equipment repairs.

Process automation

IoT with SAP integration enables the automation of processes with the help of real-time triggers, which enhances the overall efficiency of a business. For example, logistics companies can easily adjust routings on the go based on real-time information about traffic situations, eliminating potential extra fuel costs and improving delivery times.

Hyper-personalized customer experience

SAP and IoT integration enables businesses to track user behavior and offer unique, personalized customer experiences, which boosts user retention, loyalty, and average revenue per user. One of the examples here is IoT sensors installed on store shelves; these sensors send signals to SAP inventory management systems, identifying what goods need to be replenished to fulfill demand.

Smarter supply chains

SAP supply chain management systems integrated with IoT enable real-time analytics, predictive supply management, and route optimization, empowering companies to successfully adjust to changes like seasonal shifts or global trends. For instance, embedding SAP-connected IoT sensors into warehouses and trucks ensures logistics agility and increases overall efficiency.

IoT With SAP Integration Use Cases

Integration of SAP and IoT can strengthen and improve many aspects of business operations. Here are the most prominent examples:

Predictive maintenance

IoT sensors located on machines continuously send data to SAP EAM (Enterprise Asset Management). SAP EAM then processes the retrieved data using its predictive analytics capabilities and detects anomalies (too hot, slow, etc.) to anticipate equipment failure and schedule maintenance before the machine is out of order. This enables the reduction of downtime, lowers maintenance costs, and increases overall manufacturing efficiency.

Asset performance management

IoT devices continuously scan asset conditions and usage patterns to further send real-time data to SAP Business Network Asset Collaboration (BNAC) and SAP Asset Performance Management (APM). SAP systems then consolidate the acquired data, making it easy for employees in charge to evaluate asset utilization, optimize asset lifecycle costs, and track overall asset efficiency. IoT and SAP integration results in optimized asset usage, better visibility of asset condition and performance, and reduced maintenance costs.

Smart manufacturing

IoT sensors integrated directly into the manufacturing process collect real-time operational data (production rate, quality parameters, environmental conditions, etc.), sending these insights directly to SAP Digital Manufacturing (DMC) and SAP Cloud ERP. This enables SAP solutions to automatically shift production schedules, optimize resource allocation, and improve quality control. As a result, enterprises benefit from enhanced productivity, improved agility, and better product quality.

Digital twins

IoT sensors continuously capture operational data details from equipment, sending it to SAP BTP (Business Technology Platform) to create a digital twin of physical assets. In SAP BTP, these digital twins empower simulations and testing, which allows businesses to use the insights into asset behavior for proactive, data-driven decision-making. Doing so enables proactive risk management and ongoing system innovation.

Real-time inventory tracking

IoT-powered tracking devices like GPS sensors and smart shelves monitor inventory and continuously report on consumption rates and replenishment needs into SAP EWM (Extended Warehouse Management) and SAP IBP (Integrated Business Planning). With these real-time insights, you can automate inventory records updates and adjust delivery plans accordingly in your SAP system. Additionally, you can benefit from improved customer experiences due to fulfilled demands, reduction in stockouts, and better responsiveness to shifting market trends.

SAP and IoT Integration Challenges

IoT and SAP integration is a challenging process that requires careful planning and preparation. Let’s take a look at the main challenges that often happen during the process and learn how to overcome them.

High data volumes with low data relevance

A common practice for IoT devices is producing large amounts of data, while only a small part can actually bring insights and value. Keeping this raw data unsupervised can easily overload SAP systems, making decision-making processes complicated, wasting computing resources on useless background noise, reducing analytical efficiency, and increasing operational costs.

Comprehensive data validation and cleansing provided by DataLark help mitigate this issue, so that only meaningful and relevant data gets into your SAP system.

Unfiltered and too high-frequency telemetry for SAP systems

SAP solutions do their best when operating with well-structured, transactional data and batch processing, while endless streams of unfiltered, high-frequency IoT telemetry data may overwhelm the system. Leaving this problem unresolved may downgrade the performance of the SAP system, slowing real-time responsiveness and increasing risks of system instability.

Intermediary IoT platforms or edge computing solutions help prepare and accumulate data for the SAP ecosystem by reducing data frequency and ensuring better compatibility.

Latency, queueing, and processing constraints

Real-time data from IoT sensors usually requires immediate processing. Yet, SAP systems may have inherent latency, queueing mechanisms, as well as batch-processing constraints that don’t align with the demands of real-time IoT scenarios. As a result, a business may face delayed and irrelevant insights and slower response times, which can be a dealbreaker for time-sensitive processes.

Robust message queuing solutions, event-driven architectures, and edge computing techniques handle latency-sensitive processes outside SAP, providing the system with processed and timely insights.

Data compatibility issues

IoT sensors may generate raw data in various formats. This approach may cause compatibility issues, as the raw data format may not align with the SAP APIs and data requirements. This causes integration complexity, increased integration costs, high probability of data loss and corruption, and imposes overheads in data compatibility maintenance.

Sophisticated integration platforms enable businesses to automate data formatting, normalization, and mapping, to ensure data compatibility with SAP native formats and simplify the overall integration workflow.

Existing SAP and IoT Solutions

SAP offers solutions specifically designed for IoT and IIoT (Industrial Internet of Things), catering to enterprises that heavily rely on IoT-retrieved data in their operations.

SAP Integration Suite, a comprehensive cloud-based platform, is designed to connect and integrate applications, processes, data, and devices across diverse environments. SAP Integration Suite helps leverage IoT data to enhance decision-making, automate processes, and innovate operational workflows. The platform provides the infrastructure and tools required to connect and configure IoT devices and sensor networks, manage massive data streams, and apply real-time analytics and insights directly into core business processes. Besides, SAP Integration Suite allows seamless data connection from IoT devices to core SAP Cloud ERP solutions, S/4HANA and SAP Datasphere

SAP Business Network Asset Collaboration (BNAC) serves to facilitate collaboration among asset manufacturers, operators, service providers, and related entities. SAP BNAC creates a centralized platform for storing and sharing asset data, enables collaborative maintenance and real-time monitoring, supports operational information safety, and allows for digital twin creation for enhanced management and forecasting.

SAP and IoT integration solutions from Hyperscalers

In addition to SAP-native IoT integration options, businesses can choose solutions from Hyperscalers, such as AWS IoT Core and Microsoft Azure IoT Hub.

AWS IoT Core allows for an easy and safe connection between IoT devices and SAP Cloud ERP, enabling continuous data streams, streamlined management, and efficient processing of the data retrieved from IoT devices. AWS IoT Core supports robust device connectivity, message routing, and integrates seamlessly with SAP solutions, letting SAP systems analyze data in real-time and make relevant, timely decisions.

Microsoft Azure IoT Hub is a scalable cloud-hosted solution for secure management of IoT device connectivity, data ingestion, and real-time monitoring. Azure IoT Hub provides strong IoT device management capabilities, supports bidirectional communication, and easily integrates with SAP solutions for enhanced analytics, AI empowerment, and precise data processing services.

Limitations of existing SAP and IoT solutions

Both SAP Integration Suite and SAP BNAC help businesses:

\n
• Enhance operational efficiency
\n
• Introduce data-driven decision-making
\n
• Enable predictive maintenance
\n
• Cut costs associated with downtime and equipment failure
\n
• Prolong asset lifecycle through real-time monitoring, agility, and process transparency
\n

Yet, they are not perfect, and businesses that use or plan to use them should be aware of their limitations in order to plan IoT and SAP integration strategies more carefully.

High cost and complexity

SAP solutions for IoT are expensive, usually requiring tangible upfront investments in infrastructure, licenses, and qualified specialists. This often imposes high entry barriers that stop smaller businesses from using IoT’s benefits, prolong implementation timelines, and increase TCO (total cost of ownership).

Security & privacy vulnerabilities

Like all IoT ecosystems, SAP solutions for IoT are exposed to risks from insecure endpoint devices. Compromised sensors can be used to attack the SAP backend, making robust encryption, authentication, and secure device management critical.

Extensive configuration is needed for every device type

Each type of IoT device (heat sensors, GPS, smart shelves, etc.) requires profound, extensive configuration to integrate with SAP seamlessly. Various device protocols, data formats, and communication models dramatically increase integration complexity, which results in slow deployment, higher risks of integration errors or data format inconsistency, and imposes more maintenance expenses.

How DataLark Simplifies IoT and SAP Integration

DataLark, an SAP-centric data management platform, is designed specifically to streamline data integration and management in cases where standard solutions cannot cope. IoT and SAP integration is exactly that case, and disparate data formats, heavy volumes of data streams, and compatibility issues all prove this.

DataLark’s data quality management functionality helps standardize and unify IoT data before it reaches SAP, decreasing the processing burden and allowing users to get the full benefits of SAP’s predictive analytics capabilities.

DataLark performs data cleansing and validation, wiping out unnecessary or duplicate data. This ensures that irrelevant data will not fog the final result, leading to sharper forecasting and faster data processing within SAP systems.

Being a smart and secure SAP connector, DataLark can connect data from various IoT sources to SAP S/4HANA (batch production records, E-commerce and manufacturing warehouses, enterprise asset data, etc.) and facilitate data mapping with its intuitive, no-code interface. These features help provide seamless, easy IoT and SAP integration, avoiding costly IT overheads, prolonged process timelines, or data issues resolution post-integration.

Conclusion

IoT with SAP integration is a challenging yet rewarding journey that opens new opportunities for businesses’ digital transformation, market growth, and revenue generation by offering applicable data “seasoned” with real-time analytics and predictive AI capabilities.

We hope that this piece will help you decide about IoT and SAP integration if you are contemplating it, or find a solution to your integration challenges if you have already started the process.

Learn the benefits and opportunities of IoT and SAP integration for your business, navigate existing solutions, and see the pitfalls to avoid.

IoT and SAP Integration Explained: Benefits, Challenges, and Application Areas

With data-driven approaches to literally every aspect of a business taking over the world, integrating your ERP with IoT technology is just a matter of time. Integrating IoT with SAP allows enterprises to effectively transform field data into actionable insights, benefiting the business’s agility, competitiveness, efficiency, and profitability.

In this article, we’ll dig deeper into the benefits of integrating IoT and SAP, cover the main challenges one can face during integration, list the major areas of application (spoiler: they are useful for every business), and review available IoT integration solutions.

IoT and SAP Integration Benefits

Let’s take a look at the core benefits of IoT and SAP integration for businesses. All of them allow companies to outperform their competitors and keep up with the ever-evolving business landscape.

Enhanced decision-making

Data retrieved from IoT allows businesses to enhance their decision-making process and introduce a proactive approach to operations. For example, this can be clearly seen in the manufacturing area, where the integration of SAP and IoT allows companies to perform predictive maintenance by using machine learning (ML) capabilities. This helps to maintain the equipment before the actual failure occurs, greatly reducing operational downtime and cutting costs associated with standstills and equipment repairs.

Process automation

IoT with SAP integration enables the automation of processes with the help of real-time triggers, which enhances the overall efficiency of a business. For example, logistics companies can easily adjust routings on the go based on real-time information about traffic situations, eliminating potential extra fuel costs and improving delivery times.

Hyper-personalized customer experience

SAP and IoT integration enables businesses to track user behavior and offer unique, personalized customer experiences, which boosts user retention, loyalty, and average revenue per user. One of the examples here is IoT sensors installed on store shelves; these sensors send signals to SAP inventory management systems, identifying what goods need to be replenished to fulfill demand.

Smarter supply chains

SAP supply chain management systems integrated with IoT enable real-time analytics, predictive supply management, and route optimization, empowering companies to successfully adjust to changes like seasonal shifts or global trends. For instance, embedding SAP-connected IoT sensors into warehouses and trucks ensures logistics agility and increases overall efficiency.

IoT With SAP Integration Use Cases

Integration of SAP and IoT can strengthen and improve many aspects of business operations. Here are the most prominent examples:

Predictive maintenance

IoT sensors located on machines continuously send data to SAP EAM (Enterprise Asset Management). SAP EAM then processes the retrieved data using its predictive analytics capabilities and detects anomalies (too hot, slow, etc.) to anticipate equipment failure and schedule maintenance before the machine is out of order. This enables the reduction of downtime, lowers maintenance costs, and increases overall manufacturing efficiency.

Asset performance management

IoT devices continuously scan asset conditions and usage patterns to further send real-time data to SAP Business Network Asset Collaboration (BNAC) and SAP Asset Performance Management (APM). SAP systems then consolidate the acquired data, making it easy for employees in charge to evaluate asset utilization, optimize asset lifecycle costs, and track overall asset efficiency. IoT and SAP integration results in optimized asset usage, better visibility of asset condition and performance, and reduced maintenance costs.

Smart manufacturing

IoT sensors integrated directly into the manufacturing process collect real-time operational data (production rate, quality parameters, environmental conditions, etc.), sending these insights directly to SAP Digital Manufacturing (DMC) and SAP Cloud ERP. This enables SAP solutions to automatically shift production schedules, optimize resource allocation, and improve quality control. As a result, enterprises benefit from enhanced productivity, improved agility, and better product quality.

Digital twins

IoT sensors continuously capture operational data details from equipment, sending it to SAP BTP (Business Technology Platform) to create a digital twin of physical assets. In SAP BTP, these digital twins empower simulations and testing, which allows businesses to use the insights into asset behavior for proactive, data-driven decision-making. Doing so enables proactive risk management and ongoing system innovation.

Real-time inventory tracking

IoT-powered tracking devices like GPS sensors and smart shelves monitor inventory and continuously report on consumption rates and replenishment needs into SAP EWM (Extended Warehouse Management) and SAP IBP (Integrated Business Planning). With these real-time insights, you can automate inventory records updates and adjust delivery plans accordingly in your SAP system. Additionally, you can benefit from improved customer experiences due to fulfilled demands, reduction in stockouts, and better responsiveness to shifting market trends.

SAP and IoT Integration Challenges

IoT and SAP integration is a challenging process that requires careful planning and preparation. Let’s take a look at the main challenges that often happen during the process and learn how to overcome them.

High data volumes with low data relevance

A common practice for IoT devices is producing large amounts of data, while only a small part can actually bring insights and value. Keeping this raw data unsupervised can easily overload SAP systems, making decision-making processes complicated, wasting computing resources on useless background noise, reducing analytical efficiency, and increasing operational costs.

Comprehensive data validation and cleansing provided by DataLark help mitigate this issue, so that only meaningful and relevant data gets into your SAP system.

Unfiltered and too high-frequency telemetry for SAP systems

SAP solutions do their best when operating with well-structured, transactional data and batch processing, while endless streams of unfiltered, high-frequency IoT telemetry data may overwhelm the system. Leaving this problem unresolved may downgrade the performance of the SAP system, slowing real-time responsiveness and increasing risks of system instability.

Intermediary IoT platforms or edge computing solutions help prepare and accumulate data for the SAP ecosystem by reducing data frequency and ensuring better compatibility.

Latency, queueing, and processing constraints

Real-time data from IoT sensors usually requires immediate processing. Yet, SAP systems may have inherent latency, queueing mechanisms, as well as batch-processing constraints that don’t align with the demands of real-time IoT scenarios. As a result, a business may face delayed and irrelevant insights and slower response times, which can be a dealbreaker for time-sensitive processes.

Robust message queuing solutions, event-driven architectures, and edge computing techniques handle latency-sensitive processes outside SAP, providing the system with processed and timely insights.

Data compatibility issues

IoT sensors may generate raw data in various formats. This approach may cause compatibility issues, as the raw data format may not align with the SAP APIs and data requirements. This causes integration complexity, increased integration costs, high probability of data loss and corruption, and imposes overheads in data compatibility maintenance.

Sophisticated integration platforms enable businesses to automate data formatting, normalization, and mapping, to ensure data compatibility with SAP native formats and simplify the overall integration workflow.

Existing SAP and IoT Solutions

SAP offers solutions specifically designed for IoT and IIoT (Industrial Internet of Things), catering to enterprises that heavily rely on IoT-retrieved data in their operations.

SAP Integration Suite, a comprehensive cloud-based platform, is designed to connect and integrate applications, processes, data, and devices across diverse environments. SAP Integration Suite helps leverage IoT data to enhance decision-making, automate processes, and innovate operational workflows. The platform provides the infrastructure and tools required to connect and configure IoT devices and sensor networks, manage massive data streams, and apply real-time analytics and insights directly into core business processes. Besides, SAP Integration Suite allows seamless data connection from IoT devices to core SAP Cloud ERP solutions, S/4HANA and SAP Datasphere

SAP Business Network Asset Collaboration (BNAC) serves to facilitate collaboration among asset manufacturers, operators, service providers, and related entities. SAP BNAC creates a centralized platform for storing and sharing asset data, enables collaborative maintenance and real-time monitoring, supports operational information safety, and allows for digital twin creation for enhanced management and forecasting.

SAP and IoT integration solutions from Hyperscalers

In addition to SAP-native IoT integration options, businesses can choose solutions from Hyperscalers, such as AWS IoT Core and Microsoft Azure IoT Hub.

AWS IoT Core allows for an easy and safe connection between IoT devices and SAP Cloud ERP, enabling continuous data streams, streamlined management, and efficient processing of the data retrieved from IoT devices. AWS IoT Core supports robust device connectivity, message routing, and integrates seamlessly with SAP solutions, letting SAP systems analyze data in real-time and make relevant, timely decisions.

Microsoft Azure IoT Hub is a scalable cloud-hosted solution for secure management of IoT device connectivity, data ingestion, and real-time monitoring. Azure IoT Hub provides strong IoT device management capabilities, supports bidirectional communication, and easily integrates with SAP solutions for enhanced analytics, AI empowerment, and precise data processing services.

Limitations of existing SAP and IoT solutions

Both SAP Integration Suite and SAP BNAC help businesses:

\n
• Enhance operational efficiency
\n
• Introduce data-driven decision-making
\n
• Enable predictive maintenance
\n
• Cut costs associated with downtime and equipment failure
\n
• Prolong asset lifecycle through real-time monitoring, agility, and process transparency
\n

Yet, they are not perfect, and businesses that use or plan to use them should be aware of their limitations in order to plan IoT and SAP integration strategies more carefully.

High cost and complexity

SAP solutions for IoT are expensive, usually requiring tangible upfront investments in infrastructure, licenses, and qualified specialists. This often imposes high entry barriers that stop smaller businesses from using IoT’s benefits, prolong implementation timelines, and increase TCO (total cost of ownership).

Security & privacy vulnerabilities

Like all IoT ecosystems, SAP solutions for IoT are exposed to risks from insecure endpoint devices. Compromised sensors can be used to attack the SAP backend, making robust encryption, authentication, and secure device management critical.

Extensive configuration is needed for every device type

Each type of IoT device (heat sensors, GPS, smart shelves, etc.) requires profound, extensive configuration to integrate with SAP seamlessly. Various device protocols, data formats, and communication models dramatically increase integration complexity, which results in slow deployment, higher risks of integration errors or data format inconsistency, and imposes more maintenance expenses.

How DataLark Simplifies IoT and SAP Integration

DataLark, an SAP-centric data management platform, is designed specifically to streamline data integration and management in cases where standard solutions cannot cope. IoT and SAP integration is exactly that case, and disparate data formats, heavy volumes of data streams, and compatibility issues all prove this.

DataLark’s data quality management functionality helps standardize and unify IoT data before it reaches SAP, decreasing the processing burden and allowing users to get the full benefits of SAP’s predictive analytics capabilities.

DataLark performs data cleansing and validation, wiping out unnecessary or duplicate data. This ensures that irrelevant data will not fog the final result, leading to sharper forecasting and faster data processing within SAP systems.

Being a smart and secure SAP connector, DataLark can connect data from various IoT sources to SAP S/4HANA (batch production records, E-commerce and manufacturing warehouses, enterprise asset data, etc.) and facilitate data mapping with its intuitive, no-code interface. These features help provide seamless, easy IoT and SAP integration, avoiding costly IT overheads, prolonged process timelines, or data issues resolution post-integration.

Conclusion

IoT with SAP integration is a challenging yet rewarding journey that opens new opportunities for businesses’ digital transformation, market growth, and revenue generation by offering applicable data “seasoned” with real-time analytics and predictive AI capabilities.

We hope that this piece will help you decide about IoT and SAP integration if you are contemplating it, or find a solution to your integration challenges if you have already started the process.

Learn the benefits and opportunities of IoT and SAP integration for your business, navigate existing solutions, and see the pitfalls to avoid.

Learn the benefits and opportunities of IoT and SAP integration for your business, navigate existing solutions, and see the pitfalls to avoid.

Learn the benefits and opportunities of IoT and SAP integration for your business, navigate existing solutions, and see the pitfalls to avoid.

IoT and SAP Integration Explained: Benefits, Challenges, and Application Areas

With data-driven approaches to literally every aspect of a business taking over the world, integrating your ERP with IoT technology is just a matter of time. Integrating IoT with SAP allows enterprises to effectively transform field data into actionable insights, benefiting the business’s agility, competitiveness, efficiency, and profitability.

In this article, we’ll dig deeper into the benefits of integrating IoT and SAP, cover the main challenges one can face during integration, list the major areas of application (spoiler: they are useful for every business), and review available IoT integration solutions.

IoT and SAP Integration Benefits

Let’s take a look at the core benefits of IoT and SAP integration for businesses. All of them allow companies to outperform their competitors and keep up with the ever-evolving business landscape.

Enhanced decision-making

Data retrieved from IoT allows businesses to enhance their decision-making process and introduce a proactive approach to operations. For example, this can be clearly seen in the manufacturing area, where the integration of SAP and IoT allows companies to perform predictive maintenance by using machine learning (ML) capabilities. This helps to maintain the equipment before the actual failure occurs, greatly reducing operational downtime and cutting costs associated with standstills and equipment repairs.

Process automation

IoT with SAP integration enables the automation of processes with the help of real-time triggers, which enhances the overall efficiency of a business. For example, logistics companies can easily adjust routings on the go based on real-time information about traffic situations, eliminating potential extra fuel costs and improving delivery times.

Hyper-personalized customer experience

SAP and IoT integration enables businesses to track user behavior and offer unique, personalized customer experiences, which boosts user retention, loyalty, and average revenue per user. One of the examples here is IoT sensors installed on store shelves; these sensors send signals to SAP inventory management systems, identifying what goods need to be replenished to fulfill demand.

Smarter supply chains

SAP supply chain management systems integrated with IoT enable real-time analytics, predictive supply management, and route optimization, empowering companies to successfully adjust to changes like seasonal shifts or global trends. For instance, embedding SAP-connected IoT sensors into warehouses and trucks ensures logistics agility and increases overall efficiency.

IoT With SAP Integration Use Cases

Integration of SAP and IoT can strengthen and improve many aspects of business operations. Here are the most prominent examples:

Predictive maintenance

IoT sensors located on machines continuously send data to SAP EAM (Enterprise Asset Management). SAP EAM then processes the retrieved data using its predictive analytics capabilities and detects anomalies (too hot, slow, etc.) to anticipate equipment failure and schedule maintenance before the machine is out of order. This enables the reduction of downtime, lowers maintenance costs, and increases overall manufacturing efficiency.

Asset performance management

IoT devices continuously scan asset conditions and usage patterns to further send real-time data to SAP Business Network Asset Collaboration (BNAC) and SAP Asset Performance Management (APM). SAP systems then consolidate the acquired data, making it easy for employees in charge to evaluate asset utilization, optimize asset lifecycle costs, and track overall asset efficiency. IoT and SAP integration results in optimized asset usage, better visibility of asset condition and performance, and reduced maintenance costs.

Smart manufacturing

IoT sensors integrated directly into the manufacturing process collect real-time operational data (production rate, quality parameters, environmental conditions, etc.), sending these insights directly to SAP Digital Manufacturing (DMC) and SAP Cloud ERP. This enables SAP solutions to automatically shift production schedules, optimize resource allocation, and improve quality control. As a result, enterprises benefit from enhanced productivity, improved agility, and better product quality.

Digital twins

IoT sensors continuously capture operational data details from equipment, sending it to SAP BTP (Business Technology Platform) to create a digital twin of physical assets. In SAP BTP, these digital twins empower simulations and testing, which allows businesses to use the insights into asset behavior for proactive, data-driven decision-making. Doing so enables proactive risk management and ongoing system innovation.

Real-time inventory tracking

IoT-powered tracking devices like GPS sensors and smart shelves monitor inventory and continuously report on consumption rates and replenishment needs into SAP EWM (Extended Warehouse Management) and SAP IBP (Integrated Business Planning). With these real-time insights, you can automate inventory records updates and adjust delivery plans accordingly in your SAP system. Additionally, you can benefit from improved customer experiences due to fulfilled demands, reduction in stockouts, and better responsiveness to shifting market trends.

SAP and IoT Integration Challenges

IoT and SAP integration is a challenging process that requires careful planning and preparation. Let’s take a look at the main challenges that often happen during the process and learn how to overcome them.

High data volumes with low data relevance

A common practice for IoT devices is producing large amounts of data, while only a small part can actually bring insights and value. Keeping this raw data unsupervised can easily overload SAP systems, making decision-making processes complicated, wasting computing resources on useless background noise, reducing analytical efficiency, and increasing operational costs.

Comprehensive data validation and cleansing provided by DataLark help mitigate this issue, so that only meaningful and relevant data gets into your SAP system.

Unfiltered and too high-frequency telemetry for SAP systems

SAP solutions do their best when operating with well-structured, transactional data and batch processing, while endless streams of unfiltered, high-frequency IoT telemetry data may overwhelm the system. Leaving this problem unresolved may downgrade the performance of the SAP system, slowing real-time responsiveness and increasing risks of system instability.

Intermediary IoT platforms or edge computing solutions help prepare and accumulate data for the SAP ecosystem by reducing data frequency and ensuring better compatibility.

Latency, queueing, and processing constraints

Real-time data from IoT sensors usually requires immediate processing. Yet, SAP systems may have inherent latency, queueing mechanisms, as well as batch-processing constraints that don’t align with the demands of real-time IoT scenarios. As a result, a business may face delayed and irrelevant insights and slower response times, which can be a dealbreaker for time-sensitive processes.

Robust message queuing solutions, event-driven architectures, and edge computing techniques handle latency-sensitive processes outside SAP, providing the system with processed and timely insights.

Data compatibility issues

IoT sensors may generate raw data in various formats. This approach may cause compatibility issues, as the raw data format may not align with the SAP APIs and data requirements. This causes integration complexity, increased integration costs, high probability of data loss and corruption, and imposes overheads in data compatibility maintenance.

Sophisticated integration platforms enable businesses to automate data formatting, normalization, and mapping, to ensure data compatibility with SAP native formats and simplify the overall integration workflow.

Existing SAP and IoT Solutions

SAP offers solutions specifically designed for IoT and IIoT (Industrial Internet of Things), catering to enterprises that heavily rely on IoT-retrieved data in their operations.

SAP Integration Suite, a comprehensive cloud-based platform, is designed to connect and integrate applications, processes, data, and devices across diverse environments. SAP Integration Suite helps leverage IoT data to enhance decision-making, automate processes, and innovate operational workflows. The platform provides the infrastructure and tools required to connect and configure IoT devices and sensor networks, manage massive data streams, and apply real-time analytics and insights directly into core business processes. Besides, SAP Integration Suite allows seamless data connection from IoT devices to core SAP Cloud ERP solutions, S/4HANA and SAP Datasphere

SAP Business Network Asset Collaboration (BNAC) serves to facilitate collaboration among asset manufacturers, operators, service providers, and related entities. SAP BNAC creates a centralized platform for storing and sharing asset data, enables collaborative maintenance and real-time monitoring, supports operational information safety, and allows for digital twin creation for enhanced management and forecasting.

SAP and IoT integration solutions from Hyperscalers

In addition to SAP-native IoT integration options, businesses can choose solutions from Hyperscalers, such as AWS IoT Core and Microsoft Azure IoT Hub.

AWS IoT Core allows for an easy and safe connection between IoT devices and SAP Cloud ERP, enabling continuous data streams, streamlined management, and efficient processing of the data retrieved from IoT devices. AWS IoT Core supports robust device connectivity, message routing, and integrates seamlessly with SAP solutions, letting SAP systems analyze data in real-time and make relevant, timely decisions.

Microsoft Azure IoT Hub is a scalable cloud-hosted solution for secure management of IoT device connectivity, data ingestion, and real-time monitoring. Azure IoT Hub provides strong IoT device management capabilities, supports bidirectional communication, and easily integrates with SAP solutions for enhanced analytics, AI empowerment, and precise data processing services.

Limitations of existing SAP and IoT solutions

Both SAP Integration Suite and SAP BNAC help businesses:

\n
• Enhance operational efficiency
\n
• Introduce data-driven decision-making
\n
• Enable predictive maintenance
\n
• Cut costs associated with downtime and equipment failure
\n
• Prolong asset lifecycle through real-time monitoring, agility, and process transparency
\n

Yet, they are not perfect, and businesses that use or plan to use them should be aware of their limitations in order to plan IoT and SAP integration strategies more carefully.

High cost and complexity

SAP solutions for IoT are expensive, usually requiring tangible upfront investments in infrastructure, licenses, and qualified specialists. This often imposes high entry barriers that stop smaller businesses from using IoT’s benefits, prolong implementation timelines, and increase TCO (total cost of ownership).

Security & privacy vulnerabilities

Like all IoT ecosystems, SAP solutions for IoT are exposed to risks from insecure endpoint devices. Compromised sensors can be used to attack the SAP backend, making robust encryption, authentication, and secure device management critical.

Extensive configuration is needed for every device type

Each type of IoT device (heat sensors, GPS, smart shelves, etc.) requires profound, extensive configuration to integrate with SAP seamlessly. Various device protocols, data formats, and communication models dramatically increase integration complexity, which results in slow deployment, higher risks of integration errors or data format inconsistency, and imposes more maintenance expenses.

How DataLark Simplifies IoT and SAP Integration

DataLark, an SAP-centric data management platform, is designed specifically to streamline data integration and management in cases where standard solutions cannot cope. IoT and SAP integration is exactly that case, and disparate data formats, heavy volumes of data streams, and compatibility issues all prove this.

DataLark’s data quality management functionality helps standardize and unify IoT data before it reaches SAP, decreasing the processing burden and allowing users to get the full benefits of SAP’s predictive analytics capabilities.

DataLark performs data cleansing and validation, wiping out unnecessary or duplicate data. This ensures that irrelevant data will not fog the final result, leading to sharper forecasting and faster data processing within SAP systems.

Being a smart and secure SAP connector, DataLark can connect data from various IoT sources to SAP S/4HANA (batch production records, E-commerce and manufacturing warehouses, enterprise asset data, etc.) and facilitate data mapping with its intuitive, no-code interface. These features help provide seamless, easy IoT and SAP integration, avoiding costly IT overheads, prolonged process timelines, or data issues resolution post-integration.

Conclusion

IoT with SAP integration is a challenging yet rewarding journey that opens new opportunities for businesses’ digital transformation, market growth, and revenue generation by offering applicable data “seasoned” with real-time analytics and predictive AI capabilities.

We hope that this piece will help you decide about IoT and SAP integration if you are contemplating it, or find a solution to your integration challenges if you have already started the process.

Learn the benefits and opportunities of IoT and SAP integration for your business, navigate existing solutions, and see the pitfalls to avoid.

Learn the benefits and opportunities of IoT and SAP integration for your business, navigate existing solutions, and see the pitfalls to avoid.

Learn the benefits and opportunities of IoT and SAP integration for your business, navigate existing solutions, and see the pitfalls to avoid.

IoT and SAP Integration Explained: Benefits, Challenges, and Application Areas

With data-driven approaches to literally every aspect of a business taking over the world, integrating your ERP with IoT technology is just a matter of time. Integrating IoT with SAP allows enterprises to effectively transform field data into actionable insights, benefiting the business’s agility, competitiveness, efficiency, and profitability.

In this article, we’ll dig deeper into the benefits of integrating IoT and SAP, cover the main challenges one can face during integration, list the major areas of application (spoiler: they are useful for every business), and review available IoT integration solutions.

IoT and SAP Integration Benefits

Let’s take a look at the core benefits of IoT and SAP integration for businesses. All of them allow companies to outperform their competitors and keep up with the ever-evolving business landscape.

Enhanced decision-making

Data retrieved from IoT allows businesses to enhance their decision-making process and introduce a proactive approach to operations. For example, this can be clearly seen in the manufacturing area, where the integration of SAP and IoT allows companies to perform predictive maintenance by using machine learning (ML) capabilities. This helps to maintain the equipment before the actual failure occurs, greatly reducing operational downtime and cutting costs associated with standstills and equipment repairs.

Process automation

IoT with SAP integration enables the automation of processes with the help of real-time triggers, which enhances the overall efficiency of a business. For example, logistics companies can easily adjust routings on the go based on real-time information about traffic situations, eliminating potential extra fuel costs and improving delivery times.

Hyper-personalized customer experience

SAP and IoT integration enables businesses to track user behavior and offer unique, personalized customer experiences, which boosts user retention, loyalty, and average revenue per user. One of the examples here is IoT sensors installed on store shelves; these sensors send signals to SAP inventory management systems, identifying what goods need to be replenished to fulfill demand.

Smarter supply chains

SAP supply chain management systems integrated with IoT enable real-time analytics, predictive supply management, and route optimization, empowering companies to successfully adjust to changes like seasonal shifts or global trends. For instance, embedding SAP-connected IoT sensors into warehouses and trucks ensures logistics agility and increases overall efficiency.

IoT With SAP Integration Use Cases

Integration of SAP and IoT can strengthen and improve many aspects of business operations. Here are the most prominent examples:

Predictive maintenance

IoT sensors located on machines continuously send data to SAP EAM (Enterprise Asset Management). SAP EAM then processes the retrieved data using its predictive analytics capabilities and detects anomalies (too hot, slow, etc.) to anticipate equipment failure and schedule maintenance before the machine is out of order. This enables the reduction of downtime, lowers maintenance costs, and increases overall manufacturing efficiency.

Asset performance management

IoT devices continuously scan asset conditions and usage patterns to further send real-time data to SAP Business Network Asset Collaboration (BNAC) and SAP Asset Performance Management (APM). SAP systems then consolidate the acquired data, making it easy for employees in charge to evaluate asset utilization, optimize asset lifecycle costs, and track overall asset efficiency. IoT and SAP integration results in optimized asset usage, better visibility of asset condition and performance, and reduced maintenance costs.

Smart manufacturing

IoT sensors integrated directly into the manufacturing process collect real-time operational data (production rate, quality parameters, environmental conditions, etc.), sending these insights directly to SAP Digital Manufacturing (DMC) and SAP Cloud ERP. This enables SAP solutions to automatically shift production schedules, optimize resource allocation, and improve quality control. As a result, enterprises benefit from enhanced productivity, improved agility, and better product quality.

Digital twins

IoT sensors continuously capture operational data details from equipment, sending it to SAP BTP (Business Technology Platform) to create a digital twin of physical assets. In SAP BTP, these digital twins empower simulations and testing, which allows businesses to use the insights into asset behavior for proactive, data-driven decision-making. Doing so enables proactive risk management and ongoing system innovation.

Real-time inventory tracking

IoT-powered tracking devices like GPS sensors and smart shelves monitor inventory and continuously report on consumption rates and replenishment needs into SAP EWM (Extended Warehouse Management) and SAP IBP (Integrated Business Planning). With these real-time insights, you can automate inventory records updates and adjust delivery plans accordingly in your SAP system. Additionally, you can benefit from improved customer experiences due to fulfilled demands, reduction in stockouts, and better responsiveness to shifting market trends.

SAP and IoT Integration Challenges

IoT and SAP integration is a challenging process that requires careful planning and preparation. Let’s take a look at the main challenges that often happen during the process and learn how to overcome them.

High data volumes with low data relevance

A common practice for IoT devices is producing large amounts of data, while only a small part can actually bring insights and value. Keeping this raw data unsupervised can easily overload SAP systems, making decision-making processes complicated, wasting computing resources on useless background noise, reducing analytical efficiency, and increasing operational costs.

Comprehensive data validation and cleansing provided by DataLark help mitigate this issue, so that only meaningful and relevant data gets into your SAP system.

Unfiltered and too high-frequency telemetry for SAP systems

SAP solutions do their best when operating with well-structured, transactional data and batch processing, while endless streams of unfiltered, high-frequency IoT telemetry data may overwhelm the system. Leaving this problem unresolved may downgrade the performance of the SAP system, slowing real-time responsiveness and increasing risks of system instability.

Intermediary IoT platforms or edge computing solutions help prepare and accumulate data for the SAP ecosystem by reducing data frequency and ensuring better compatibility.

Latency, queueing, and processing constraints

Real-time data from IoT sensors usually requires immediate processing. Yet, SAP systems may have inherent latency, queueing mechanisms, as well as batch-processing constraints that don’t align with the demands of real-time IoT scenarios. As a result, a business may face delayed and irrelevant insights and slower response times, which can be a dealbreaker for time-sensitive processes.

Robust message queuing solutions, event-driven architectures, and edge computing techniques handle latency-sensitive processes outside SAP, providing the system with processed and timely insights.

Data compatibility issues

IoT sensors may generate raw data in various formats. This approach may cause compatibility issues, as the raw data format may not align with the SAP APIs and data requirements. This causes integration complexity, increased integration costs, high probability of data loss and corruption, and imposes overheads in data compatibility maintenance.

Sophisticated integration platforms enable businesses to automate data formatting, normalization, and mapping, to ensure data compatibility with SAP native formats and simplify the overall integration workflow.

Existing SAP and IoT Solutions

SAP offers solutions specifically designed for IoT and IIoT (Industrial Internet of Things), catering to enterprises that heavily rely on IoT-retrieved data in their operations.

SAP Integration Suite, a comprehensive cloud-based platform, is designed to connect and integrate applications, processes, data, and devices across diverse environments. SAP Integration Suite helps leverage IoT data to enhance decision-making, automate processes, and innovate operational workflows. The platform provides the infrastructure and tools required to connect and configure IoT devices and sensor networks, manage massive data streams, and apply real-time analytics and insights directly into core business processes. Besides, SAP Integration Suite allows seamless data connection from IoT devices to core SAP Cloud ERP solutions, S/4HANA and SAP Datasphere

SAP Business Network Asset Collaboration (BNAC) serves to facilitate collaboration among asset manufacturers, operators, service providers, and related entities. SAP BNAC creates a centralized platform for storing and sharing asset data, enables collaborative maintenance and real-time monitoring, supports operational information safety, and allows for digital twin creation for enhanced management and forecasting.

SAP and IoT integration solutions from Hyperscalers

In addition to SAP-native IoT integration options, businesses can choose solutions from Hyperscalers, such as AWS IoT Core and Microsoft Azure IoT Hub.

AWS IoT Core allows for an easy and safe connection between IoT devices and SAP Cloud ERP, enabling continuous data streams, streamlined management, and efficient processing of the data retrieved from IoT devices. AWS IoT Core supports robust device connectivity, message routing, and integrates seamlessly with SAP solutions, letting SAP systems analyze data in real-time and make relevant, timely decisions.

Microsoft Azure IoT Hub is a scalable cloud-hosted solution for secure management of IoT device connectivity, data ingestion, and real-time monitoring. Azure IoT Hub provides strong IoT device management capabilities, supports bidirectional communication, and easily integrates with SAP solutions for enhanced analytics, AI empowerment, and precise data processing services.

Limitations of existing SAP and IoT solutions

Both SAP Integration Suite and SAP BNAC help businesses:

\n
• Enhance operational efficiency
\n
• Introduce data-driven decision-making
\n
• Enable predictive maintenance
\n
• Cut costs associated with downtime and equipment failure
\n
• Prolong asset lifecycle through real-time monitoring, agility, and process transparency
\n

Yet, they are not perfect, and businesses that use or plan to use them should be aware of their limitations in order to plan IoT and SAP integration strategies more carefully.

High cost and complexity

SAP solutions for IoT are expensive, usually requiring tangible upfront investments in infrastructure, licenses, and qualified specialists. This often imposes high entry barriers that stop smaller businesses from using IoT’s benefits, prolong implementation timelines, and increase TCO (total cost of ownership).

Security & privacy vulnerabilities

Like all IoT ecosystems, SAP solutions for IoT are exposed to risks from insecure endpoint devices. Compromised sensors can be used to attack the SAP backend, making robust encryption, authentication, and secure device management critical.

Extensive configuration is needed for every device type

Each type of IoT device (heat sensors, GPS, smart shelves, etc.) requires profound, extensive configuration to integrate with SAP seamlessly. Various device protocols, data formats, and communication models dramatically increase integration complexity, which results in slow deployment, higher risks of integration errors or data format inconsistency, and imposes more maintenance expenses.

How DataLark Simplifies IoT and SAP Integration

DataLark, an SAP-centric data management platform, is designed specifically to streamline data integration and management in cases where standard solutions cannot cope. IoT and SAP integration is exactly that case, and disparate data formats, heavy volumes of data streams, and compatibility issues all prove this.

DataLark’s data quality management functionality helps standardize and unify IoT data before it reaches SAP, decreasing the processing burden and allowing users to get the full benefits of SAP’s predictive analytics capabilities.

DataLark performs data cleansing and validation, wiping out unnecessary or duplicate data. This ensures that irrelevant data will not fog the final result, leading to sharper forecasting and faster data processing within SAP systems.

Being a smart and secure SAP connector, DataLark can connect data from various IoT sources to SAP S/4HANA (batch production records, E-commerce and manufacturing warehouses, enterprise asset data, etc.) and facilitate data mapping with its intuitive, no-code interface. These features help provide seamless, easy IoT and SAP integration, avoiding costly IT overheads, prolonged process timelines, or data issues resolution post-integration.

Conclusion

IoT with SAP integration is a challenging yet rewarding journey that opens new opportunities for businesses’ digital transformation, market growth, and revenue generation by offering applicable data “seasoned” with real-time analytics and predictive AI capabilities.

We hope that this piece will help you decide about IoT and SAP integration if you are contemplating it, or find a solution to your integration challenges if you have already started the process.

Learn the benefits and opportunities of IoT and SAP integration for your business, navigate existing solutions, and see the pitfalls to avoid.

Understanding SAP Integration: Benefits, Scenarios, and Solutions

SAP is a complex, multi-component ecosystem that caters to different operational needs across various industries. It is typically used together with many third-party tools and systems to extend the existing functionality and create a unified enterprise management hub that operates as a single source of truth and relevant data. Both SAP components and third-party solutions need to be integrated into a single ecosystem to ensure uninterrupted operations and seamless data flows. This is where SAP integration plays a critical role.

In this post, we’ll delve deeper into SAP integration, so you will know how it works, understand its importance, and explore the most common SAP integration scenarios and use cases.

Why Does Your Business Need SAP Integration?

SAP integration allows businesses to unite different systems and platforms into a single ecosystem, providing a single source of trusted data and knowledge. This eventually helps streamline processes in many ways, covering the following aspects:

Standardized communication

One of the primary advantages of SAP integration is the ability to establish consistent communication standards across disparate systems. In a typical enterprise, multiple applications ranging from ERP and CRM to third-party logistics and financial platforms often use different protocols and data formats. SAP integration provides a unified framework that standardizes data structures and communication protocols (e.g., REST, OData, IDoc, RFC), enabling seamless interaction across systems. This not only reduces complexity, but it also minimizes translation errors, accelerates development, and ensures all systems speak the same language.

Real-time and asynchronous workflows

Modern business requires both speed and flexibility. SAP integration supports real-time workflows for scenarios where immediate data processing is essential, such as inventory updates, customer transactions, or financial postings. It also enables asynchronous processing for tasks that can be scheduled or queued, like batch processing or document archiving. This dual capability allows organizations to design their processes according to business needs, like balancing performance, system load, and responsiveness, while ensuring that data consistency and integrity are preserved across all systems.

Simplified system integration

Traditionally, integrating enterprise systems has been a complex and resource-intensive endeavor. SAP integration solutions, such as SAP Integration Suite, SAP Cloud Connector, and pre-built APIs, greatly reduce this complexity. These tools offer reusable components, visual interfaces for workflow orchestration, and out-of-the-box connectors for both SAP and non-SAP applications. As a result, IT teams can implement integrations faster and more reliably, while business users can access unified data without dealing with underlying technical intricacies. Simplified integration also enables agility, helping organizations adapt quickly to new requirements, partners, or technologies.

Security, governance, and compliance

Data security and compliance are non-negotiable in today’s digital environment. SAP integration frameworks come with enterprise-grade security features, including encrypted communication, secure user authentication (e.g., OAuth, SAML), role-based access control, and audit logging. These capabilities ensure that data transferred between systems remains protected against unauthorized access or tampering. Additionally, governance tools built into SAP’s integration solutions help organizations manage data lineage, monitor usage, and enforce compliance with regulations like GDPR, HIPAA, and SOX. This is especially critical when sharing data across departments, subsidiaries, or external partners.

SAP Integration Scenarios

Integration with SAP is critical for enabling fluid data exchange and process automation across a wide array of systems and environments. Below are the most common integration scenarios that organizations implement using SAP technologies:

Application-to-application (A2A)

A2A integration connects different applications within the same enterprise, ensuring consistent and synchronized data across internal systems like ERP, CRM, SCM, and HR. SAP connectors help streamline internal processes by automating data exchange between modules, reducing manual input, and improving system responsiveness.

Business-to-business (B2B)

B2B integration focuses on connecting SAP systems with external partners, suppliers, or customers. Through standardized protocols like EDI or APIs, SAP connectors enable secure and efficient exchange of documents, such as purchase orders, invoices, and shipment details, ensuring real-time collaboration across organizational boundaries.

Data integration

Data integration ensures seamless synchronization and movement of data between SAP and non-SAP systems. Whether for reporting, analytics, or real-time processing, SAP connectors support various formats and protocols to consolidate enterprise data, improve consistency, and support data-driven decision-making.

User interface integration

User interface (UI) integration allows end-users to access data and processes from multiple systems through a unified interface. These connectors use technologies like SAP Fiori, SAP Business Technology Platform (BTP), and OData-based services to enable a seamless user experience, minimizing the need to switch between applications.

Cloud integration

As enterprises adopt hybrid and multi-cloud environments, cloud integration becomes essential. SAP connectors facilitate communication between on-premise SAP systems and cloud platforms such as SAP S/4HANA Cloud, Salesforce, or Microsoft Azure. This ensures real-time data access, scalability, and continuity across deployment models.

SAP Integration Solutions

SAP offers various comprehensive solutions designed to simplify integration and turn a company’s set of applications and sub-systems into a unified ecosystem. These solutions allow for data integration between SAP and non-SAP systems, covering many common integration cases.

SAP Integration Suite (on SAP BTP)

SAP Integration Suite is a comprehensive, cloud-based integration platform-as-a-service (iPaaS) that enables the integration of on-premise and cloud-based applications and processes.

Key Features:

\n
• Pre-built integration content and APIs
\n
• Supports A2A, B2B, B2G, and event-driven integrations
\n
• Tools for API management, process integration, and data flow orchestration
\n
• Open connectors for non-SAP apps (e.g., Salesforce, Microsoft 365)
\n

SAP Integration Suite is most often used when integrating hybrid landscapes and executing cloud and multi-cloud integration.

SAP Process Orchestration (SAP PO)

SAP PO is a legacy on-premise middleware suite combining SAP Process Integration (PI), SAP Business Process Management (BPM), and SAP Business Rules Management (BRM).

Key Features:

\n
• Message routing and transformation
\n
• B2B and A2A scenarios
\n
• Support for XML, IDoc, SOAP, and more
\n

SAP PO is best suited for existing on-premise or regulated environments. SAP recommends considering migration to SAP Integration Suite for new projects.

SAP Cloud Connector

SAP Cloud Connector is a lightweight agent that provides secure connectivity between on-premise systems and the SAP Business Technology Platform (BTP).

Key Features:

\n
• Secure VPN-like tunnel to SAP BTP
\n
• Easy configuration and management
\n
• No public exposure of on-premise systems
\n

It is widely used in hybrid cloud integrations and secure backend access for cloud apps.

SAP API Management (part of Integration Suite)

Included in SAP Integration Suite, SAP API Management manages the full lifecycle of APIs – design, publishing, monitoring, and security.

Key Features:

\n
• API design and policy enforcement
\n
• Analytics and usage tracking
\n
• Developer portal for publishing APIs
\n

It enables external applications and microservices architectures to leverage SAP APIs securely and efficiently.

SAP Datasphere (formerly SAP Data Intelligence)

SAP Datasphere is SAP's modern solution for unified data integration and orchestration across heterogeneous data environments.

Key Features:

\n
• Data pipeline design and automation
\n
• AI/ML integration
\n
• Governance and metadata management
\n

Datasphere is ideal for organizations managing complex data landscapes with analytics or migration requirements.

The comparison table below will help you better understand the differences between the SAP integration solutions and decide which suits your business the most.

DataLark – Secure and Smart Solution for SAP Integration

When you need to integrate complex landscapes with large amounts of disparate data, one native SAP solution may not be enough. That’s where DataLark – a data management platform with robust architecture and feature set designed specifically for high-complexity environments – can help.

DataLark supports both batch and real-time data processing. This is critical for scenarios where some data (e.g., financial transactions) require immediate consistency, while others (e.g., reporting data) can be handled periodically. Event-driven triggers (API calls, webhooks, or change data capture) and scheduled jobs make the solution adaptable to a wide range of SAP integration needs.

The platform's modular, plugin-based connector framework supports a broad variety of systems out of the box, including SAP (S/4HANA, ECC), non-SAP ERPs, SQL/NoSQL databases, REST/SOAP APIs, and file-based sources. This reduces the need for custom development and enables faster deployment of data flows.

In addition to SAP integration, DataLark can be utilized to ensure your system’s ongoing reliability with real-time monitoring, execution logs, and alerting functionality. This is especially useful in complex workflows where latency, volume, and transformation depth can introduce failure points. Logs can be used for audit trails, and the system provides visibility into individual transaction states.

DataLark supports both on-premise and cloud-based models, which makes the solution a great choice for a wide variety of enterprises.

SAP Integration Use Cases

Now let’s take a look at some use cases where SAP integration can help businesses improve process management, efficiency, and overall performance by streamlining data flows, improving communication, and reducing costly mistakes in data interpretation. As you can see below, SAP integration is beneficial for different industries.

E-commerce

In the E-commerce area, a common occurrence is the need to integrate an E-commerce platform (Shopify, Magento (now Adobe), Joomla!, or another) and SAP S/4HANA to manage inventory, pricing, and order fulfillment efficiently with real-time synchronization.

Integration scope may look like this:

\n
• Real-time inventory updates from SAP to the storefront
\n
• Automatic order creation in SAP upon online purchase
\n
• Synchronization of customer and product data
\n

As a result of successful integration, a company may achieve:

\n
• Reduction in order processing time due to the elimination of manual entries
\n
• Real-time stock visibility across all sales channels
\n
• Significant drop in out-of-stock incidents, which improves customer satisfaction
\n

Manufacturing

Manufacturing businesses often struggle to optimize production routings. Integrating 3DX PLM to SAP S/4HANA is one of the most reliable ways to automate routing and BOM synchronization.

Integration scope in this scenario might be the following:

\n
• Synchronizing materials, routings, operations, and component assignments
\n
• Embedding inspection and quality control characteristics
\n

Integration of SAP and 3DX PLM results in:

\n
• Reduction in manual routing data entry
\n
• Increase in data accuracy across engineering and production systems
\n
• Faster production planning cycles due to real-time data availability
\n

Finance

Global enterprises may need to integrate external finance systems (QuickBooks, NetSuite) for local transactions with SAP S/4HANA, which is used for core accounting to consolidate reporting.

To achieve the desired result, the scope of the project may look like this:

\n
• Automated financial data aggregation for monthly close
\n
• Real-time currency conversion and compliance validation
\n
• Standardized chart of accounts mapping across regions
\n

Integrating SAP with local financial systems helps enterprises get:

\n
• Faster financial close cycle
\n
• Improved regulatory compliance through centralized audit trails
\n
• Consistent financial reporting across global entitiesy
\n

Healthcare

Healthcare institutions like hospitals may need to integrate their Electronic Medical Records (EMR) systems with SAP MM and SD modules to automate medical supply procurement and tracking.

The integration scope in this case is usually like this:

\n
• Triggering supply requisitions from EMR treatment plans
\n
• Real-time updates of inventory and usage logs
\n
• Linking billing and insurance data to material movements
\n

Successful SAP integration improves hospitals’ supply chain management with:

\n
• Reduction in supply overstock
\n
• Faster turnaround for critical inventory replenishment
\n
• Streamlined billing accuracy, reducing patient disputes
\n

Utilities

Utility companies, especially those managing electricity infrastructure, may need real-time integration between their SCADA (Supervisory Control and Data Acquisition) systems and SAP PM to improve asset maintenance scheduling.

The integration scope of connecting SCADA to SAP PM may look like this:

\n
• Automated work order generation based on SCADA alerts
\n
• Real-time asset status updates in SAP
\n
• Integration of maintenance logs with historical performance data
\n

As a result of such integration, companies may get:

\n
• A decrease in unplanned outages due to predictive maintenance
\n
• Increased asset uptime and reliability
\n
• More efficient field technician scheduling, which improves SLA compliance
\n

Conclusion

SAP integration is a complex yet rewarding process that allows your business to greatly improve data quality, streamline data flows, adjust communication, boost productivity and scalability, enhance customer experience, and drive innovation. And businesses need professional data integration solutions to handle everything properly.

Here’s where DataLark comes in – and this solution goes beyond the integration process itself. With DataLark, you can speed up the process and ensure everything goes error-free while monitoring and managing your data to ensure uninterrupted processes and flawless operations. Contact us today to future-proof your enterprise with a dedicated solution for data integration and management.

Get a better understanding of SAP integration, take a closer look at its benefits, and discover solutions to successfully integrate your systems with SAP.

Get a better understanding of SAP integration, take a closer look at its benefits, and discover solutions to successfully integrate your systems with SAP.

Understanding SAP Integration: Benefits, Scenarios, and Solutions

SAP is a complex, multi-component ecosystem that caters to different operational needs across various industries. It is typically used together with many third-party tools and systems to extend the existing functionality and create a unified enterprise management hub that operates as a single source of truth and relevant data. Both SAP components and third-party solutions need to be integrated into a single ecosystem to ensure uninterrupted operations and seamless data flows. This is where SAP integration plays a critical role.

In this post, we’ll delve deeper into SAP integration, so you will know how it works, understand its importance, and explore the most common SAP integration scenarios and use cases.

Why Does Your Business Need SAP Integration?

SAP integration allows businesses to unite different systems and platforms into a single ecosystem, providing a single source of trusted data and knowledge. This eventually helps streamline processes in many ways, covering the following aspects:

Standardized communication

One of the primary advantages of SAP integration is the ability to establish consistent communication standards across disparate systems. In a typical enterprise, multiple applications ranging from ERP and CRM to third-party logistics and financial platforms often use different protocols and data formats. SAP integration provides a unified framework that standardizes data structures and communication protocols (e.g., REST, OData, IDoc, RFC), enabling seamless interaction across systems. This not only reduces complexity, but it also minimizes translation errors, accelerates development, and ensures all systems speak the same language.

Real-time and asynchronous workflows

Modern business requires both speed and flexibility. SAP integration supports real-time workflows for scenarios where immediate data processing is essential, such as inventory updates, customer transactions, or financial postings. It also enables asynchronous processing for tasks that can be scheduled or queued, like batch processing or document archiving. This dual capability allows organizations to design their processes according to business needs, like balancing performance, system load, and responsiveness, while ensuring that data consistency and integrity are preserved across all systems.

Simplified system integration

Traditionally, integrating enterprise systems has been a complex and resource-intensive endeavor. SAP integration solutions, such as SAP Integration Suite, SAP Cloud Connector, and pre-built APIs, greatly reduce this complexity. These tools offer reusable components, visual interfaces for workflow orchestration, and out-of-the-box connectors for both SAP and non-SAP applications. As a result, IT teams can implement integrations faster and more reliably, while business users can access unified data without dealing with underlying technical intricacies. Simplified integration also enables agility, helping organizations adapt quickly to new requirements, partners, or technologies.

Security, governance, and compliance

Data security and compliance are non-negotiable in today’s digital environment. SAP integration frameworks come with enterprise-grade security features, including encrypted communication, secure user authentication (e.g., OAuth, SAML), role-based access control, and audit logging. These capabilities ensure that data transferred between systems remains protected against unauthorized access or tampering. Additionally, governance tools built into SAP’s integration solutions help organizations manage data lineage, monitor usage, and enforce compliance with regulations like GDPR, HIPAA, and SOX. This is especially critical when sharing data across departments, subsidiaries, or external partners.

SAP Integration Scenarios

Integration with SAP is critical for enabling fluid data exchange and process automation across a wide array of systems and environments. Below are the most common integration scenarios that organizations implement using SAP technologies:

Application-to-application (A2A)

A2A integration connects different applications within the same enterprise, ensuring consistent and synchronized data across internal systems like ERP, CRM, SCM, and HR. SAP connectors help streamline internal processes by automating data exchange between modules, reducing manual input, and improving system responsiveness.

Business-to-business (B2B)

B2B integration focuses on connecting SAP systems with external partners, suppliers, or customers. Through standardized protocols like EDI or APIs, SAP connectors enable secure and efficient exchange of documents, such as purchase orders, invoices, and shipment details, ensuring real-time collaboration across organizational boundaries.

Data integration

Data integration ensures seamless synchronization and movement of data between SAP and non-SAP systems. Whether for reporting, analytics, or real-time processing, SAP connectors support various formats and protocols to consolidate enterprise data, improve consistency, and support data-driven decision-making.

User interface integration

User interface (UI) integration allows end-users to access data and processes from multiple systems through a unified interface. These connectors use technologies like SAP Fiori, SAP Business Technology Platform (BTP), and OData-based services to enable a seamless user experience, minimizing the need to switch between applications.

Cloud integration

As enterprises adopt hybrid and multi-cloud environments, cloud integration becomes essential. SAP connectors facilitate communication between on-premise SAP systems and cloud platforms such as SAP S/4HANA Cloud, Salesforce, or Microsoft Azure. This ensures real-time data access, scalability, and continuity across deployment models.

SAP Integration Solutions

SAP offers various comprehensive solutions designed to simplify integration and turn a company’s set of applications and sub-systems into a unified ecosystem. These solutions allow for data integration between SAP and non-SAP systems, covering many common integration cases.

SAP Integration Suite (on SAP BTP)

SAP Integration Suite is a comprehensive, cloud-based integration platform-as-a-service (iPaaS) that enables the integration of on-premise and cloud-based applications and processes.

Key Features:

\n
• Pre-built integration content and APIs
\n
• Supports A2A, B2B, B2G, and event-driven integrations
\n
• Tools for API management, process integration, and data flow orchestration
\n
• Open connectors for non-SAP apps (e.g., Salesforce, Microsoft 365)
\n

SAP Integration Suite is most often used when integrating hybrid landscapes and executing cloud and multi-cloud integration.

SAP Process Orchestration (SAP PO)

SAP PO is a legacy on-premise middleware suite combining SAP Process Integration (PI), SAP Business Process Management (BPM), and SAP Business Rules Management (BRM).

Key Features:

\n
• Message routing and transformation
\n
• B2B and A2A scenarios
\n
• Support for XML, IDoc, SOAP, and more
\n

SAP PO is best suited for existing on-premise or regulated environments. SAP recommends considering migration to SAP Integration Suite for new projects.

SAP Cloud Connector

SAP Cloud Connector is a lightweight agent that provides secure connectivity between on-premise systems and the SAP Business Technology Platform (BTP).

Key Features:

\n
• Secure VPN-like tunnel to SAP BTP
\n
• Easy configuration and management
\n
• No public exposure of on-premise systems
\n

It is widely used in hybrid cloud integrations and secure backend access for cloud apps.

SAP API Management (part of Integration Suite)

Included in SAP Integration Suite, SAP API Management manages the full lifecycle of APIs – design, publishing, monitoring, and security.

Key Features:

\n
• API design and policy enforcement
\n
• Analytics and usage tracking
\n
• Developer portal for publishing APIs
\n

It enables external applications and microservices architectures to leverage SAP APIs securely and efficiently.

SAP Datasphere (formerly SAP Data Intelligence)

SAP Datasphere is SAP's modern solution for unified data integration and orchestration across heterogeneous data environments.

Key Features:

\n
• Data pipeline design and automation
\n
• AI/ML integration
\n
• Governance and metadata management
\n

Datasphere is ideal for organizations managing complex data landscapes with analytics or migration requirements.

The comparison table below will help you better understand the differences between the SAP integration solutions and decide which suits your business the most.

DataLark – Secure and Smart Solution for SAP Integration

When you need to integrate complex landscapes with large amounts of disparate data, one native SAP solution may not be enough. That’s where DataLark – a data management platform with robust architecture and feature set designed specifically for high-complexity environments – can help.

DataLark supports both batch and real-time data processing. This is critical for scenarios where some data (e.g., financial transactions) require immediate consistency, while others (e.g., reporting data) can be handled periodically. Event-driven triggers (API calls, webhooks, or change data capture) and scheduled jobs make the solution adaptable to a wide range of SAP integration needs.

The platform's modular, plugin-based connector framework supports a broad variety of systems out of the box, including SAP (S/4HANA, ECC), non-SAP ERPs, SQL/NoSQL databases, REST/SOAP APIs, and file-based sources. This reduces the need for custom development and enables faster deployment of data flows.

In addition to SAP integration, DataLark can be utilized to ensure your system’s ongoing reliability with real-time monitoring, execution logs, and alerting functionality. This is especially useful in complex workflows where latency, volume, and transformation depth can introduce failure points. Logs can be used for audit trails, and the system provides visibility into individual transaction states.

DataLark supports both on-premise and cloud-based models, which makes the solution a great choice for a wide variety of enterprises.

SAP Integration Use Cases

Now let’s take a look at some use cases where SAP integration can help businesses improve process management, efficiency, and overall performance by streamlining data flows, improving communication, and reducing costly mistakes in data interpretation. As you can see below, SAP integration is beneficial for different industries.

E-commerce

In the E-commerce area, a common occurrence is the need to integrate an E-commerce platform (Shopify, Magento (now Adobe), Joomla!, or another) and SAP S/4HANA to manage inventory, pricing, and order fulfillment efficiently with real-time synchronization.

Integration scope may look like this:

\n
• Real-time inventory updates from SAP to the storefront
\n
• Automatic order creation in SAP upon online purchase
\n
• Synchronization of customer and product data
\n

As a result of successful integration, a company may achieve:

\n
• Reduction in order processing time due to the elimination of manual entries
\n
• Real-time stock visibility across all sales channels
\n
• Significant drop in out-of-stock incidents, which improves customer satisfaction
\n

Manufacturing

Manufacturing businesses often struggle to optimize production routings. Integrating 3DX PLM to SAP S/4HANA is one of the most reliable ways to automate routing and BOM synchronization.

Integration scope in this scenario might be the following:

\n
• Synchronizing materials, routings, operations, and component assignments
\n
• Embedding inspection and quality control characteristics
\n

Integration of SAP and 3DX PLM results in:

\n
• Reduction in manual routing data entry
\n
• Increase in data accuracy across engineering and production systems
\n
• Faster production planning cycles due to real-time data availability
\n

Finance

Global enterprises may need to integrate external finance systems (QuickBooks, NetSuite) for local transactions with SAP S/4HANA, which is used for core accounting to consolidate reporting.

To achieve the desired result, the scope of the project may look like this:

\n
• Automated financial data aggregation for monthly close
\n
• Real-time currency conversion and compliance validation
\n
• Standardized chart of accounts mapping across regions
\n

Integrating SAP with local financial systems helps enterprises get:

\n
• Faster financial close cycle
\n
• Improved regulatory compliance through centralized audit trails
\n
• Consistent financial reporting across global entitiesy
\n

Healthcare

Healthcare institutions like hospitals may need to integrate their Electronic Medical Records (EMR) systems with SAP MM and SD modules to automate medical supply procurement and tracking.

The integration scope in this case is usually like this:

\n
• Triggering supply requisitions from EMR treatment plans
\n
• Real-time updates of inventory and usage logs
\n
• Linking billing and insurance data to material movements
\n

Successful SAP integration improves hospitals’ supply chain management with:

\n
• Reduction in supply overstock
\n
• Faster turnaround for critical inventory replenishment
\n
• Streamlined billing accuracy, reducing patient disputes
\n

Utilities

Utility companies, especially those managing electricity infrastructure, may need real-time integration between their SCADA (Supervisory Control and Data Acquisition) systems and SAP PM to improve asset maintenance scheduling.

The integration scope of connecting SCADA to SAP PM may look like this:

\n
• Automated work order generation based on SCADA alerts
\n
• Real-time asset status updates in SAP
\n
• Integration of maintenance logs with historical performance data
\n

As a result of such integration, companies may get:

\n
• A decrease in unplanned outages due to predictive maintenance
\n
• Increased asset uptime and reliability
\n
• More efficient field technician scheduling, which improves SLA compliance
\n

Conclusion

SAP integration is a complex yet rewarding process that allows your business to greatly improve data quality, streamline data flows, adjust communication, boost productivity and scalability, enhance customer experience, and drive innovation. And businesses need professional data integration solutions to handle everything properly.

Here’s where DataLark comes in – and this solution goes beyond the integration process itself. With DataLark, you can speed up the process and ensure everything goes error-free while monitoring and managing your data to ensure uninterrupted processes and flawless operations. Contact us today to future-proof your enterprise with a dedicated solution for data integration and management.

Get a better understanding of SAP integration, take a closer look at its benefits, and discover solutions to successfully integrate your systems with SAP.

Get a better understanding of SAP integration, take a closer look at its benefits, and discover solutions to successfully integrate your systems with SAP.

Understanding SAP Integration: Benefits, Scenarios, and Solutions

SAP is a complex, multi-component ecosystem that caters to different operational needs across various industries. It is typically used together with many third-party tools and systems to extend the existing functionality and create a unified enterprise management hub that operates as a single source of truth and relevant data. Both SAP components and third-party solutions need to be integrated into a single ecosystem to ensure uninterrupted operations and seamless data flows. This is where SAP integration plays a critical role.

In this post, we’ll delve deeper into SAP integration, so you will know how it works, understand its importance, and explore the most common SAP integration scenarios and use cases.

Why Does Your Business Need SAP Integration?

SAP integration allows businesses to unite different systems and platforms into a single ecosystem, providing a single source of trusted data and knowledge. This eventually helps streamline processes in many ways, covering the following aspects:

Standardized communication

One of the primary advantages of SAP integration is the ability to establish consistent communication standards across disparate systems. In a typical enterprise, multiple applications ranging from ERP and CRM to third-party logistics and financial platforms often use different protocols and data formats. SAP integration provides a unified framework that standardizes data structures and communication protocols (e.g., REST, OData, IDoc, RFC), enabling seamless interaction across systems. This not only reduces complexity, but it also minimizes translation errors, accelerates development, and ensures all systems speak the same language.

Real-time and asynchronous workflows

Modern business requires both speed and flexibility. SAP integration supports real-time workflows for scenarios where immediate data processing is essential, such as inventory updates, customer transactions, or financial postings. It also enables asynchronous processing for tasks that can be scheduled or queued, like batch processing or document archiving. This dual capability allows organizations to design their processes according to business needs, like balancing performance, system load, and responsiveness, while ensuring that data consistency and integrity are preserved across all systems.

Simplified system integration

Traditionally, integrating enterprise systems has been a complex and resource-intensive endeavor. SAP integration solutions, such as SAP Integration Suite, SAP Cloud Connector, and pre-built APIs, greatly reduce this complexity. These tools offer reusable components, visual interfaces for workflow orchestration, and out-of-the-box connectors for both SAP and non-SAP applications. As a result, IT teams can implement integrations faster and more reliably, while business users can access unified data without dealing with underlying technical intricacies. Simplified integration also enables agility, helping organizations adapt quickly to new requirements, partners, or technologies.

Security, governance, and compliance

Data security and compliance are non-negotiable in today’s digital environment. SAP integration frameworks come with enterprise-grade security features, including encrypted communication, secure user authentication (e.g., OAuth, SAML), role-based access control, and audit logging. These capabilities ensure that data transferred between systems remains protected against unauthorized access or tampering. Additionally, governance tools built into SAP’s integration solutions help organizations manage data lineage, monitor usage, and enforce compliance with regulations like GDPR, HIPAA, and SOX. This is especially critical when sharing data across departments, subsidiaries, or external partners.

SAP Integration Scenarios

Integration with SAP is critical for enabling fluid data exchange and process automation across a wide array of systems and environments. Below are the most common integration scenarios that organizations implement using SAP technologies:

Application-to-application (A2A)

A2A integration connects different applications within the same enterprise, ensuring consistent and synchronized data across internal systems like ERP, CRM, SCM, and HR. SAP connectors help streamline internal processes by automating data exchange between modules, reducing manual input, and improving system responsiveness.

Business-to-business (B2B)

B2B integration focuses on connecting SAP systems with external partners, suppliers, or customers. Through standardized protocols like EDI or APIs, SAP connectors enable secure and efficient exchange of documents, such as purchase orders, invoices, and shipment details, ensuring real-time collaboration across organizational boundaries.

Data integration

Data integration ensures seamless synchronization and movement of data between SAP and non-SAP systems. Whether for reporting, analytics, or real-time processing, SAP connectors support various formats and protocols to consolidate enterprise data, improve consistency, and support data-driven decision-making.

User interface integration

User interface (UI) integration allows end-users to access data and processes from multiple systems through a unified interface. These connectors use technologies like SAP Fiori, SAP Business Technology Platform (BTP), and OData-based services to enable a seamless user experience, minimizing the need to switch between applications.

Cloud integration

As enterprises adopt hybrid and multi-cloud environments, cloud integration becomes essential. SAP connectors facilitate communication between on-premise SAP systems and cloud platforms such as SAP S/4HANA Cloud, Salesforce, or Microsoft Azure. This ensures real-time data access, scalability, and continuity across deployment models.

SAP Integration Solutions

SAP offers various comprehensive solutions designed to simplify integration and turn a company’s set of applications and sub-systems into a unified ecosystem. These solutions allow for data integration between SAP and non-SAP systems, covering many common integration cases.

SAP Integration Suite (on SAP BTP)

SAP Integration Suite is a comprehensive, cloud-based integration platform-as-a-service (iPaaS) that enables the integration of on-premise and cloud-based applications and processes.

Key Features:

\n
• Pre-built integration content and APIs
\n
• Supports A2A, B2B, B2G, and event-driven integrations
\n
• Tools for API management, process integration, and data flow orchestration
\n
• Open connectors for non-SAP apps (e.g., Salesforce, Microsoft 365)
\n

SAP Integration Suite is most often used when integrating hybrid landscapes and executing cloud and multi-cloud integration.

SAP Process Orchestration (SAP PO)

SAP PO is a legacy on-premise middleware suite combining SAP Process Integration (PI), SAP Business Process Management (BPM), and SAP Business Rules Management (BRM).

Key Features:

\n
• Message routing and transformation
\n
• B2B and A2A scenarios
\n
• Support for XML, IDoc, SOAP, and more
\n

SAP PO is best suited for existing on-premise or regulated environments. SAP recommends considering migration to SAP Integration Suite for new projects.

SAP Cloud Connector

SAP Cloud Connector is a lightweight agent that provides secure connectivity between on-premise systems and the SAP Business Technology Platform (BTP).

Key Features:

\n
• Secure VPN-like tunnel to SAP BTP
\n
• Easy configuration and management
\n
• No public exposure of on-premise systems
\n

It is widely used in hybrid cloud integrations and secure backend access for cloud apps.

SAP API Management (part of Integration Suite)

Included in SAP Integration Suite, SAP API Management manages the full lifecycle of APIs – design, publishing, monitoring, and security.

Key Features:

\n
• API design and policy enforcement
\n
• Analytics and usage tracking
\n
• Developer portal for publishing APIs
\n

It enables external applications and microservices architectures to leverage SAP APIs securely and efficiently.

SAP Datasphere (formerly SAP Data Intelligence)

SAP Datasphere is SAP's modern solution for unified data integration and orchestration across heterogeneous data environments.

Key Features:

\n
• Data pipeline design and automation
\n
• AI/ML integration
\n
• Governance and metadata management
\n

Datasphere is ideal for organizations managing complex data landscapes with analytics or migration requirements.

The comparison table below will help you better understand the differences between the SAP integration solutions and decide which suits your business the most.

DataLark – Secure and Smart Solution for SAP Integration

When you need to integrate complex landscapes with large amounts of disparate data, one native SAP solution may not be enough. That’s where DataLark – a data management platform with robust architecture and feature set designed specifically for high-complexity environments – can help.

DataLark supports both batch and real-time data processing. This is critical for scenarios where some data (e.g., financial transactions) require immediate consistency, while others (e.g., reporting data) can be handled periodically. Event-driven triggers (API calls, webhooks, or change data capture) and scheduled jobs make the solution adaptable to a wide range of SAP integration needs.

The platform's modular, plugin-based connector framework supports a broad variety of systems out of the box, including SAP (S/4HANA, ECC), non-SAP ERPs, SQL/NoSQL databases, REST/SOAP APIs, and file-based sources. This reduces the need for custom development and enables faster deployment of data flows.

In addition to SAP integration, DataLark can be utilized to ensure your system’s ongoing reliability with real-time monitoring, execution logs, and alerting functionality. This is especially useful in complex workflows where latency, volume, and transformation depth can introduce failure points. Logs can be used for audit trails, and the system provides visibility into individual transaction states.

DataLark supports both on-premise and cloud-based models, which makes the solution a great choice for a wide variety of enterprises.

SAP Integration Use Cases

Now let’s take a look at some use cases where SAP integration can help businesses improve process management, efficiency, and overall performance by streamlining data flows, improving communication, and reducing costly mistakes in data interpretation. As you can see below, SAP integration is beneficial for different industries.

E-commerce

In the E-commerce area, a common occurrence is the need to integrate an E-commerce platform (Shopify, Magento (now Adobe), Joomla!, or another) and SAP S/4HANA to manage inventory, pricing, and order fulfillment efficiently with real-time synchronization.

Integration scope may look like this:

\n
• Real-time inventory updates from SAP to the storefront
\n
• Automatic order creation in SAP upon online purchase
\n
• Synchronization of customer and product data
\n

As a result of successful integration, a company may achieve:

\n
• Reduction in order processing time due to the elimination of manual entries
\n
• Real-time stock visibility across all sales channels
\n
• Significant drop in out-of-stock incidents, which improves customer satisfaction
\n

Manufacturing

Manufacturing businesses often struggle to optimize production routings. Integrating 3DX PLM to SAP S/4HANA is one of the most reliable ways to automate routing and BOM synchronization.

Integration scope in this scenario might be the following:

\n
• Synchronizing materials, routings, operations, and component assignments
\n
• Embedding inspection and quality control characteristics
\n

Integration of SAP and 3DX PLM results in:

\n
• Reduction in manual routing data entry
\n
• Increase in data accuracy across engineering and production systems
\n
• Faster production planning cycles due to real-time data availability
\n

Finance

Global enterprises may need to integrate external finance systems (QuickBooks, NetSuite) for local transactions with SAP S/4HANA, which is used for core accounting to consolidate reporting.

To achieve the desired result, the scope of the project may look like this:

\n
• Automated financial data aggregation for monthly close
\n
• Real-time currency conversion and compliance validation
\n
• Standardized chart of accounts mapping across regions
\n

Integrating SAP with local financial systems helps enterprises get:

\n
• Faster financial close cycle
\n
• Improved regulatory compliance through centralized audit trails
\n
• Consistent financial reporting across global entitiesy
\n

Healthcare

Healthcare institutions like hospitals may need to integrate their Electronic Medical Records (EMR) systems with SAP MM and SD modules to automate medical supply procurement and tracking.

The integration scope in this case is usually like this:

\n
• Triggering supply requisitions from EMR treatment plans
\n
• Real-time updates of inventory and usage logs
\n
• Linking billing and insurance data to material movements
\n

Successful SAP integration improves hospitals’ supply chain management with:

\n
• Reduction in supply overstock
\n
• Faster turnaround for critical inventory replenishment
\n
• Streamlined billing accuracy, reducing patient disputes
\n

Utilities

Utility companies, especially those managing electricity infrastructure, may need real-time integration between their SCADA (Supervisory Control and Data Acquisition) systems and SAP PM to improve asset maintenance scheduling.

The integration scope of connecting SCADA to SAP PM may look like this:

\n
• Automated work order generation based on SCADA alerts
\n
• Real-time asset status updates in SAP
\n
• Integration of maintenance logs with historical performance data
\n

As a result of such integration, companies may get:

\n
• A decrease in unplanned outages due to predictive maintenance
\n
• Increased asset uptime and reliability
\n
• More efficient field technician scheduling, which improves SLA compliance
\n

Conclusion

SAP integration is a complex yet rewarding process that allows your business to greatly improve data quality, streamline data flows, adjust communication, boost productivity and scalability, enhance customer experience, and drive innovation. And businesses need professional data integration solutions to handle everything properly.

Here’s where DataLark comes in – and this solution goes beyond the integration process itself. With DataLark, you can speed up the process and ensure everything goes error-free while monitoring and managing your data to ensure uninterrupted processes and flawless operations. Contact us today to future-proof your enterprise with a dedicated solution for data integration and management.

Get a better understanding of SAP integration, take a closer look at its benefits, and discover solutions to successfully integrate your systems with SAP.

Understanding SAP Integration: Benefits, Scenarios, and Solutions

SAP is a complex, multi-component ecosystem that caters to different operational needs across various industries. It is typically used together with many third-party tools and systems to extend the existing functionality and create a unified enterprise management hub that operates as a single source of truth and relevant data. Both SAP components and third-party solutions need to be integrated into a single ecosystem to ensure uninterrupted operations and seamless data flows. This is where SAP integration plays a critical role.

In this post, we’ll delve deeper into SAP integration, so you will know how it works, understand its importance, and explore the most common SAP integration scenarios and use cases.

Why Does Your Business Need SAP Integration?

SAP integration allows businesses to unite different systems and platforms into a single ecosystem, providing a single source of trusted data and knowledge. This eventually helps streamline processes in many ways, covering the following aspects:

Standardized communication

One of the primary advantages of SAP integration is the ability to establish consistent communication standards across disparate systems. In a typical enterprise, multiple applications ranging from ERP and CRM to third-party logistics and financial platforms often use different protocols and data formats. SAP integration provides a unified framework that standardizes data structures and communication protocols (e.g., REST, OData, IDoc, RFC), enabling seamless interaction across systems. This not only reduces complexity, but it also minimizes translation errors, accelerates development, and ensures all systems speak the same language.

Real-time and asynchronous workflows

Modern business requires both speed and flexibility. SAP integration supports real-time workflows for scenarios where immediate data processing is essential, such as inventory updates, customer transactions, or financial postings. It also enables asynchronous processing for tasks that can be scheduled or queued, like batch processing or document archiving. This dual capability allows organizations to design their processes according to business needs, like balancing performance, system load, and responsiveness, while ensuring that data consistency and integrity are preserved across all systems.

Simplified system integration

Traditionally, integrating enterprise systems has been a complex and resource-intensive endeavor. SAP integration solutions, such as SAP Integration Suite, SAP Cloud Connector, and pre-built APIs, greatly reduce this complexity. These tools offer reusable components, visual interfaces for workflow orchestration, and out-of-the-box connectors for both SAP and non-SAP applications. As a result, IT teams can implement integrations faster and more reliably, while business users can access unified data without dealing with underlying technical intricacies. Simplified integration also enables agility, helping organizations adapt quickly to new requirements, partners, or technologies.

Security, governance, and compliance

Data security and compliance are non-negotiable in today’s digital environment. SAP integration frameworks come with enterprise-grade security features, including encrypted communication, secure user authentication (e.g., OAuth, SAML), role-based access control, and audit logging. These capabilities ensure that data transferred between systems remains protected against unauthorized access or tampering. Additionally, governance tools built into SAP’s integration solutions help organizations manage data lineage, monitor usage, and enforce compliance with regulations like GDPR, HIPAA, and SOX. This is especially critical when sharing data across departments, subsidiaries, or external partners.

SAP Integration Scenarios

Integration with SAP is critical for enabling fluid data exchange and process automation across a wide array of systems and environments. Below are the most common integration scenarios that organizations implement using SAP technologies:

Application-to-application (A2A)

A2A integration connects different applications within the same enterprise, ensuring consistent and synchronized data across internal systems like ERP, CRM, SCM, and HR. SAP connectors help streamline internal processes by automating data exchange between modules, reducing manual input, and improving system responsiveness.

Business-to-business (B2B)

B2B integration focuses on connecting SAP systems with external partners, suppliers, or customers. Through standardized protocols like EDI or APIs, SAP connectors enable secure and efficient exchange of documents, such as purchase orders, invoices, and shipment details, ensuring real-time collaboration across organizational boundaries.

Data integration

Data integration ensures seamless synchronization and movement of data between SAP and non-SAP systems. Whether for reporting, analytics, or real-time processing, SAP connectors support various formats and protocols to consolidate enterprise data, improve consistency, and support data-driven decision-making.

User interface integration

User interface (UI) integration allows end-users to access data and processes from multiple systems through a unified interface. These connectors use technologies like SAP Fiori, SAP Business Technology Platform (BTP), and OData-based services to enable a seamless user experience, minimizing the need to switch between applications.

Cloud integration

As enterprises adopt hybrid and multi-cloud environments, cloud integration becomes essential. SAP connectors facilitate communication between on-premise SAP systems and cloud platforms such as SAP S/4HANA Cloud, Salesforce, or Microsoft Azure. This ensures real-time data access, scalability, and continuity across deployment models.

SAP Integration Solutions

SAP offers various comprehensive solutions designed to simplify integration and turn a company’s set of applications and sub-systems into a unified ecosystem. These solutions allow for data integration between SAP and non-SAP systems, covering many common integration cases.

SAP Integration Suite (on SAP BTP)

SAP Integration Suite is a comprehensive, cloud-based integration platform-as-a-service (iPaaS) that enables the integration of on-premise and cloud-based applications and processes.

Key Features:

\n
• Pre-built integration content and APIs
\n
• Supports A2A, B2B, B2G, and event-driven integrations
\n
• Tools for API management, process integration, and data flow orchestration
\n
• Open connectors for non-SAP apps (e.g., Salesforce, Microsoft 365)
\n

SAP Integration Suite is most often used when integrating hybrid landscapes and executing cloud and multi-cloud integration.

SAP Process Orchestration (SAP PO)

SAP PO is a legacy on-premise middleware suite combining SAP Process Integration (PI), SAP Business Process Management (BPM), and SAP Business Rules Management (BRM).

Key Features:

\n
• Message routing and transformation
\n
• B2B and A2A scenarios
\n
• Support for XML, IDoc, SOAP, and more
\n

SAP PO is best suited for existing on-premise or regulated environments. SAP recommends considering migration to SAP Integration Suite for new projects.

SAP Cloud Connector

SAP Cloud Connector is a lightweight agent that provides secure connectivity between on-premise systems and the SAP Business Technology Platform (BTP).

Key Features:

\n
• Secure VPN-like tunnel to SAP BTP
\n
• Easy configuration and management
\n
• No public exposure of on-premise systems
\n

It is widely used in hybrid cloud integrations and secure backend access for cloud apps.

SAP API Management (part of Integration Suite)

Included in SAP Integration Suite, SAP API Management manages the full lifecycle of APIs – design, publishing, monitoring, and security.

Key Features:

\n
• API design and policy enforcement
\n
• Analytics and usage tracking
\n
• Developer portal for publishing APIs
\n

It enables external applications and microservices architectures to leverage SAP APIs securely and efficiently.

SAP Datasphere (formerly SAP Data Intelligence)

SAP Datasphere is SAP's modern solution for unified data integration and orchestration across heterogeneous data environments.

Key Features:

\n
• Data pipeline design and automation
\n
• AI/ML integration
\n
• Governance and metadata management
\n

Datasphere is ideal for organizations managing complex data landscapes with analytics or migration requirements.

The comparison table below will help you better understand the differences between the SAP integration solutions and decide which suits your business the most.

DataLark – Secure and Smart Solution for SAP Integration

When you need to integrate complex landscapes with large amounts of disparate data, one native SAP solution may not be enough. That’s where DataLark – a data management platform with robust architecture and feature set designed specifically for high-complexity environments – can help.

DataLark supports both batch and real-time data processing. This is critical for scenarios where some data (e.g., financial transactions) require immediate consistency, while others (e.g., reporting data) can be handled periodically. Event-driven triggers (API calls, webhooks, or change data capture) and scheduled jobs make the solution adaptable to a wide range of SAP integration needs.

The platform's modular, plugin-based connector framework supports a broad variety of systems out of the box, including SAP (S/4HANA, ECC), non-SAP ERPs, SQL/NoSQL databases, REST/SOAP APIs, and file-based sources. This reduces the need for custom development and enables faster deployment of data flows.

In addition to SAP integration, DataLark can be utilized to ensure your system’s ongoing reliability with real-time monitoring, execution logs, and alerting functionality. This is especially useful in complex workflows where latency, volume, and transformation depth can introduce failure points. Logs can be used for audit trails, and the system provides visibility into individual transaction states.

DataLark supports both on-premise and cloud-based models, which makes the solution a great choice for a wide variety of enterprises.

SAP Integration Use Cases

Now let’s take a look at some use cases where SAP integration can help businesses improve process management, efficiency, and overall performance by streamlining data flows, improving communication, and reducing costly mistakes in data interpretation. As you can see below, SAP integration is beneficial for different industries.

E-commerce

In the E-commerce area, a common occurrence is the need to integrate an E-commerce platform (Shopify, Magento (now Adobe), Joomla!, or another) and SAP S/4HANA to manage inventory, pricing, and order fulfillment efficiently with real-time synchronization.

Integration scope may look like this:

\n
• Real-time inventory updates from SAP to the storefront
\n
• Automatic order creation in SAP upon online purchase
\n
• Synchronization of customer and product data
\n

As a result of successful integration, a company may achieve:

\n
• Reduction in order processing time due to the elimination of manual entries
\n
• Real-time stock visibility across all sales channels
\n
• Significant drop in out-of-stock incidents, which improves customer satisfaction
\n

Manufacturing

Manufacturing businesses often struggle to optimize production routings. Integrating 3DX PLM to SAP S/4HANA is one of the most reliable ways to automate routing and BOM synchronization.

Integration scope in this scenario might be the following:

\n
• Synchronizing materials, routings, operations, and component assignments
\n
• Embedding inspection and quality control characteristics
\n

Integration of SAP and 3DX PLM results in:

\n
• Reduction in manual routing data entry
\n
• Increase in data accuracy across engineering and production systems
\n
• Faster production planning cycles due to real-time data availability
\n

Finance

Global enterprises may need to integrate external finance systems (QuickBooks, NetSuite) for local transactions with SAP S/4HANA, which is used for core accounting to consolidate reporting.

To achieve the desired result, the scope of the project may look like this:

\n
• Automated financial data aggregation for monthly close
\n
• Real-time currency conversion and compliance validation
\n
• Standardized chart of accounts mapping across regions
\n

Integrating SAP with local financial systems helps enterprises get:

\n
• Faster financial close cycle
\n
• Improved regulatory compliance through centralized audit trails
\n
• Consistent financial reporting across global entitiesy
\n

Healthcare

Healthcare institutions like hospitals may need to integrate their Electronic Medical Records (EMR) systems with SAP MM and SD modules to automate medical supply procurement and tracking.

The integration scope in this case is usually like this:

\n
• Triggering supply requisitions from EMR treatment plans
\n
• Real-time updates of inventory and usage logs
\n
• Linking billing and insurance data to material movements
\n

Successful SAP integration improves hospitals’ supply chain management with:

\n
• Reduction in supply overstock
\n
• Faster turnaround for critical inventory replenishment
\n
• Streamlined billing accuracy, reducing patient disputes
\n

Utilities

Utility companies, especially those managing electricity infrastructure, may need real-time integration between their SCADA (Supervisory Control and Data Acquisition) systems and SAP PM to improve asset maintenance scheduling.

The integration scope of connecting SCADA to SAP PM may look like this:

\n
• Automated work order generation based on SCADA alerts
\n
• Real-time asset status updates in SAP
\n
• Integration of maintenance logs with historical performance data
\n

As a result of such integration, companies may get:

\n
• A decrease in unplanned outages due to predictive maintenance
\n
• Increased asset uptime and reliability
\n
• More efficient field technician scheduling, which improves SLA compliance
\n

Conclusion

SAP integration is a complex yet rewarding process that allows your business to greatly improve data quality, streamline data flows, adjust communication, boost productivity and scalability, enhance customer experience, and drive innovation. And businesses need professional data integration solutions to handle everything properly.

Here’s where DataLark comes in – and this solution goes beyond the integration process itself. With DataLark, you can speed up the process and ensure everything goes error-free while monitoring and managing your data to ensure uninterrupted processes and flawless operations. Contact us today to future-proof your enterprise with a dedicated solution for data integration and management.

Get a better understanding of SAP integration, take a closer look at its benefits, and discover solutions to successfully integrate your systems with SAP.

Get a better understanding of SAP integration, take a closer look at its benefits, and discover solutions to successfully integrate your systems with SAP.

Get a better understanding of SAP integration, take a closer look at its benefits, and discover solutions to successfully integrate your systems with SAP.

Understanding SAP Integration: Benefits, Scenarios, and Solutions

SAP is a complex, multi-component ecosystem that caters to different operational needs across various industries. It is typically used together with many third-party tools and systems to extend the existing functionality and create a unified enterprise management hub that operates as a single source of truth and relevant data. Both SAP components and third-party solutions need to be integrated into a single ecosystem to ensure uninterrupted operations and seamless data flows. This is where SAP integration plays a critical role.

In this post, we’ll delve deeper into SAP integration, so you will know how it works, understand its importance, and explore the most common SAP integration scenarios and use cases.

Why Does Your Business Need SAP Integration?

SAP integration allows businesses to unite different systems and platforms into a single ecosystem, providing a single source of trusted data and knowledge. This eventually helps streamline processes in many ways, covering the following aspects:

Standardized communication

One of the primary advantages of SAP integration is the ability to establish consistent communication standards across disparate systems. In a typical enterprise, multiple applications ranging from ERP and CRM to third-party logistics and financial platforms often use different protocols and data formats. SAP integration provides a unified framework that standardizes data structures and communication protocols (e.g., REST, OData, IDoc, RFC), enabling seamless interaction across systems. This not only reduces complexity, but it also minimizes translation errors, accelerates development, and ensures all systems speak the same language.

Real-time and asynchronous workflows

Modern business requires both speed and flexibility. SAP integration supports real-time workflows for scenarios where immediate data processing is essential, such as inventory updates, customer transactions, or financial postings. It also enables asynchronous processing for tasks that can be scheduled or queued, like batch processing or document archiving. This dual capability allows organizations to design their processes according to business needs, like balancing performance, system load, and responsiveness, while ensuring that data consistency and integrity are preserved across all systems.

Simplified system integration

Traditionally, integrating enterprise systems has been a complex and resource-intensive endeavor. SAP integration solutions, such as SAP Integration Suite, SAP Cloud Connector, and pre-built APIs, greatly reduce this complexity. These tools offer reusable components, visual interfaces for workflow orchestration, and out-of-the-box connectors for both SAP and non-SAP applications. As a result, IT teams can implement integrations faster and more reliably, while business users can access unified data without dealing with underlying technical intricacies. Simplified integration also enables agility, helping organizations adapt quickly to new requirements, partners, or technologies.

Security, governance, and compliance

Data security and compliance are non-negotiable in today’s digital environment. SAP integration frameworks come with enterprise-grade security features, including encrypted communication, secure user authentication (e.g., OAuth, SAML), role-based access control, and audit logging. These capabilities ensure that data transferred between systems remains protected against unauthorized access or tampering. Additionally, governance tools built into SAP’s integration solutions help organizations manage data lineage, monitor usage, and enforce compliance with regulations like GDPR, HIPAA, and SOX. This is especially critical when sharing data across departments, subsidiaries, or external partners.

SAP Integration Scenarios

Integration with SAP is critical for enabling fluid data exchange and process automation across a wide array of systems and environments. Below are the most common integration scenarios that organizations implement using SAP technologies:

Application-to-application (A2A)

A2A integration connects different applications within the same enterprise, ensuring consistent and synchronized data across internal systems like ERP, CRM, SCM, and HR. SAP connectors help streamline internal processes by automating data exchange between modules, reducing manual input, and improving system responsiveness.

Business-to-business (B2B)

B2B integration focuses on connecting SAP systems with external partners, suppliers, or customers. Through standardized protocols like EDI or APIs, SAP connectors enable secure and efficient exchange of documents, such as purchase orders, invoices, and shipment details, ensuring real-time collaboration across organizational boundaries.

Data integration

Data integration ensures seamless synchronization and movement of data between SAP and non-SAP systems. Whether for reporting, analytics, or real-time processing, SAP connectors support various formats and protocols to consolidate enterprise data, improve consistency, and support data-driven decision-making.

User interface integration

User interface (UI) integration allows end-users to access data and processes from multiple systems through a unified interface. These connectors use technologies like SAP Fiori, SAP Business Technology Platform (BTP), and OData-based services to enable a seamless user experience, minimizing the need to switch between applications.

Cloud integration

As enterprises adopt hybrid and multi-cloud environments, cloud integration becomes essential. SAP connectors facilitate communication between on-premise SAP systems and cloud platforms such as SAP S/4HANA Cloud, Salesforce, or Microsoft Azure. This ensures real-time data access, scalability, and continuity across deployment models.

SAP Integration Solutions

SAP offers various comprehensive solutions designed to simplify integration and turn a company’s set of applications and sub-systems into a unified ecosystem. These solutions allow for data integration between SAP and non-SAP systems, covering many common integration cases.

SAP Integration Suite (on SAP BTP)

SAP Integration Suite is a comprehensive, cloud-based integration platform-as-a-service (iPaaS) that enables the integration of on-premise and cloud-based applications and processes.

Key Features:

\n
• Pre-built integration content and APIs
\n
• Supports A2A, B2B, B2G, and event-driven integrations
\n
• Tools for API management, process integration, and data flow orchestration
\n
• Open connectors for non-SAP apps (e.g., Salesforce, Microsoft 365)
\n

SAP Integration Suite is most often used when integrating hybrid landscapes and executing cloud and multi-cloud integration.

SAP Process Orchestration (SAP PO)

SAP PO is a legacy on-premise middleware suite combining SAP Process Integration (PI), SAP Business Process Management (BPM), and SAP Business Rules Management (BRM).

Key Features:

\n
• Message routing and transformation
\n
• B2B and A2A scenarios
\n
• Support for XML, IDoc, SOAP, and more
\n

SAP PO is best suited for existing on-premise or regulated environments. SAP recommends considering migration to SAP Integration Suite for new projects.

SAP Cloud Connector

SAP Cloud Connector is a lightweight agent that provides secure connectivity between on-premise systems and the SAP Business Technology Platform (BTP).

Key Features:

\n
• Secure VPN-like tunnel to SAP BTP
\n
• Easy configuration and management
\n
• No public exposure of on-premise systems
\n

It is widely used in hybrid cloud integrations and secure backend access for cloud apps.

SAP API Management (part of Integration Suite)

Included in SAP Integration Suite, SAP API Management manages the full lifecycle of APIs – design, publishing, monitoring, and security.

Key Features:

\n
• API design and policy enforcement
\n
• Analytics and usage tracking
\n
• Developer portal for publishing APIs
\n

It enables external applications and microservices architectures to leverage SAP APIs securely and efficiently.

SAP Datasphere (formerly SAP Data Intelligence)

SAP Datasphere is SAP's modern solution for unified data integration and orchestration across heterogeneous data environments.

Key Features:

\n
• Data pipeline design and automation
\n
• AI/ML integration
\n
• Governance and metadata management
\n

Datasphere is ideal for organizations managing complex data landscapes with analytics or migration requirements.

The comparison table below will help you better understand the differences between the SAP integration solutions and decide which suits your business the most.

DataLark – Secure and Smart Solution for SAP Integration

When you need to integrate complex landscapes with large amounts of disparate data, one native SAP solution may not be enough. That’s where DataLark – a data management platform with robust architecture and feature set designed specifically for high-complexity environments – can help.

DataLark supports both batch and real-time data processing. This is critical for scenarios where some data (e.g., financial transactions) require immediate consistency, while others (e.g., reporting data) can be handled periodically. Event-driven triggers (API calls, webhooks, or change data capture) and scheduled jobs make the solution adaptable to a wide range of SAP integration needs.

The platform's modular, plugin-based connector framework supports a broad variety of systems out of the box, including SAP (S/4HANA, ECC), non-SAP ERPs, SQL/NoSQL databases, REST/SOAP APIs, and file-based sources. This reduces the need for custom development and enables faster deployment of data flows.

In addition to SAP integration, DataLark can be utilized to ensure your system’s ongoing reliability with real-time monitoring, execution logs, and alerting functionality. This is especially useful in complex workflows where latency, volume, and transformation depth can introduce failure points. Logs can be used for audit trails, and the system provides visibility into individual transaction states.

DataLark supports both on-premise and cloud-based models, which makes the solution a great choice for a wide variety of enterprises.

SAP Integration Use Cases

Now let’s take a look at some use cases where SAP integration can help businesses improve process management, efficiency, and overall performance by streamlining data flows, improving communication, and reducing costly mistakes in data interpretation. As you can see below, SAP integration is beneficial for different industries.

E-commerce

In the E-commerce area, a common occurrence is the need to integrate an E-commerce platform (Shopify, Magento (now Adobe), Joomla!, or another) and SAP S/4HANA to manage inventory, pricing, and order fulfillment efficiently with real-time synchronization.

Integration scope may look like this:

\n
• Real-time inventory updates from SAP to the storefront
\n
• Automatic order creation in SAP upon online purchase
\n
• Synchronization of customer and product data
\n

As a result of successful integration, a company may achieve:

\n
• Reduction in order processing time due to the elimination of manual entries
\n
• Real-time stock visibility across all sales channels
\n
• Significant drop in out-of-stock incidents, which improves customer satisfaction
\n

Manufacturing

Manufacturing businesses often struggle to optimize production routings. Integrating 3DX PLM to SAP S/4HANA is one of the most reliable ways to automate routing and BOM synchronization.

Integration scope in this scenario might be the following:

\n
• Synchronizing materials, routings, operations, and component assignments
\n
• Embedding inspection and quality control characteristics
\n

Integration of SAP and 3DX PLM results in:

\n
• Reduction in manual routing data entry
\n
• Increase in data accuracy across engineering and production systems
\n
• Faster production planning cycles due to real-time data availability
\n

Finance

Global enterprises may need to integrate external finance systems (QuickBooks, NetSuite) for local transactions with SAP S/4HANA, which is used for core accounting to consolidate reporting.

To achieve the desired result, the scope of the project may look like this:

\n
• Automated financial data aggregation for monthly close
\n
• Real-time currency conversion and compliance validation
\n
• Standardized chart of accounts mapping across regions
\n

Integrating SAP with local financial systems helps enterprises get:

\n
• Faster financial close cycle
\n
• Improved regulatory compliance through centralized audit trails
\n
• Consistent financial reporting across global entitiesy
\n

Healthcare

Healthcare institutions like hospitals may need to integrate their Electronic Medical Records (EMR) systems with SAP MM and SD modules to automate medical supply procurement and tracking.

The integration scope in this case is usually like this:

\n
• Triggering supply requisitions from EMR treatment plans
\n
• Real-time updates of inventory and usage logs
\n
• Linking billing and insurance data to material movements
\n

Successful SAP integration improves hospitals’ supply chain management with:

\n
• Reduction in supply overstock
\n
• Faster turnaround for critical inventory replenishment
\n
• Streamlined billing accuracy, reducing patient disputes
\n

Utilities

Utility companies, especially those managing electricity infrastructure, may need real-time integration between their SCADA (Supervisory Control and Data Acquisition) systems and SAP PM to improve asset maintenance scheduling.

The integration scope of connecting SCADA to SAP PM may look like this:

\n
• Automated work order generation based on SCADA alerts
\n
• Real-time asset status updates in SAP
\n
• Integration of maintenance logs with historical performance data
\n

As a result of such integration, companies may get:

\n
• A decrease in unplanned outages due to predictive maintenance
\n
• Increased asset uptime and reliability
\n
• More efficient field technician scheduling, which improves SLA compliance
\n

Conclusion

SAP integration is a complex yet rewarding process that allows your business to greatly improve data quality, streamline data flows, adjust communication, boost productivity and scalability, enhance customer experience, and drive innovation. And businesses need professional data integration solutions to handle everything properly.

Here’s where DataLark comes in – and this solution goes beyond the integration process itself. With DataLark, you can speed up the process and ensure everything goes error-free while monitoring and managing your data to ensure uninterrupted processes and flawless operations. Contact us today to future-proof your enterprise with a dedicated solution for data integration and management.

Get a better understanding of SAP integration, take a closer look at its benefits, and discover solutions to successfully integrate your systems with SAP.

Get a better understanding of SAP integration, take a closer look at its benefits, and discover solutions to successfully integrate your systems with SAP.

Get a better understanding of SAP integration, take a closer look at its benefits, and discover solutions to successfully integrate your systems with SAP.

Understanding SAP Integration: Benefits, Scenarios, and Solutions

SAP is a complex, multi-component ecosystem that caters to different operational needs across various industries. It is typically used together with many third-party tools and systems to extend the existing functionality and create a unified enterprise management hub that operates as a single source of truth and relevant data. Both SAP components and third-party solutions need to be integrated into a single ecosystem to ensure uninterrupted operations and seamless data flows. This is where SAP integration plays a critical role.

In this post, we’ll delve deeper into SAP integration, so you will know how it works, understand its importance, and explore the most common SAP integration scenarios and use cases.

Why Does Your Business Need SAP Integration?

SAP integration allows businesses to unite different systems and platforms into a single ecosystem, providing a single source of trusted data and knowledge. This eventually helps streamline processes in many ways, covering the following aspects:

Standardized communication

One of the primary advantages of SAP integration is the ability to establish consistent communication standards across disparate systems. In a typical enterprise, multiple applications ranging from ERP and CRM to third-party logistics and financial platforms often use different protocols and data formats. SAP integration provides a unified framework that standardizes data structures and communication protocols (e.g., REST, OData, IDoc, RFC), enabling seamless interaction across systems. This not only reduces complexity, but it also minimizes translation errors, accelerates development, and ensures all systems speak the same language.

Real-time and asynchronous workflows

Modern business requires both speed and flexibility. SAP integration supports real-time workflows for scenarios where immediate data processing is essential, such as inventory updates, customer transactions, or financial postings. It also enables asynchronous processing for tasks that can be scheduled or queued, like batch processing or document archiving. This dual capability allows organizations to design their processes according to business needs, like balancing performance, system load, and responsiveness, while ensuring that data consistency and integrity are preserved across all systems.

Simplified system integration

Traditionally, integrating enterprise systems has been a complex and resource-intensive endeavor. SAP integration solutions, such as SAP Integration Suite, SAP Cloud Connector, and pre-built APIs, greatly reduce this complexity. These tools offer reusable components, visual interfaces for workflow orchestration, and out-of-the-box connectors for both SAP and non-SAP applications. As a result, IT teams can implement integrations faster and more reliably, while business users can access unified data without dealing with underlying technical intricacies. Simplified integration also enables agility, helping organizations adapt quickly to new requirements, partners, or technologies.

Security, governance, and compliance

Data security and compliance are non-negotiable in today’s digital environment. SAP integration frameworks come with enterprise-grade security features, including encrypted communication, secure user authentication (e.g., OAuth, SAML), role-based access control, and audit logging. These capabilities ensure that data transferred between systems remains protected against unauthorized access or tampering. Additionally, governance tools built into SAP’s integration solutions help organizations manage data lineage, monitor usage, and enforce compliance with regulations like GDPR, HIPAA, and SOX. This is especially critical when sharing data across departments, subsidiaries, or external partners.

SAP Integration Scenarios

Integration with SAP is critical for enabling fluid data exchange and process automation across a wide array of systems and environments. Below are the most common integration scenarios that organizations implement using SAP technologies:

Application-to-application (A2A)

A2A integration connects different applications within the same enterprise, ensuring consistent and synchronized data across internal systems like ERP, CRM, SCM, and HR. SAP connectors help streamline internal processes by automating data exchange between modules, reducing manual input, and improving system responsiveness.

Business-to-business (B2B)

B2B integration focuses on connecting SAP systems with external partners, suppliers, or customers. Through standardized protocols like EDI or APIs, SAP connectors enable secure and efficient exchange of documents, such as purchase orders, invoices, and shipment details, ensuring real-time collaboration across organizational boundaries.

Data integration

Data integration ensures seamless synchronization and movement of data between SAP and non-SAP systems. Whether for reporting, analytics, or real-time processing, SAP connectors support various formats and protocols to consolidate enterprise data, improve consistency, and support data-driven decision-making.

User interface integration

User interface (UI) integration allows end-users to access data and processes from multiple systems through a unified interface. These connectors use technologies like SAP Fiori, SAP Business Technology Platform (BTP), and OData-based services to enable a seamless user experience, minimizing the need to switch between applications.

Cloud integration

As enterprises adopt hybrid and multi-cloud environments, cloud integration becomes essential. SAP connectors facilitate communication between on-premise SAP systems and cloud platforms such as SAP S/4HANA Cloud, Salesforce, or Microsoft Azure. This ensures real-time data access, scalability, and continuity across deployment models.

SAP Integration Solutions

SAP offers various comprehensive solutions designed to simplify integration and turn a company’s set of applications and sub-systems into a unified ecosystem. These solutions allow for data integration between SAP and non-SAP systems, covering many common integration cases.

SAP Integration Suite (on SAP BTP)

SAP Integration Suite is a comprehensive, cloud-based integration platform-as-a-service (iPaaS) that enables the integration of on-premise and cloud-based applications and processes.

Key Features:

\n
• Pre-built integration content and APIs
\n
• Supports A2A, B2B, B2G, and event-driven integrations
\n
• Tools for API management, process integration, and data flow orchestration
\n
• Open connectors for non-SAP apps (e.g., Salesforce, Microsoft 365)
\n

SAP Integration Suite is most often used when integrating hybrid landscapes and executing cloud and multi-cloud integration.

SAP Process Orchestration (SAP PO)

SAP PO is a legacy on-premise middleware suite combining SAP Process Integration (PI), SAP Business Process Management (BPM), and SAP Business Rules Management (BRM).

Key Features:

\n
• Message routing and transformation
\n
• B2B and A2A scenarios
\n
• Support for XML, IDoc, SOAP, and more
\n

SAP PO is best suited for existing on-premise or regulated environments. SAP recommends considering migration to SAP Integration Suite for new projects.

SAP Cloud Connector

SAP Cloud Connector is a lightweight agent that provides secure connectivity between on-premise systems and the SAP Business Technology Platform (BTP).

Key Features:

\n
• Secure VPN-like tunnel to SAP BTP
\n
• Easy configuration and management
\n
• No public exposure of on-premise systems
\n

It is widely used in hybrid cloud integrations and secure backend access for cloud apps.

SAP API Management (part of Integration Suite)

Included in SAP Integration Suite, SAP API Management manages the full lifecycle of APIs – design, publishing, monitoring, and security.

Key Features:

\n
• API design and policy enforcement
\n
• Analytics and usage tracking
\n
• Developer portal for publishing APIs
\n

It enables external applications and microservices architectures to leverage SAP APIs securely and efficiently.

SAP Datasphere (formerly SAP Data Intelligence)

SAP Datasphere is SAP's modern solution for unified data integration and orchestration across heterogeneous data environments.

Key Features:

\n
• Data pipeline design and automation
\n
• AI/ML integration
\n
• Governance and metadata management
\n

Datasphere is ideal for organizations managing complex data landscapes with analytics or migration requirements.

The comparison table below will help you better understand the differences between the SAP integration solutions and decide which suits your business the most.

DataLark – Secure and Smart Solution for SAP Integration

When you need to integrate complex landscapes with large amounts of disparate data, one native SAP solution may not be enough. That’s where DataLark – a data management platform with robust architecture and feature set designed specifically for high-complexity environments – can help.

DataLark supports both batch and real-time data processing. This is critical for scenarios where some data (e.g., financial transactions) require immediate consistency, while others (e.g., reporting data) can be handled periodically. Event-driven triggers (API calls, webhooks, or change data capture) and scheduled jobs make the solution adaptable to a wide range of SAP integration needs.

The platform's modular, plugin-based connector framework supports a broad variety of systems out of the box, including SAP (S/4HANA, ECC), non-SAP ERPs, SQL/NoSQL databases, REST/SOAP APIs, and file-based sources. This reduces the need for custom development and enables faster deployment of data flows.

In addition to SAP integration, DataLark can be utilized to ensure your system’s ongoing reliability with real-time monitoring, execution logs, and alerting functionality. This is especially useful in complex workflows where latency, volume, and transformation depth can introduce failure points. Logs can be used for audit trails, and the system provides visibility into individual transaction states.

DataLark supports both on-premise and cloud-based models, which makes the solution a great choice for a wide variety of enterprises.

SAP Integration Use Cases

Now let’s take a look at some use cases where SAP integration can help businesses improve process management, efficiency, and overall performance by streamlining data flows, improving communication, and reducing costly mistakes in data interpretation. As you can see below, SAP integration is beneficial for different industries.

E-commerce

In the E-commerce area, a common occurrence is the need to integrate an E-commerce platform (Shopify, Magento (now Adobe), Joomla!, or another) and SAP S/4HANA to manage inventory, pricing, and order fulfillment efficiently with real-time synchronization.

Integration scope may look like this:

\n
• Real-time inventory updates from SAP to the storefront
\n
• Automatic order creation in SAP upon online purchase
\n
• Synchronization of customer and product data
\n

As a result of successful integration, a company may achieve:

\n
• Reduction in order processing time due to the elimination of manual entries
\n
• Real-time stock visibility across all sales channels
\n
• Significant drop in out-of-stock incidents, which improves customer satisfaction
\n

Manufacturing

Manufacturing businesses often struggle to optimize production routings. Integrating 3DX PLM to SAP S/4HANA is one of the most reliable ways to automate routing and BOM synchronization.

Integration scope in this scenario might be the following:

\n
• Synchronizing materials, routings, operations, and component assignments
\n
• Embedding inspection and quality control characteristics
\n

Integration of SAP and 3DX PLM results in:

\n
• Reduction in manual routing data entry
\n
• Increase in data accuracy across engineering and production systems
\n
• Faster production planning cycles due to real-time data availability
\n

Finance

Global enterprises may need to integrate external finance systems (QuickBooks, NetSuite) for local transactions with SAP S/4HANA, which is used for core accounting to consolidate reporting.

To achieve the desired result, the scope of the project may look like this:

\n
• Automated financial data aggregation for monthly close
\n
• Real-time currency conversion and compliance validation
\n
• Standardized chart of accounts mapping across regions
\n

Integrating SAP with local financial systems helps enterprises get:

\n
• Faster financial close cycle
\n
• Improved regulatory compliance through centralized audit trails
\n
• Consistent financial reporting across global entitiesy
\n

Healthcare

Healthcare institutions like hospitals may need to integrate their Electronic Medical Records (EMR) systems with SAP MM and SD modules to automate medical supply procurement and tracking.

The integration scope in this case is usually like this:

\n
• Triggering supply requisitions from EMR treatment plans
\n
• Real-time updates of inventory and usage logs
\n
• Linking billing and insurance data to material movements
\n

Successful SAP integration improves hospitals’ supply chain management with:

\n
• Reduction in supply overstock
\n
• Faster turnaround for critical inventory replenishment
\n
• Streamlined billing accuracy, reducing patient disputes
\n

Utilities

Utility companies, especially those managing electricity infrastructure, may need real-time integration between their SCADA (Supervisory Control and Data Acquisition) systems and SAP PM to improve asset maintenance scheduling.

The integration scope of connecting SCADA to SAP PM may look like this:

\n
• Automated work order generation based on SCADA alerts
\n
• Real-time asset status updates in SAP
\n
• Integration of maintenance logs with historical performance data
\n

As a result of such integration, companies may get:

\n
• A decrease in unplanned outages due to predictive maintenance
\n
• Increased asset uptime and reliability
\n
• More efficient field technician scheduling, which improves SLA compliance
\n

Conclusion

SAP integration is a complex yet rewarding process that allows your business to greatly improve data quality, streamline data flows, adjust communication, boost productivity and scalability, enhance customer experience, and drive innovation. And businesses need professional data integration solutions to handle everything properly.

Here’s where DataLark comes in – and this solution goes beyond the integration process itself. With DataLark, you can speed up the process and ensure everything goes error-free while monitoring and managing your data to ensure uninterrupted processes and flawless operations. Contact us today to future-proof your enterprise with a dedicated solution for data integration and management.

Get a better understanding of SAP integration, take a closer look at its benefits, and discover solutions to successfully integrate your systems with SAP.

Building a Comprehensive Business Case for SAP S/4HANA Migration

Migrating to SAP S/4HANA represents a significant milestone in any organization’s digital transformation journey. However, such a transition requires careful planning, strategic vision, and solid financial justification. Building a comprehensive business case is essential to ensure stakeholders understand the value, risks, and expected returns of this complex endeavor. 

This article explores the critical components of a compelling SAP S/4HANA migration business case, guiding you through its key strategic components, migration planning and preparation, and simplification opportunities.

Download our SAP S/4HANA migration whitepaper to explore all the migration process details for your successful transition.

Why Do You Need a Business Case for SAP S/4HANA Migration?

Migrating to SAP S/4HANA is a major strategic imperative that requires more than just technical planning: it demands a well-structured business case. A comprehensive business case plays two critical roles in the migration journey.    

Securing buy-in from key decision-makers

A clear, data-backed business case is essential for aligning executive sponsors, finance leaders, and other key stakeholders. It translates the technical benefits of S/4HANA into strategic business value, demonstrating how the migration supports organizational goals such as increased agility, improved operational efficiency, and future readiness. This alignment is crucial for securing the budget, resources, and organizational support necessary to move forward confidently.

Establishing a benchmark for measuring ROI

The SAP S/4HANA business case, besides serving as an investment justification, also acts as a benchmark to measure the overall success of the SAP S/4HANA migration. Clearly defining expected outcomes, such as cost savings, performance improvements, or process enhancements, provides a baseline against which post-migration results can be measured. Calculating the ROI of SAP S/4HANA migration ensures payback and allows optimizing investment over time.

Key Components of an SAP S/4HANA Business Case

A comprehensive and persuasive business case for SAP S/4HANA migration covers many aspects of your company’s current state and performance, defining benchmarks for future success measurement. When establishing a compelling case, thoroughly consider the elements detailed below.

Strategic objectives

Clearly setting strategic objectives is essential for the SAP S/4HANA migration business case, as they will help you establish your goals, adhere to the migration plan, and prove the necessity of this investment to key stakeholders. Usually, strategic objectives include:

Alignment with corporate goals

\n
• Better customer experience: Offer unique customer journeys and improve satisfaction with the help of real-time data processing and predictive analytics.
\n
• M&A readiness: Provide seamless mergers and acquisitions due to unified and streamlined business processes.
\n
• Global expansion: Ensure smooth worldwide operations and compliance with key regulations with a scalable and flexible ERP.
\n

Business drivers

\n
• Agility: Promptly adjust operations in response to market fluctuations and changing trends with flexible processes.
\n
• Real-time analytics: Access relevant data for informed decision-making and proactive issue resolution.
\n
• Simplified operations: Streamline business processes to reduce operational burden and boost efficiency.
\n

Competition

\n
• Global digital transformation: Businesses worldwide actively implement new technologies and switch to up-to-date ERP solutions to gain a competitive advantage in hectic, fluctuating markets.
\n
• Adoption of SAP S/4HANA: SAP S/4HANA offers companies a set of competitive advantages, such as access to real-time analytics, process automation, system agility, and AI capabilities.
\n

Current state assessment

A comprehensive assessment of your current IT landscape is crucial to highlight the limits of a legacy system, find drawbacks of your current processes, and spot potential compliance risks. This creates a baseline for success measurement after migration, showcasing the advantages of SAP S/4HANA.

Strategic Advantages of SAP S/4HANA

\n
• Real-time analytics and data processing that support data-driven decision-making.
\n
• Simplified data model that lowers data redundancy and data footprint.
\n
• Intuitive SAP Fiori interface, which enhances user experience and makes the system available on mobile devices.
\n
• Advanced functionalities and innovations, such as embedded analytics, AI, machine learning, and robotic process automation.
\n
• Better integration with cloud and digital technologies due to hybrid and cloud deployment.
\n
• Seamless integration with SAP cloud portfolio (SAP Cloud Platform, SAP Ariba).
\n
• Improved performance and scalability due to the HANA in-memory engine.
\n
• Simplified IT landscape and lower total cost of ownership (TCO).
\n

Financial justification

Proving that the SAP S/4HANA migration is financially reasonable is crucial for persuading key stakeholders to start the process. It will also help you adhere to your financial plan and track the payback period, reaching your goals as a result. Financial justification comprises the following steps:

ROI calculations and metrics

Define clear metrics to track the overall success and ROI of SAP S/4HANA migration:

\n
• Cost savings: Set benchmarks to track expense reduction caused by IT maintenance, process inefficiencies, and system downtime.
\n
• Operational efficiency: Track cycle times, fulfillment rates, and other key performance indicators that are relevant to your industry.
\n
• User adoption rates: Evaluate how effectively employees are using the system to perform their roles.
\n

TCO reduction plan in 5 -10 years

According to research by Forrester, organizations can greatly reduce TCO (total cost of ownership) after implementing SAP Cloud ERP.

\n
• Shifting from local on-premise ERP solutions eliminates related infrastructure, maintenance, and labor expenses, cutting overall maintenance costs.
\n
• SAP S/4HANA improves efficiency by up to 30% for users who spend the majority of their working time interacting with the ERP system, and by up to 15% for users who only interact with the ERP system occasionally.
\n
• Switching to in-built SAP Cloud ERP security features instead of legacy recovery platforms and security solutions also saves costs on third-party tools.
\n

Payback period and breakeven point

Set the financial recovery timeline to clearly showcase the payback benchmarks:

\n
• Payback period: The typical timeframe for recovering the initial investment in SAP S/4HANA ranges from 18 to 36 months.
\n
• Breakeven point: The cumulative benefits from the migration offset the total costs incurred within 2 to 3 years post-migration.
\n
• Ongoing value generation: Besides breakeven, companies may expect growing ROI due to scalable operations, enhanced data-driven decision-making, and reduced IT overhead.
\n

Risk assessment and mitigation plan

Evaluate all the potential risks while migrating to SAP S/4HANA. This will help you prepare your options if the transition does not go according to plan. Additionally, work out a migration plan that will help your business gradually adjust to the new system, providing a smoother transition.

Potential risks

\n
• Implementation challenges: The complexity of data migration and system integration.
\n
• User adoption: Resistance to change and inadequate training.
\n
• Operational disruptions: Downtime during transition.
\n

Mitigation strategies

\n
• Comprehensive planning: Detailed project timelines and resource allocation.
\n
• Change management: Effective communication and training programs.
\n
• Phased implementation: Gradual rollout to minimize disruptions.
\n

Implementation roadmap

One of the best practices for implementing SAP S/4HANA is the SAP Activate methodology, which breaks down the implementation process into six essential steps:

Discover

\n
• Evaluate current processes: Assess the existing needs and processes of your business.
\n
• Define technical requirements: Create a detailed specification of the functional and technical needs of the new system, in our case, SAP S/4HANA.
\n

Prepare

\n
• Set goals and objectives: Establish and agree on the goals for the project.
\n
• Assemble the project team: Appoint team members and define their roles and responsibilities.
\n
• Develop the project plan: Create a comprehensive plan that outlines project phases, timelines, resources, and KPIs.
\n
• Determine budget: Estimate and approve the budget.
\n
• Prepare specifications: Develop the technical and functional specifications for the development team.
\n

Explore

\n
• Ensure that business requirements are met: Check that SAP S/4HANA migration aligns with business requirements and project objectives.
\n
• alidate data:V Verify the accuracy and compliance of the data.
\n

Realize

\n
• Migrate data: Transfer data from existing systems to the new one.
\n
• Configure the system: Set up the solution according to requirements and specifications.
\n
• Customize the system: Develop additional features and modules, if the standard solution does not meet all needs.
\n
• Integrate with your business: Configure SAP S/4HANA to work with other IT systems and applications in your environment.
\n

Deploy

\n
• Test the system: Perform functional, integration, regression, and load testing to ensure everything works correctly.
\n
• Educate users: Organize sessions to help users become familiar with the new system.
\n

Run

\n
• Check system readiness: Verify that the system is ready for operational use.
\n
• Launch: Officially transition to active use of SAP S/4HANA.
\n
• Ensure ongoing support: Continuously monitor solution performance to identify and resolve any issues.
\n

KPIs

To measure the success of migrating your business to SAP S/4HANA, consider setting KPIs. This will help you achieve your business goals and make timely adjustments to the strategy in case of underperformance.

System performance metrics

\n
• System availability: Monitor the reliability and accessibility of the SAP system.
\n
• Transaction response time: Measure how quickly transactions are processed to ensure a smooth user experience.
\n

Business process efficiency

\n
• Order-to-cash cycle time: Track the duration from order receipt to payment collection.
\n
• Procure-to-pay cycle time: Measure the efficiency of your procurement process.
\n
• Inventory turnover rate: Assess how quickly inventory is sold and replaced.
\n

Financial metrics

\n
• Cost savings: Calculate reductions in operational costs, IT maintenance, and overhead.
\n
• Revenue growth: Evaluate improvements linked to faster processes and better decision-making.
\n

User adoption & satisfaction

\n
• User adoption rate: Percentage of employees actively using the system compared to the total number of anticipated users.
\n
• Training completion rate: Track how many users have completed SAP training programs.
\n
• User satisfaction score: Collect feedback to gauge ease-of-use and support quality.
\n

Data quality & reporting

\n
• Accuracy of master data: Monitor error rates in key data fields.
\n
• Report generation time: Measure how quickly business insights are produced.
\n

Innovation & agility

\n
• Number of process improvements implemented: Track how many optimizations or automations have been introduced post-migration.
\n
• Time to market for new products/services: Measure how migration enables faster launches.
\n

How DataLark Makes SAP S/4HANA Migration a Success

Migrating to SAP S/4HANA is a complicated and, let’s be honest, costly procedure that requires time and resources. It’s natural that business owners want to lower the price without losing quality.

DataLark, an SAP-centric data management solution developed by LeverX, can help you significantly boost the cost efficiency and success of the SAP S/4HANA migration. By streamlining complex data migration processes, DataLark enables organizations to achieve faster, more reliable, cost-effective transitions to SAP S/4HANA, making the business case more attractive.

With DataLark, you get:

\n
• 60% acceleration of the SAP S/4HANA migration process
\n
• 90% improvement in data management transparency
\n
• 60% enhancement in business modernization and scalability speed
\n

No coding experience required! The intuitive, drag-and-drop interface allows non-technical users to design and implement data workflows. This accessibility reduces the dependency on specialized IT resources, lowering labor costs and expediting the migration process.

DataLark offers robust data extraction, cleansing, transformation, and validation capabilities. These functions ensure high-quality data migration, reducing errors and the need for costly post-migration corrections.

Supporting on-premise, cloud, and hybrid environments, DataLark provides organizations with the flexibility to choose deployment models that align with their infrastructure and cost considerations.

DataLark integrates smoothly with the SAP Migration Cockpit and supports various data loading methods, including OData and RFC APIs. This compatibility ensures a cohesive migration experience and leverages existing SAP tools, enhancing overall efficiency.

DataLark’s unified data‑migration assessment & roadmap

DataLark allows you to create a consistent data migration strategy, assess data, conduct testing, and prepare a fallback plan (in case something goes wrong) via fruitful collaboration with LeverX migration experts.

This offer is a four‑week consulting sprint. We design a safe, cost‑balanced path to move your master and transactional data from SAP ECC and non‑SAP systems into SAP S/4HANA.

As a result, the following artifacts of your SAP S/4HANA will be migration-ready:

\n
• High-level migration blueprint — target architecture, data‑flow diagrams, tooling stack
\n
• Pilot results report — load metrics, issue log, remediation actions
\n
• Data quality & cleansing report — ranked by risk and impact
\n
• Cut-over playbook — a plan with fallback scenarios
\n

Conclusion

To effectively support decision-making, it's essential to blend both qualitative and quantitative evaluations of SAP S/4HANA migration and present them in a clear, leadership-friendly format. The focus should be on highlighting key business value drivers, current pain points, opportunities for improvement, and setting practical, achievable goals. Before embarking on a migration journey, business owners should validate the business case to ensure alignment and accuracy.

We hope this has provided valuable insights into creating the business case for the SAP S/4HANA migration and demonstrated why it represents a strong investment opportunity. When implemented with the right strategy, this robust ERP solution can drive efficiency and reduce costs, enhancing productivity, customer satisfaction, and overall business performance.

Create a compelling business case for SAP S/4HANA migration, enumerating process and financial benefits, justified by ROI calculation benchmarks.

Create a compelling business case for SAP S/4HANA migration, enumerating process and financial benefits, justified by ROI calculation benchmarks.

Building a Comprehensive Business Case for SAP S/4HANA Migration

Migrating to SAP S/4HANA represents a significant milestone in any organization’s digital transformation journey. However, such a transition requires careful planning, strategic vision, and solid financial justification. Building a comprehensive business case is essential to ensure stakeholders understand the value, risks, and expected returns of this complex endeavor. 

This article explores the critical components of a compelling SAP S/4HANA migration business case, guiding you through its key strategic components, migration planning and preparation, and simplification opportunities.

Download our SAP S/4HANA migration whitepaper to explore all the migration process details for your successful transition.

Why Do You Need a Business Case for SAP S/4HANA Migration?

Migrating to SAP S/4HANA is a major strategic imperative that requires more than just technical planning: it demands a well-structured business case. A comprehensive business case plays two critical roles in the migration journey.    

Securing buy-in from key decision-makers

A clear, data-backed business case is essential for aligning executive sponsors, finance leaders, and other key stakeholders. It translates the technical benefits of S/4HANA into strategic business value, demonstrating how the migration supports organizational goals such as increased agility, improved operational efficiency, and future readiness. This alignment is crucial for securing the budget, resources, and organizational support necessary to move forward confidently.

Establishing a benchmark for measuring ROI

The SAP S/4HANA business case, besides serving as an investment justification, also acts as a benchmark to measure the overall success of the SAP S/4HANA migration. Clearly defining expected outcomes, such as cost savings, performance improvements, or process enhancements, provides a baseline against which post-migration results can be measured. Calculating the ROI of SAP S/4HANA migration ensures payback and allows optimizing investment over time.

Key Components of an SAP S/4HANA Business Case

A comprehensive and persuasive business case for SAP S/4HANA migration covers many aspects of your company’s current state and performance, defining benchmarks for future success measurement. When establishing a compelling case, thoroughly consider the elements detailed below.

Strategic objectives

Clearly setting strategic objectives is essential for the SAP S/4HANA migration business case, as they will help you establish your goals, adhere to the migration plan, and prove the necessity of this investment to key stakeholders. Usually, strategic objectives include:

Alignment with corporate goals

\n
• Better customer experience: Offer unique customer journeys and improve satisfaction with the help of real-time data processing and predictive analytics.
\n
• M&A readiness: Provide seamless mergers and acquisitions due to unified and streamlined business processes.
\n
• Global expansion: Ensure smooth worldwide operations and compliance with key regulations with a scalable and flexible ERP.
\n

Business drivers

\n
• Agility: Promptly adjust operations in response to market fluctuations and changing trends with flexible processes.
\n
• Real-time analytics: Access relevant data for informed decision-making and proactive issue resolution.
\n
• Simplified operations: Streamline business processes to reduce operational burden and boost efficiency.
\n

Competition

\n
• Global digital transformation: Businesses worldwide actively implement new technologies and switch to up-to-date ERP solutions to gain a competitive advantage in hectic, fluctuating markets.
\n
• Adoption of SAP S/4HANA: SAP S/4HANA offers companies a set of competitive advantages, such as access to real-time analytics, process automation, system agility, and AI capabilities.
\n

Current state assessment

A comprehensive assessment of your current IT landscape is crucial to highlight the limits of a legacy system, find drawbacks of your current processes, and spot potential compliance risks. This creates a baseline for success measurement after migration, showcasing the advantages of SAP S/4HANA.

Strategic Advantages of SAP S/4HANA

\n
• Real-time analytics and data processing that support data-driven decision-making.
\n
• Simplified data model that lowers data redundancy and data footprint.
\n
• Intuitive SAP Fiori interface, which enhances user experience and makes the system available on mobile devices.
\n
• Advanced functionalities and innovations, such as embedded analytics, AI, machine learning, and robotic process automation.
\n
• Better integration with cloud and digital technologies due to hybrid and cloud deployment.
\n
• Seamless integration with SAP cloud portfolio (SAP Cloud Platform, SAP Ariba).
\n
• Improved performance and scalability due to the HANA in-memory engine.
\n
• Simplified IT landscape and lower total cost of ownership (TCO).
\n

Financial justification

Proving that the SAP S/4HANA migration is financially reasonable is crucial for persuading key stakeholders to start the process. It will also help you adhere to your financial plan and track the payback period, reaching your goals as a result. Financial justification comprises the following steps:

ROI calculations and metrics

Define clear metrics to track the overall success and ROI of SAP S/4HANA migration:

\n
• Cost savings: Set benchmarks to track expense reduction caused by IT maintenance, process inefficiencies, and system downtime.
\n
• Operational efficiency: Track cycle times, fulfillment rates, and other key performance indicators that are relevant to your industry.
\n
• User adoption rates: Evaluate how effectively employees are using the system to perform their roles.
\n

TCO reduction plan in 5 -10 years

According to research by Forrester, organizations can greatly reduce TCO (total cost of ownership) after implementing SAP Cloud ERP.

\n
• Shifting from local on-premise ERP solutions eliminates related infrastructure, maintenance, and labor expenses, cutting overall maintenance costs.
\n
• SAP S/4HANA improves efficiency by up to 30% for users who spend the majority of their working time interacting with the ERP system, and by up to 15% for users who only interact with the ERP system occasionally.
\n
• Switching to in-built SAP Cloud ERP security features instead of legacy recovery platforms and security solutions also saves costs on third-party tools.
\n

Payback period and breakeven point

Set the financial recovery timeline to clearly showcase the payback benchmarks:

\n
• Payback period: The typical timeframe for recovering the initial investment in SAP S/4HANA ranges from 18 to 36 months.
\n
• Breakeven point: The cumulative benefits from the migration offset the total costs incurred within 2 to 3 years post-migration.
\n
• Ongoing value generation: Besides breakeven, companies may expect growing ROI due to scalable operations, enhanced data-driven decision-making, and reduced IT overhead.
\n

Risk assessment and mitigation plan

Evaluate all the potential risks while migrating to SAP S/4HANA. This will help you prepare your options if the transition does not go according to plan. Additionally, work out a migration plan that will help your business gradually adjust to the new system, providing a smoother transition.

Potential risks

\n
• Implementation challenges: The complexity of data migration and system integration.
\n
• User adoption: Resistance to change and inadequate training.
\n
• Operational disruptions: Downtime during transition.
\n

Mitigation strategies

\n
• Comprehensive planning: Detailed project timelines and resource allocation.
\n
• Change management: Effective communication and training programs.
\n
• Phased implementation: Gradual rollout to minimize disruptions.
\n

Implementation roadmap

One of the best practices for implementing SAP S/4HANA is the SAP Activate methodology, which breaks down the implementation process into six essential steps:

Discover

\n
• Evaluate current processes: Assess the existing needs and processes of your business.
\n
• Define technical requirements: Create a detailed specification of the functional and technical needs of the new system, in our case, SAP S/4HANA.
\n

Prepare

\n
• Set goals and objectives: Establish and agree on the goals for the project.
\n
• Assemble the project team: Appoint team members and define their roles and responsibilities.
\n
• Develop the project plan: Create a comprehensive plan that outlines project phases, timelines, resources, and KPIs.
\n
• Determine budget: Estimate and approve the budget.
\n
• Prepare specifications: Develop the technical and functional specifications for the development team.
\n

Explore

\n
• Ensure that business requirements are met: Check that SAP S/4HANA migration aligns with business requirements and project objectives.
\n
• alidate data:V Verify the accuracy and compliance of the data.
\n

Realize

\n
• Migrate data: Transfer data from existing systems to the new one.
\n
• Configure the system: Set up the solution according to requirements and specifications.
\n
• Customize the system: Develop additional features and modules, if the standard solution does not meet all needs.
\n
• Integrate with your business: Configure SAP S/4HANA to work with other IT systems and applications in your environment.
\n

Deploy

\n
• Test the system: Perform functional, integration, regression, and load testing to ensure everything works correctly.
\n
• Educate users: Organize sessions to help users become familiar with the new system.
\n

Run

\n
• Check system readiness: Verify that the system is ready for operational use.
\n
• Launch: Officially transition to active use of SAP S/4HANA.
\n
• Ensure ongoing support: Continuously monitor solution performance to identify and resolve any issues.
\n

KPIs

To measure the success of migrating your business to SAP S/4HANA, consider setting KPIs. This will help you achieve your business goals and make timely adjustments to the strategy in case of underperformance.

System performance metrics

\n
• System availability: Monitor the reliability and accessibility of the SAP system.
\n
• Transaction response time: Measure how quickly transactions are processed to ensure a smooth user experience.
\n

Business process efficiency

\n
• Order-to-cash cycle time: Track the duration from order receipt to payment collection.
\n
• Procure-to-pay cycle time: Measure the efficiency of your procurement process.
\n
• Inventory turnover rate: Assess how quickly inventory is sold and replaced.
\n

Financial metrics

\n
• Cost savings: Calculate reductions in operational costs, IT maintenance, and overhead.
\n
• Revenue growth: Evaluate improvements linked to faster processes and better decision-making.
\n

User adoption & satisfaction

\n
• User adoption rate: Percentage of employees actively using the system compared to the total number of anticipated users.
\n
• Training completion rate: Track how many users have completed SAP training programs.
\n
• User satisfaction score: Collect feedback to gauge ease-of-use and support quality.
\n

Data quality & reporting

\n
• Accuracy of master data: Monitor error rates in key data fields.
\n
• Report generation time: Measure how quickly business insights are produced.
\n

Innovation & agility

\n
• Number of process improvements implemented: Track how many optimizations or automations have been introduced post-migration.
\n
• Time to market for new products/services: Measure how migration enables faster launches.
\n

How DataLark Makes SAP S/4HANA Migration a Success

Migrating to SAP S/4HANA is a complicated and, let’s be honest, costly procedure that requires time and resources. It’s natural that business owners want to lower the price without losing quality.

DataLark, an SAP-centric data management solution developed by LeverX, can help you significantly boost the cost efficiency and success of the SAP S/4HANA migration. By streamlining complex data migration processes, DataLark enables organizations to achieve faster, more reliable, cost-effective transitions to SAP S/4HANA, making the business case more attractive.

With DataLark, you get:

\n
• 60% acceleration of the SAP S/4HANA migration process
\n
• 90% improvement in data management transparency
\n
• 60% enhancement in business modernization and scalability speed
\n

No coding experience required! The intuitive, drag-and-drop interface allows non-technical users to design and implement data workflows. This accessibility reduces the dependency on specialized IT resources, lowering labor costs and expediting the migration process.

DataLark offers robust data extraction, cleansing, transformation, and validation capabilities. These functions ensure high-quality data migration, reducing errors and the need for costly post-migration corrections.

Supporting on-premise, cloud, and hybrid environments, DataLark provides organizations with the flexibility to choose deployment models that align with their infrastructure and cost considerations.

DataLark integrates smoothly with the SAP Migration Cockpit and supports various data loading methods, including OData and RFC APIs. This compatibility ensures a cohesive migration experience and leverages existing SAP tools, enhancing overall efficiency.

DataLark’s unified data‑migration assessment & roadmap

DataLark allows you to create a consistent data migration strategy, assess data, conduct testing, and prepare a fallback plan (in case something goes wrong) via fruitful collaboration with LeverX migration experts.

This offer is a four‑week consulting sprint. We design a safe, cost‑balanced path to move your master and transactional data from SAP ECC and non‑SAP systems into SAP S/4HANA.

As a result, the following artifacts of your SAP S/4HANA will be migration-ready:

\n
• High-level migration blueprint — target architecture, data‑flow diagrams, tooling stack
\n
• Pilot results report — load metrics, issue log, remediation actions
\n
• Data quality & cleansing report — ranked by risk and impact
\n
• Cut-over playbook — a plan with fallback scenarios
\n

Conclusion

To effectively support decision-making, it's essential to blend both qualitative and quantitative evaluations of SAP S/4HANA migration and present them in a clear, leadership-friendly format. The focus should be on highlighting key business value drivers, current pain points, opportunities for improvement, and setting practical, achievable goals. Before embarking on a migration journey, business owners should validate the business case to ensure alignment and accuracy.

We hope this has provided valuable insights into creating the business case for the SAP S/4HANA migration and demonstrated why it represents a strong investment opportunity. When implemented with the right strategy, this robust ERP solution can drive efficiency and reduce costs, enhancing productivity, customer satisfaction, and overall business performance.

Create a compelling business case for SAP S/4HANA migration, enumerating process and financial benefits, justified by ROI calculation benchmarks.

Create a compelling business case for SAP S/4HANA migration, enumerating process and financial benefits, justified by ROI calculation benchmarks.

Building a Comprehensive Business Case for SAP S/4HANA Migration

Migrating to SAP S/4HANA represents a significant milestone in any organization’s digital transformation journey. However, such a transition requires careful planning, strategic vision, and solid financial justification. Building a comprehensive business case is essential to ensure stakeholders understand the value, risks, and expected returns of this complex endeavor. 

This article explores the critical components of a compelling SAP S/4HANA migration business case, guiding you through its key strategic components, migration planning and preparation, and simplification opportunities.

Download our SAP S/4HANA migration whitepaper to explore all the migration process details for your successful transition.

Why Do You Need a Business Case for SAP S/4HANA Migration?

Migrating to SAP S/4HANA is a major strategic imperative that requires more than just technical planning: it demands a well-structured business case. A comprehensive business case plays two critical roles in the migration journey.    

Securing buy-in from key decision-makers

A clear, data-backed business case is essential for aligning executive sponsors, finance leaders, and other key stakeholders. It translates the technical benefits of S/4HANA into strategic business value, demonstrating how the migration supports organizational goals such as increased agility, improved operational efficiency, and future readiness. This alignment is crucial for securing the budget, resources, and organizational support necessary to move forward confidently.

Establishing a benchmark for measuring ROI

The SAP S/4HANA business case, besides serving as an investment justification, also acts as a benchmark to measure the overall success of the SAP S/4HANA migration. Clearly defining expected outcomes, such as cost savings, performance improvements, or process enhancements, provides a baseline against which post-migration results can be measured. Calculating the ROI of SAP S/4HANA migration ensures payback and allows optimizing investment over time.

Key Components of an SAP S/4HANA Business Case

A comprehensive and persuasive business case for SAP S/4HANA migration covers many aspects of your company’s current state and performance, defining benchmarks for future success measurement. When establishing a compelling case, thoroughly consider the elements detailed below.

Strategic objectives

Clearly setting strategic objectives is essential for the SAP S/4HANA migration business case, as they will help you establish your goals, adhere to the migration plan, and prove the necessity of this investment to key stakeholders. Usually, strategic objectives include:

Alignment with corporate goals

\n
• Better customer experience: Offer unique customer journeys and improve satisfaction with the help of real-time data processing and predictive analytics.
\n
• M&A readiness: Provide seamless mergers and acquisitions due to unified and streamlined business processes.
\n
• Global expansion: Ensure smooth worldwide operations and compliance with key regulations with a scalable and flexible ERP.
\n

Business drivers

\n
• Agility: Promptly adjust operations in response to market fluctuations and changing trends with flexible processes.
\n
• Real-time analytics: Access relevant data for informed decision-making and proactive issue resolution.
\n
• Simplified operations: Streamline business processes to reduce operational burden and boost efficiency.
\n

Competition

\n
• Global digital transformation: Businesses worldwide actively implement new technologies and switch to up-to-date ERP solutions to gain a competitive advantage in hectic, fluctuating markets.
\n
• Adoption of SAP S/4HANA: SAP S/4HANA offers companies a set of competitive advantages, such as access to real-time analytics, process automation, system agility, and AI capabilities.
\n

Current state assessment

A comprehensive assessment of your current IT landscape is crucial to highlight the limits of a legacy system, find drawbacks of your current processes, and spot potential compliance risks. This creates a baseline for success measurement after migration, showcasing the advantages of SAP S/4HANA.

Strategic Advantages of SAP S/4HANA

\n
• Real-time analytics and data processing that support data-driven decision-making.
\n
• Simplified data model that lowers data redundancy and data footprint.
\n
• Intuitive SAP Fiori interface, which enhances user experience and makes the system available on mobile devices.
\n
• Advanced functionalities and innovations, such as embedded analytics, AI, machine learning, and robotic process automation.
\n
• Better integration with cloud and digital technologies due to hybrid and cloud deployment.
\n
• Seamless integration with SAP cloud portfolio (SAP Cloud Platform, SAP Ariba).
\n
• Improved performance and scalability due to the HANA in-memory engine.
\n
• Simplified IT landscape and lower total cost of ownership (TCO).
\n

Financial justification

Proving that the SAP S/4HANA migration is financially reasonable is crucial for persuading key stakeholders to start the process. It will also help you adhere to your financial plan and track the payback period, reaching your goals as a result. Financial justification comprises the following steps:

ROI calculations and metrics

Define clear metrics to track the overall success and ROI of SAP S/4HANA migration:

\n
• Cost savings: Set benchmarks to track expense reduction caused by IT maintenance, process inefficiencies, and system downtime.
\n
• Operational efficiency: Track cycle times, fulfillment rates, and other key performance indicators that are relevant to your industry.
\n
• User adoption rates: Evaluate how effectively employees are using the system to perform their roles.
\n

TCO reduction plan in 5 -10 years

According to research by Forrester, organizations can greatly reduce TCO (total cost of ownership) after implementing SAP Cloud ERP.

\n
• Shifting from local on-premise ERP solutions eliminates related infrastructure, maintenance, and labor expenses, cutting overall maintenance costs.
\n
• SAP S/4HANA improves efficiency by up to 30% for users who spend the majority of their working time interacting with the ERP system, and by up to 15% for users who only interact with the ERP system occasionally.
\n
• Switching to in-built SAP Cloud ERP security features instead of legacy recovery platforms and security solutions also saves costs on third-party tools.
\n

Payback period and breakeven point

Set the financial recovery timeline to clearly showcase the payback benchmarks:

\n
• Payback period: The typical timeframe for recovering the initial investment in SAP S/4HANA ranges from 18 to 36 months.
\n
• Breakeven point: The cumulative benefits from the migration offset the total costs incurred within 2 to 3 years post-migration.
\n
• Ongoing value generation: Besides breakeven, companies may expect growing ROI due to scalable operations, enhanced data-driven decision-making, and reduced IT overhead.
\n

Risk assessment and mitigation plan

Evaluate all the potential risks while migrating to SAP S/4HANA. This will help you prepare your options if the transition does not go according to plan. Additionally, work out a migration plan that will help your business gradually adjust to the new system, providing a smoother transition.

Potential risks

\n
• Implementation challenges: The complexity of data migration and system integration.
\n
• User adoption: Resistance to change and inadequate training.
\n
• Operational disruptions: Downtime during transition.
\n

Mitigation strategies

\n
• Comprehensive planning: Detailed project timelines and resource allocation.
\n
• Change management: Effective communication and training programs.
\n
• Phased implementation: Gradual rollout to minimize disruptions.
\n

Implementation roadmap

One of the best practices for implementing SAP S/4HANA is the SAP Activate methodology, which breaks down the implementation process into six essential steps:

Discover

\n
• Evaluate current processes: Assess the existing needs and processes of your business.
\n
• Define technical requirements: Create a detailed specification of the functional and technical needs of the new system, in our case, SAP S/4HANA.
\n

Prepare

\n
• Set goals and objectives: Establish and agree on the goals for the project.
\n
• Assemble the project team: Appoint team members and define their roles and responsibilities.
\n
• Develop the project plan: Create a comprehensive plan that outlines project phases, timelines, resources, and KPIs.
\n
• Determine budget: Estimate and approve the budget.
\n
• Prepare specifications: Develop the technical and functional specifications for the development team.
\n

Explore

\n
• Ensure that business requirements are met: Check that SAP S/4HANA migration aligns with business requirements and project objectives.
\n
• alidate data:V Verify the accuracy and compliance of the data.
\n

Realize

\n
• Migrate data: Transfer data from existing systems to the new one.
\n
• Configure the system: Set up the solution according to requirements and specifications.
\n
• Customize the system: Develop additional features and modules, if the standard solution does not meet all needs.
\n
• Integrate with your business: Configure SAP S/4HANA to work with other IT systems and applications in your environment.
\n

Deploy

\n
• Test the system: Perform functional, integration, regression, and load testing to ensure everything works correctly.
\n
• Educate users: Organize sessions to help users become familiar with the new system.
\n

Run

\n
• Check system readiness: Verify that the system is ready for operational use.
\n
• Launch: Officially transition to active use of SAP S/4HANA.
\n
• Ensure ongoing support: Continuously monitor solution performance to identify and resolve any issues.
\n

KPIs

To measure the success of migrating your business to SAP S/4HANA, consider setting KPIs. This will help you achieve your business goals and make timely adjustments to the strategy in case of underperformance.

System performance metrics

\n
• System availability: Monitor the reliability and accessibility of the SAP system.
\n
• Transaction response time: Measure how quickly transactions are processed to ensure a smooth user experience.
\n

Business process efficiency

\n
• Order-to-cash cycle time: Track the duration from order receipt to payment collection.
\n
• Procure-to-pay cycle time: Measure the efficiency of your procurement process.
\n
• Inventory turnover rate: Assess how quickly inventory is sold and replaced.
\n

Financial metrics

\n
• Cost savings: Calculate reductions in operational costs, IT maintenance, and overhead.
\n
• Revenue growth: Evaluate improvements linked to faster processes and better decision-making.
\n

User adoption & satisfaction

\n
• User adoption rate: Percentage of employees actively using the system compared to the total number of anticipated users.
\n
• Training completion rate: Track how many users have completed SAP training programs.
\n
• User satisfaction score: Collect feedback to gauge ease-of-use and support quality.
\n

Data quality & reporting

\n
• Accuracy of master data: Monitor error rates in key data fields.
\n
• Report generation time: Measure how quickly business insights are produced.
\n

Innovation & agility

\n
• Number of process improvements implemented: Track how many optimizations or automations have been introduced post-migration.
\n
• Time to market for new products/services: Measure how migration enables faster launches.
\n

How DataLark Makes SAP S/4HANA Migration a Success

Migrating to SAP S/4HANA is a complicated and, let’s be honest, costly procedure that requires time and resources. It’s natural that business owners want to lower the price without losing quality.

DataLark, an SAP-centric data management solution developed by LeverX, can help you significantly boost the cost efficiency and success of the SAP S/4HANA migration. By streamlining complex data migration processes, DataLark enables organizations to achieve faster, more reliable, cost-effective transitions to SAP S/4HANA, making the business case more attractive.

With DataLark, you get:

\n
• 60% acceleration of the SAP S/4HANA migration process
\n
• 90% improvement in data management transparency
\n
• 60% enhancement in business modernization and scalability speed
\n

No coding experience required! The intuitive, drag-and-drop interface allows non-technical users to design and implement data workflows. This accessibility reduces the dependency on specialized IT resources, lowering labor costs and expediting the migration process.

DataLark offers robust data extraction, cleansing, transformation, and validation capabilities. These functions ensure high-quality data migration, reducing errors and the need for costly post-migration corrections.

Supporting on-premise, cloud, and hybrid environments, DataLark provides organizations with the flexibility to choose deployment models that align with their infrastructure and cost considerations.

DataLark integrates smoothly with the SAP Migration Cockpit and supports various data loading methods, including OData and RFC APIs. This compatibility ensures a cohesive migration experience and leverages existing SAP tools, enhancing overall efficiency.

DataLark’s unified data‑migration assessment & roadmap

DataLark allows you to create a consistent data migration strategy, assess data, conduct testing, and prepare a fallback plan (in case something goes wrong) via fruitful collaboration with LeverX migration experts.

This offer is a four‑week consulting sprint. We design a safe, cost‑balanced path to move your master and transactional data from SAP ECC and non‑SAP systems into SAP S/4HANA.

As a result, the following artifacts of your SAP S/4HANA will be migration-ready:

\n
• High-level migration blueprint — target architecture, data‑flow diagrams, tooling stack
\n
• Pilot results report — load metrics, issue log, remediation actions
\n
• Data quality & cleansing report — ranked by risk and impact
\n
• Cut-over playbook — a plan with fallback scenarios
\n

Conclusion

To effectively support decision-making, it's essential to blend both qualitative and quantitative evaluations of SAP S/4HANA migration and present them in a clear, leadership-friendly format. The focus should be on highlighting key business value drivers, current pain points, opportunities for improvement, and setting practical, achievable goals. Before embarking on a migration journey, business owners should validate the business case to ensure alignment and accuracy.

We hope this has provided valuable insights into creating the business case for the SAP S/4HANA migration and demonstrated why it represents a strong investment opportunity. When implemented with the right strategy, this robust ERP solution can drive efficiency and reduce costs, enhancing productivity, customer satisfaction, and overall business performance.

Create a compelling business case for SAP S/4HANA migration, enumerating process and financial benefits, justified by ROI calculation benchmarks.

Building a Comprehensive Business Case for SAP S/4HANA Migration

Migrating to SAP S/4HANA represents a significant milestone in any organization’s digital transformation journey. However, such a transition requires careful planning, strategic vision, and solid financial justification. Building a comprehensive business case is essential to ensure stakeholders understand the value, risks, and expected returns of this complex endeavor. 

This article explores the critical components of a compelling SAP S/4HANA migration business case, guiding you through its key strategic components, migration planning and preparation, and simplification opportunities.

Download our SAP S/4HANA migration whitepaper to explore all the migration process details for your successful transition.

Why Do You Need a Business Case for SAP S/4HANA Migration?

Migrating to SAP S/4HANA is a major strategic imperative that requires more than just technical planning: it demands a well-structured business case. A comprehensive business case plays two critical roles in the migration journey.    

Securing buy-in from key decision-makers

A clear, data-backed business case is essential for aligning executive sponsors, finance leaders, and other key stakeholders. It translates the technical benefits of S/4HANA into strategic business value, demonstrating how the migration supports organizational goals such as increased agility, improved operational efficiency, and future readiness. This alignment is crucial for securing the budget, resources, and organizational support necessary to move forward confidently.

Establishing a benchmark for measuring ROI

The SAP S/4HANA business case, besides serving as an investment justification, also acts as a benchmark to measure the overall success of the SAP S/4HANA migration. Clearly defining expected outcomes, such as cost savings, performance improvements, or process enhancements, provides a baseline against which post-migration results can be measured. Calculating the ROI of SAP S/4HANA migration ensures payback and allows optimizing investment over time.

Key Components of an SAP S/4HANA Business Case

A comprehensive and persuasive business case for SAP S/4HANA migration covers many aspects of your company’s current state and performance, defining benchmarks for future success measurement. When establishing a compelling case, thoroughly consider the elements detailed below.

Strategic objectives

Clearly setting strategic objectives is essential for the SAP S/4HANA migration business case, as they will help you establish your goals, adhere to the migration plan, and prove the necessity of this investment to key stakeholders. Usually, strategic objectives include:

Alignment with corporate goals

\n
• Better customer experience: Offer unique customer journeys and improve satisfaction with the help of real-time data processing and predictive analytics.
\n
• M&A readiness: Provide seamless mergers and acquisitions due to unified and streamlined business processes.
\n
• Global expansion: Ensure smooth worldwide operations and compliance with key regulations with a scalable and flexible ERP.
\n

Business drivers

\n
• Agility: Promptly adjust operations in response to market fluctuations and changing trends with flexible processes.
\n
• Real-time analytics: Access relevant data for informed decision-making and proactive issue resolution.
\n
• Simplified operations: Streamline business processes to reduce operational burden and boost efficiency.
\n

Competition

\n
• Global digital transformation: Businesses worldwide actively implement new technologies and switch to up-to-date ERP solutions to gain a competitive advantage in hectic, fluctuating markets.
\n
• Adoption of SAP S/4HANA: SAP S/4HANA offers companies a set of competitive advantages, such as access to real-time analytics, process automation, system agility, and AI capabilities.
\n

Current state assessment

A comprehensive assessment of your current IT landscape is crucial to highlight the limits of a legacy system, find drawbacks of your current processes, and spot potential compliance risks. This creates a baseline for success measurement after migration, showcasing the advantages of SAP S/4HANA.

Strategic Advantages of SAP S/4HANA

\n
• Real-time analytics and data processing that support data-driven decision-making.
\n
• Simplified data model that lowers data redundancy and data footprint.
\n
• Intuitive SAP Fiori interface, which enhances user experience and makes the system available on mobile devices.
\n
• Advanced functionalities and innovations, such as embedded analytics, AI, machine learning, and robotic process automation.
\n
• Better integration with cloud and digital technologies due to hybrid and cloud deployment.
\n
• Seamless integration with SAP cloud portfolio (SAP Cloud Platform, SAP Ariba).
\n
• Improved performance and scalability due to the HANA in-memory engine.
\n
• Simplified IT landscape and lower total cost of ownership (TCO).
\n

Financial justification

Proving that the SAP S/4HANA migration is financially reasonable is crucial for persuading key stakeholders to start the process. It will also help you adhere to your financial plan and track the payback period, reaching your goals as a result. Financial justification comprises the following steps:

ROI calculations and metrics

Define clear metrics to track the overall success and ROI of SAP S/4HANA migration:

\n
• Cost savings: Set benchmarks to track expense reduction caused by IT maintenance, process inefficiencies, and system downtime.
\n
• Operational efficiency: Track cycle times, fulfillment rates, and other key performance indicators that are relevant to your industry.
\n
• User adoption rates: Evaluate how effectively employees are using the system to perform their roles.
\n

TCO reduction plan in 5 -10 years

According to research by Forrester, organizations can greatly reduce TCO (total cost of ownership) after implementing SAP Cloud ERP.

\n
• Shifting from local on-premise ERP solutions eliminates related infrastructure, maintenance, and labor expenses, cutting overall maintenance costs.
\n
• SAP S/4HANA improves efficiency by up to 30% for users who spend the majority of their working time interacting with the ERP system, and by up to 15% for users who only interact with the ERP system occasionally.
\n
• Switching to in-built SAP Cloud ERP security features instead of legacy recovery platforms and security solutions also saves costs on third-party tools.
\n

Payback period and breakeven point

Set the financial recovery timeline to clearly showcase the payback benchmarks:

\n
• Payback period: The typical timeframe for recovering the initial investment in SAP S/4HANA ranges from 18 to 36 months.
\n
• Breakeven point: The cumulative benefits from the migration offset the total costs incurred within 2 to 3 years post-migration.
\n
• Ongoing value generation: Besides breakeven, companies may expect growing ROI due to scalable operations, enhanced data-driven decision-making, and reduced IT overhead.
\n

Risk assessment and mitigation plan

Evaluate all the potential risks while migrating to SAP S/4HANA. This will help you prepare your options if the transition does not go according to plan. Additionally, work out a migration plan that will help your business gradually adjust to the new system, providing a smoother transition.

Potential risks

\n
• Implementation challenges: The complexity of data migration and system integration.
\n
• User adoption: Resistance to change and inadequate training.
\n
• Operational disruptions: Downtime during transition.
\n

Mitigation strategies

\n
• Comprehensive planning: Detailed project timelines and resource allocation.
\n
• Change management: Effective communication and training programs.
\n
• Phased implementation: Gradual rollout to minimize disruptions.
\n

Implementation roadmap

One of the best practices for implementing SAP S/4HANA is the SAP Activate methodology, which breaks down the implementation process into six essential steps:

Discover

\n
• Evaluate current processes: Assess the existing needs and processes of your business.
\n
• Define technical requirements: Create a detailed specification of the functional and technical needs of the new system, in our case, SAP S/4HANA.
\n

Prepare

\n
• Set goals and objectives: Establish and agree on the goals for the project.
\n
• Assemble the project team: Appoint team members and define their roles and responsibilities.
\n
• Develop the project plan: Create a comprehensive plan that outlines project phases, timelines, resources, and KPIs.
\n
• Determine budget: Estimate and approve the budget.
\n
• Prepare specifications: Develop the technical and functional specifications for the development team.
\n

Explore

\n
• Ensure that business requirements are met: Check that SAP S/4HANA migration aligns with business requirements and project objectives.
\n
• alidate data:V Verify the accuracy and compliance of the data.
\n

Realize

\n
• Migrate data: Transfer data from existing systems to the new one.
\n
• Configure the system: Set up the solution according to requirements and specifications.
\n
• Customize the system: Develop additional features and modules, if the standard solution does not meet all needs.
\n
• Integrate with your business: Configure SAP S/4HANA to work with other IT systems and applications in your environment.
\n

Deploy

\n
• Test the system: Perform functional, integration, regression, and load testing to ensure everything works correctly.
\n
• Educate users: Organize sessions to help users become familiar with the new system.
\n

Run

\n
• Check system readiness: Verify that the system is ready for operational use.
\n
• Launch: Officially transition to active use of SAP S/4HANA.
\n
• Ensure ongoing support: Continuously monitor solution performance to identify and resolve any issues.
\n

KPIs

To measure the success of migrating your business to SAP S/4HANA, consider setting KPIs. This will help you achieve your business goals and make timely adjustments to the strategy in case of underperformance.

System performance metrics

\n
• System availability: Monitor the reliability and accessibility of the SAP system.
\n
• Transaction response time: Measure how quickly transactions are processed to ensure a smooth user experience.
\n

Business process efficiency

\n
• Order-to-cash cycle time: Track the duration from order receipt to payment collection.
\n
• Procure-to-pay cycle time: Measure the efficiency of your procurement process.
\n
• Inventory turnover rate: Assess how quickly inventory is sold and replaced.
\n

Financial metrics

\n
• Cost savings: Calculate reductions in operational costs, IT maintenance, and overhead.
\n
• Revenue growth: Evaluate improvements linked to faster processes and better decision-making.
\n

User adoption & satisfaction

\n
• User adoption rate: Percentage of employees actively using the system compared to the total number of anticipated users.
\n
• Training completion rate: Track how many users have completed SAP training programs.
\n
• User satisfaction score: Collect feedback to gauge ease-of-use and support quality.
\n

Data quality & reporting

\n
• Accuracy of master data: Monitor error rates in key data fields.
\n
• Report generation time: Measure how quickly business insights are produced.
\n

Innovation & agility

\n
• Number of process improvements implemented: Track how many optimizations or automations have been introduced post-migration.
\n
• Time to market for new products/services: Measure how migration enables faster launches.
\n

How DataLark Makes SAP S/4HANA Migration a Success

Migrating to SAP S/4HANA is a complicated and, let’s be honest, costly procedure that requires time and resources. It’s natural that business owners want to lower the price without losing quality.

DataLark, an SAP-centric data management solution developed by LeverX, can help you significantly boost the cost efficiency and success of the SAP S/4HANA migration. By streamlining complex data migration processes, DataLark enables organizations to achieve faster, more reliable, cost-effective transitions to SAP S/4HANA, making the business case more attractive.

With DataLark, you get:

\n
• 60% acceleration of the SAP S/4HANA migration process
\n
• 90% improvement in data management transparency
\n
• 60% enhancement in business modernization and scalability speed
\n

No coding experience required! The intuitive, drag-and-drop interface allows non-technical users to design and implement data workflows. This accessibility reduces the dependency on specialized IT resources, lowering labor costs and expediting the migration process.

DataLark offers robust data extraction, cleansing, transformation, and validation capabilities. These functions ensure high-quality data migration, reducing errors and the need for costly post-migration corrections.

Supporting on-premise, cloud, and hybrid environments, DataLark provides organizations with the flexibility to choose deployment models that align with their infrastructure and cost considerations.

DataLark integrates smoothly with the SAP Migration Cockpit and supports various data loading methods, including OData and RFC APIs. This compatibility ensures a cohesive migration experience and leverages existing SAP tools, enhancing overall efficiency.

DataLark’s unified data‑migration assessment & roadmap

DataLark allows you to create a consistent data migration strategy, assess data, conduct testing, and prepare a fallback plan (in case something goes wrong) via fruitful collaboration with LeverX migration experts.

This offer is a four‑week consulting sprint. We design a safe, cost‑balanced path to move your master and transactional data from SAP ECC and non‑SAP systems into SAP S/4HANA.

As a result, the following artifacts of your SAP S/4HANA will be migration-ready:

\n
• High-level migration blueprint — target architecture, data‑flow diagrams, tooling stack
\n
• Pilot results report — load metrics, issue log, remediation actions
\n
• Data quality & cleansing report — ranked by risk and impact
\n
• Cut-over playbook — a plan with fallback scenarios
\n

Conclusion

To effectively support decision-making, it's essential to blend both qualitative and quantitative evaluations of SAP S/4HANA migration and present them in a clear, leadership-friendly format. The focus should be on highlighting key business value drivers, current pain points, opportunities for improvement, and setting practical, achievable goals. Before embarking on a migration journey, business owners should validate the business case to ensure alignment and accuracy.

We hope this has provided valuable insights into creating the business case for the SAP S/4HANA migration and demonstrated why it represents a strong investment opportunity. When implemented with the right strategy, this robust ERP solution can drive efficiency and reduce costs, enhancing productivity, customer satisfaction, and overall business performance.

Create a compelling business case for SAP S/4HANA migration, enumerating process and financial benefits, justified by ROI calculation benchmarks.

Create a compelling business case for SAP S/4HANA migration, enumerating process and financial benefits, justified by ROI calculation benchmarks.

Create a compelling business case for SAP S/4HANA migration, enumerating process and financial benefits, justified by ROI calculation benchmarks.

Building a Comprehensive Business Case for SAP S/4HANA Migration

Migrating to SAP S/4HANA represents a significant milestone in any organization’s digital transformation journey. However, such a transition requires careful planning, strategic vision, and solid financial justification. Building a comprehensive business case is essential to ensure stakeholders understand the value, risks, and expected returns of this complex endeavor. 

This article explores the critical components of a compelling SAP S/4HANA migration business case, guiding you through its key strategic components, migration planning and preparation, and simplification opportunities.

Download our SAP S/4HANA migration whitepaper to explore all the migration process details for your successful transition.

Why Do You Need a Business Case for SAP S/4HANA Migration?

Migrating to SAP S/4HANA is a major strategic imperative that requires more than just technical planning: it demands a well-structured business case. A comprehensive business case plays two critical roles in the migration journey.    

Securing buy-in from key decision-makers

A clear, data-backed business case is essential for aligning executive sponsors, finance leaders, and other key stakeholders. It translates the technical benefits of S/4HANA into strategic business value, demonstrating how the migration supports organizational goals such as increased agility, improved operational efficiency, and future readiness. This alignment is crucial for securing the budget, resources, and organizational support necessary to move forward confidently.

Establishing a benchmark for measuring ROI

The SAP S/4HANA business case, besides serving as an investment justification, also acts as a benchmark to measure the overall success of the SAP S/4HANA migration. Clearly defining expected outcomes, such as cost savings, performance improvements, or process enhancements, provides a baseline against which post-migration results can be measured. Calculating the ROI of SAP S/4HANA migration ensures payback and allows optimizing investment over time.

Key Components of an SAP S/4HANA Business Case

A comprehensive and persuasive business case for SAP S/4HANA migration covers many aspects of your company’s current state and performance, defining benchmarks for future success measurement. When establishing a compelling case, thoroughly consider the elements detailed below.

Strategic objectives

Clearly setting strategic objectives is essential for the SAP S/4HANA migration business case, as they will help you establish your goals, adhere to the migration plan, and prove the necessity of this investment to key stakeholders. Usually, strategic objectives include:

Alignment with corporate goals

\n
• Better customer experience: Offer unique customer journeys and improve satisfaction with the help of real-time data processing and predictive analytics.
\n
• M&A readiness: Provide seamless mergers and acquisitions due to unified and streamlined business processes.
\n
• Global expansion: Ensure smooth worldwide operations and compliance with key regulations with a scalable and flexible ERP.
\n

Business drivers

\n
• Agility: Promptly adjust operations in response to market fluctuations and changing trends with flexible processes.
\n
• Real-time analytics: Access relevant data for informed decision-making and proactive issue resolution.
\n
• Simplified operations: Streamline business processes to reduce operational burden and boost efficiency.
\n

Competition

\n
• Global digital transformation: Businesses worldwide actively implement new technologies and switch to up-to-date ERP solutions to gain a competitive advantage in hectic, fluctuating markets.
\n
• Adoption of SAP S/4HANA: SAP S/4HANA offers companies a set of competitive advantages, such as access to real-time analytics, process automation, system agility, and AI capabilities.
\n

Current state assessment

A comprehensive assessment of your current IT landscape is crucial to highlight the limits of a legacy system, find drawbacks of your current processes, and spot potential compliance risks. This creates a baseline for success measurement after migration, showcasing the advantages of SAP S/4HANA.

Strategic Advantages of SAP S/4HANA

\n
• Real-time analytics and data processing that support data-driven decision-making.
\n
• Simplified data model that lowers data redundancy and data footprint.
\n
• Intuitive SAP Fiori interface, which enhances user experience and makes the system available on mobile devices.
\n
• Advanced functionalities and innovations, such as embedded analytics, AI, machine learning, and robotic process automation.
\n
• Better integration with cloud and digital technologies due to hybrid and cloud deployment.
\n
• Seamless integration with SAP cloud portfolio (SAP Cloud Platform, SAP Ariba).
\n
• Improved performance and scalability due to the HANA in-memory engine.
\n
• Simplified IT landscape and lower total cost of ownership (TCO).
\n

Financial justification

Proving that the SAP S/4HANA migration is financially reasonable is crucial for persuading key stakeholders to start the process. It will also help you adhere to your financial plan and track the payback period, reaching your goals as a result. Financial justification comprises the following steps:

ROI calculations and metrics

Define clear metrics to track the overall success and ROI of SAP S/4HANA migration:

\n
• Cost savings: Set benchmarks to track expense reduction caused by IT maintenance, process inefficiencies, and system downtime.
\n
• Operational efficiency: Track cycle times, fulfillment rates, and other key performance indicators that are relevant to your industry.
\n
• User adoption rates: Evaluate how effectively employees are using the system to perform their roles.
\n

TCO reduction plan in 5 -10 years

According to research by Forrester, organizations can greatly reduce TCO (total cost of ownership) after implementing SAP Cloud ERP.

\n
• Shifting from local on-premise ERP solutions eliminates related infrastructure, maintenance, and labor expenses, cutting overall maintenance costs.
\n
• SAP S/4HANA improves efficiency by up to 30% for users who spend the majority of their working time interacting with the ERP system, and by up to 15% for users who only interact with the ERP system occasionally.
\n
• Switching to in-built SAP Cloud ERP security features instead of legacy recovery platforms and security solutions also saves costs on third-party tools.
\n

Payback period and breakeven point

Set the financial recovery timeline to clearly showcase the payback benchmarks:

\n
• Payback period: The typical timeframe for recovering the initial investment in SAP S/4HANA ranges from 18 to 36 months.
\n
• Breakeven point: The cumulative benefits from the migration offset the total costs incurred within 2 to 3 years post-migration.
\n
• Ongoing value generation: Besides breakeven, companies may expect growing ROI due to scalable operations, enhanced data-driven decision-making, and reduced IT overhead.
\n

Risk assessment and mitigation plan

Evaluate all the potential risks while migrating to SAP S/4HANA. This will help you prepare your options if the transition does not go according to plan. Additionally, work out a migration plan that will help your business gradually adjust to the new system, providing a smoother transition.

Potential risks

\n
• Implementation challenges: The complexity of data migration and system integration.
\n
• User adoption: Resistance to change and inadequate training.
\n
• Operational disruptions: Downtime during transition.
\n

Mitigation strategies

\n
• Comprehensive planning: Detailed project timelines and resource allocation.
\n
• Change management: Effective communication and training programs.
\n
• Phased implementation: Gradual rollout to minimize disruptions.
\n

Implementation roadmap

One of the best practices for implementing SAP S/4HANA is the SAP Activate methodology, which breaks down the implementation process into six essential steps:

Discover

\n
• Evaluate current processes: Assess the existing needs and processes of your business.
\n
• Define technical requirements: Create a detailed specification of the functional and technical needs of the new system, in our case, SAP S/4HANA.
\n

Prepare

\n
• Set goals and objectives: Establish and agree on the goals for the project.
\n
• Assemble the project team: Appoint team members and define their roles and responsibilities.
\n
• Develop the project plan: Create a comprehensive plan that outlines project phases, timelines, resources, and KPIs.
\n
• Determine budget: Estimate and approve the budget.
\n
• Prepare specifications: Develop the technical and functional specifications for the development team.
\n

Explore

\n
• Ensure that business requirements are met: Check that SAP S/4HANA migration aligns with business requirements and project objectives.
\n
• alidate data:V Verify the accuracy and compliance of the data.
\n

Realize

\n
• Migrate data: Transfer data from existing systems to the new one.
\n
• Configure the system: Set up the solution according to requirements and specifications.
\n
• Customize the system: Develop additional features and modules, if the standard solution does not meet all needs.
\n
• Integrate with your business: Configure SAP S/4HANA to work with other IT systems and applications in your environment.
\n

Deploy

\n
• Test the system: Perform functional, integration, regression, and load testing to ensure everything works correctly.
\n
• Educate users: Organize sessions to help users become familiar with the new system.
\n

Run

\n
• Check system readiness: Verify that the system is ready for operational use.
\n
• Launch: Officially transition to active use of SAP S/4HANA.
\n
• Ensure ongoing support: Continuously monitor solution performance to identify and resolve any issues.
\n

KPIs

To measure the success of migrating your business to SAP S/4HANA, consider setting KPIs. This will help you achieve your business goals and make timely adjustments to the strategy in case of underperformance.

System performance metrics

\n
• System availability: Monitor the reliability and accessibility of the SAP system.
\n
• Transaction response time: Measure how quickly transactions are processed to ensure a smooth user experience.
\n

Business process efficiency

\n
• Order-to-cash cycle time: Track the duration from order receipt to payment collection.
\n
• Procure-to-pay cycle time: Measure the efficiency of your procurement process.
\n
• Inventory turnover rate: Assess how quickly inventory is sold and replaced.
\n

Financial metrics

\n
• Cost savings: Calculate reductions in operational costs, IT maintenance, and overhead.
\n
• Revenue growth: Evaluate improvements linked to faster processes and better decision-making.
\n

User adoption & satisfaction

\n
• User adoption rate: Percentage of employees actively using the system compared to the total number of anticipated users.
\n
• Training completion rate: Track how many users have completed SAP training programs.
\n
• User satisfaction score: Collect feedback to gauge ease-of-use and support quality.
\n

Data quality & reporting

\n
• Accuracy of master data: Monitor error rates in key data fields.
\n
• Report generation time: Measure how quickly business insights are produced.
\n

Innovation & agility

\n
• Number of process improvements implemented: Track how many optimizations or automations have been introduced post-migration.
\n
• Time to market for new products/services: Measure how migration enables faster launches.
\n

How DataLark Makes SAP S/4HANA Migration a Success

Migrating to SAP S/4HANA is a complicated and, let’s be honest, costly procedure that requires time and resources. It’s natural that business owners want to lower the price without losing quality.

DataLark, an SAP-centric data management solution developed by LeverX, can help you significantly boost the cost efficiency and success of the SAP S/4HANA migration. By streamlining complex data migration processes, DataLark enables organizations to achieve faster, more reliable, cost-effective transitions to SAP S/4HANA, making the business case more attractive.

With DataLark, you get:

\n
• 60% acceleration of the SAP S/4HANA migration process
\n
• 90% improvement in data management transparency
\n
• 60% enhancement in business modernization and scalability speed
\n

No coding experience required! The intuitive, drag-and-drop interface allows non-technical users to design and implement data workflows. This accessibility reduces the dependency on specialized IT resources, lowering labor costs and expediting the migration process.

DataLark offers robust data extraction, cleansing, transformation, and validation capabilities. These functions ensure high-quality data migration, reducing errors and the need for costly post-migration corrections.

Supporting on-premise, cloud, and hybrid environments, DataLark provides organizations with the flexibility to choose deployment models that align with their infrastructure and cost considerations.

DataLark integrates smoothly with the SAP Migration Cockpit and supports various data loading methods, including OData and RFC APIs. This compatibility ensures a cohesive migration experience and leverages existing SAP tools, enhancing overall efficiency.

DataLark’s unified data‑migration assessment & roadmap

DataLark allows you to create a consistent data migration strategy, assess data, conduct testing, and prepare a fallback plan (in case something goes wrong) via fruitful collaboration with LeverX migration experts.

This offer is a four‑week consulting sprint. We design a safe, cost‑balanced path to move your master and transactional data from SAP ECC and non‑SAP systems into SAP S/4HANA.

As a result, the following artifacts of your SAP S/4HANA will be migration-ready:

\n
• High-level migration blueprint — target architecture, data‑flow diagrams, tooling stack
\n
• Pilot results report — load metrics, issue log, remediation actions
\n
• Data quality & cleansing report — ranked by risk and impact
\n
• Cut-over playbook — a plan with fallback scenarios
\n

Conclusion

To effectively support decision-making, it's essential to blend both qualitative and quantitative evaluations of SAP S/4HANA migration and present them in a clear, leadership-friendly format. The focus should be on highlighting key business value drivers, current pain points, opportunities for improvement, and setting practical, achievable goals. Before embarking on a migration journey, business owners should validate the business case to ensure alignment and accuracy.

We hope this has provided valuable insights into creating the business case for the SAP S/4HANA migration and demonstrated why it represents a strong investment opportunity. When implemented with the right strategy, this robust ERP solution can drive efficiency and reduce costs, enhancing productivity, customer satisfaction, and overall business performance.

Create a compelling business case for SAP S/4HANA migration, enumerating process and financial benefits, justified by ROI calculation benchmarks.

Create a compelling business case for SAP S/4HANA migration, enumerating process and financial benefits, justified by ROI calculation benchmarks.

Create a compelling business case for SAP S/4HANA migration, enumerating process and financial benefits, justified by ROI calculation benchmarks.

Building a Comprehensive Business Case for SAP S/4HANA Migration

Migrating to SAP S/4HANA represents a significant milestone in any organization’s digital transformation journey. However, such a transition requires careful planning, strategic vision, and solid financial justification. Building a comprehensive business case is essential to ensure stakeholders understand the value, risks, and expected returns of this complex endeavor. 

This article explores the critical components of a compelling SAP S/4HANA migration business case, guiding you through its key strategic components, migration planning and preparation, and simplification opportunities.

Download our SAP S/4HANA migration whitepaper to explore all the migration process details for your successful transition.

Why Do You Need a Business Case for SAP S/4HANA Migration?

Migrating to SAP S/4HANA is a major strategic imperative that requires more than just technical planning: it demands a well-structured business case. A comprehensive business case plays two critical roles in the migration journey.    

Securing buy-in from key decision-makers

A clear, data-backed business case is essential for aligning executive sponsors, finance leaders, and other key stakeholders. It translates the technical benefits of S/4HANA into strategic business value, demonstrating how the migration supports organizational goals such as increased agility, improved operational efficiency, and future readiness. This alignment is crucial for securing the budget, resources, and organizational support necessary to move forward confidently.

Establishing a benchmark for measuring ROI

The SAP S/4HANA business case, besides serving as an investment justification, also acts as a benchmark to measure the overall success of the SAP S/4HANA migration. Clearly defining expected outcomes, such as cost savings, performance improvements, or process enhancements, provides a baseline against which post-migration results can be measured. Calculating the ROI of SAP S/4HANA migration ensures payback and allows optimizing investment over time.

Key Components of an SAP S/4HANA Business Case

A comprehensive and persuasive business case for SAP S/4HANA migration covers many aspects of your company’s current state and performance, defining benchmarks for future success measurement. When establishing a compelling case, thoroughly consider the elements detailed below.

Strategic objectives

Clearly setting strategic objectives is essential for the SAP S/4HANA migration business case, as they will help you establish your goals, adhere to the migration plan, and prove the necessity of this investment to key stakeholders. Usually, strategic objectives include:

Alignment with corporate goals

\n
• Better customer experience: Offer unique customer journeys and improve satisfaction with the help of real-time data processing and predictive analytics.
\n
• M&A readiness: Provide seamless mergers and acquisitions due to unified and streamlined business processes.
\n
• Global expansion: Ensure smooth worldwide operations and compliance with key regulations with a scalable and flexible ERP.
\n

Business drivers

\n
• Agility: Promptly adjust operations in response to market fluctuations and changing trends with flexible processes.
\n
• Real-time analytics: Access relevant data for informed decision-making and proactive issue resolution.
\n
• Simplified operations: Streamline business processes to reduce operational burden and boost efficiency.
\n

Competition

\n
• Global digital transformation: Businesses worldwide actively implement new technologies and switch to up-to-date ERP solutions to gain a competitive advantage in hectic, fluctuating markets.
\n
• Adoption of SAP S/4HANA: SAP S/4HANA offers companies a set of competitive advantages, such as access to real-time analytics, process automation, system agility, and AI capabilities.
\n

Current state assessment

A comprehensive assessment of your current IT landscape is crucial to highlight the limits of a legacy system, find drawbacks of your current processes, and spot potential compliance risks. This creates a baseline for success measurement after migration, showcasing the advantages of SAP S/4HANA.

Strategic Advantages of SAP S/4HANA

\n
• Real-time analytics and data processing that support data-driven decision-making.
\n
• Simplified data model that lowers data redundancy and data footprint.
\n
• Intuitive SAP Fiori interface, which enhances user experience and makes the system available on mobile devices.
\n
• Advanced functionalities and innovations, such as embedded analytics, AI, machine learning, and robotic process automation.
\n
• Better integration with cloud and digital technologies due to hybrid and cloud deployment.
\n
• Seamless integration with SAP cloud portfolio (SAP Cloud Platform, SAP Ariba).
\n
• Improved performance and scalability due to the HANA in-memory engine.
\n
• Simplified IT landscape and lower total cost of ownership (TCO).
\n

Financial justification

Proving that the SAP S/4HANA migration is financially reasonable is crucial for persuading key stakeholders to start the process. It will also help you adhere to your financial plan and track the payback period, reaching your goals as a result. Financial justification comprises the following steps:

ROI calculations and metrics

Define clear metrics to track the overall success and ROI of SAP S/4HANA migration:

\n
• Cost savings: Set benchmarks to track expense reduction caused by IT maintenance, process inefficiencies, and system downtime.
\n
• Operational efficiency: Track cycle times, fulfillment rates, and other key performance indicators that are relevant to your industry.
\n
• User adoption rates: Evaluate how effectively employees are using the system to perform their roles.
\n

TCO reduction plan in 5 -10 years

According to research by Forrester, organizations can greatly reduce TCO (total cost of ownership) after implementing SAP Cloud ERP.

\n
• Shifting from local on-premise ERP solutions eliminates related infrastructure, maintenance, and labor expenses, cutting overall maintenance costs.
\n
• SAP S/4HANA improves efficiency by up to 30% for users who spend the majority of their working time interacting with the ERP system, and by up to 15% for users who only interact with the ERP system occasionally.
\n
• Switching to in-built SAP Cloud ERP security features instead of legacy recovery platforms and security solutions also saves costs on third-party tools.
\n

Payback period and breakeven point

Set the financial recovery timeline to clearly showcase the payback benchmarks:

\n
• Payback period: The typical timeframe for recovering the initial investment in SAP S/4HANA ranges from 18 to 36 months.
\n
• Breakeven point: The cumulative benefits from the migration offset the total costs incurred within 2 to 3 years post-migration.
\n
• Ongoing value generation: Besides breakeven, companies may expect growing ROI due to scalable operations, enhanced data-driven decision-making, and reduced IT overhead.
\n

Risk assessment and mitigation plan

Evaluate all the potential risks while migrating to SAP S/4HANA. This will help you prepare your options if the transition does not go according to plan. Additionally, work out a migration plan that will help your business gradually adjust to the new system, providing a smoother transition.

Potential risks

\n
• Implementation challenges: The complexity of data migration and system integration.
\n
• User adoption: Resistance to change and inadequate training.
\n
• Operational disruptions: Downtime during transition.
\n

Mitigation strategies

\n
• Comprehensive planning: Detailed project timelines and resource allocation.
\n
• Change management: Effective communication and training programs.
\n
• Phased implementation: Gradual rollout to minimize disruptions.
\n

Implementation roadmap

One of the best practices for implementing SAP S/4HANA is the SAP Activate methodology, which breaks down the implementation process into six essential steps:

Discover

\n
• Evaluate current processes: Assess the existing needs and processes of your business.
\n
• Define technical requirements: Create a detailed specification of the functional and technical needs of the new system, in our case, SAP S/4HANA.
\n

Prepare

\n
• Set goals and objectives: Establish and agree on the goals for the project.
\n
• Assemble the project team: Appoint team members and define their roles and responsibilities.
\n
• Develop the project plan: Create a comprehensive plan that outlines project phases, timelines, resources, and KPIs.
\n
• Determine budget: Estimate and approve the budget.
\n
• Prepare specifications: Develop the technical and functional specifications for the development team.
\n

Explore

\n
• Ensure that business requirements are met: Check that SAP S/4HANA migration aligns with business requirements and project objectives.
\n
• alidate data:V Verify the accuracy and compliance of the data.
\n

Realize

\n
• Migrate data: Transfer data from existing systems to the new one.
\n
• Configure the system: Set up the solution according to requirements and specifications.
\n
• Customize the system: Develop additional features and modules, if the standard solution does not meet all needs.
\n
• Integrate with your business: Configure SAP S/4HANA to work with other IT systems and applications in your environment.
\n

Deploy

\n
• Test the system: Perform functional, integration, regression, and load testing to ensure everything works correctly.
\n
• Educate users: Organize sessions to help users become familiar with the new system.
\n

Run

\n
• Check system readiness: Verify that the system is ready for operational use.
\n
• Launch: Officially transition to active use of SAP S/4HANA.
\n
• Ensure ongoing support: Continuously monitor solution performance to identify and resolve any issues.
\n

KPIs

To measure the success of migrating your business to SAP S/4HANA, consider setting KPIs. This will help you achieve your business goals and make timely adjustments to the strategy in case of underperformance.

System performance metrics

\n
• System availability: Monitor the reliability and accessibility of the SAP system.
\n
• Transaction response time: Measure how quickly transactions are processed to ensure a smooth user experience.
\n

Business process efficiency

\n
• Order-to-cash cycle time: Track the duration from order receipt to payment collection.
\n
• Procure-to-pay cycle time: Measure the efficiency of your procurement process.
\n
• Inventory turnover rate: Assess how quickly inventory is sold and replaced.
\n

Financial metrics

\n
• Cost savings: Calculate reductions in operational costs, IT maintenance, and overhead.
\n
• Revenue growth: Evaluate improvements linked to faster processes and better decision-making.
\n

User adoption & satisfaction

\n
• User adoption rate: Percentage of employees actively using the system compared to the total number of anticipated users.
\n
• Training completion rate: Track how many users have completed SAP training programs.
\n
• User satisfaction score: Collect feedback to gauge ease-of-use and support quality.
\n

Data quality & reporting

\n
• Accuracy of master data: Monitor error rates in key data fields.
\n
• Report generation time: Measure how quickly business insights are produced.
\n

Innovation & agility

\n
• Number of process improvements implemented: Track how many optimizations or automations have been introduced post-migration.
\n
• Time to market for new products/services: Measure how migration enables faster launches.
\n

How DataLark Makes SAP S/4HANA Migration a Success

Migrating to SAP S/4HANA is a complicated and, let’s be honest, costly procedure that requires time and resources. It’s natural that business owners want to lower the price without losing quality.

DataLark, an SAP-centric data management solution developed by LeverX, can help you significantly boost the cost efficiency and success of the SAP S/4HANA migration. By streamlining complex data migration processes, DataLark enables organizations to achieve faster, more reliable, cost-effective transitions to SAP S/4HANA, making the business case more attractive.

With DataLark, you get:

\n
• 60% acceleration of the SAP S/4HANA migration process
\n
• 90% improvement in data management transparency
\n
• 60% enhancement in business modernization and scalability speed
\n

No coding experience required! The intuitive, drag-and-drop interface allows non-technical users to design and implement data workflows. This accessibility reduces the dependency on specialized IT resources, lowering labor costs and expediting the migration process.

DataLark offers robust data extraction, cleansing, transformation, and validation capabilities. These functions ensure high-quality data migration, reducing errors and the need for costly post-migration corrections.

Supporting on-premise, cloud, and hybrid environments, DataLark provides organizations with the flexibility to choose deployment models that align with their infrastructure and cost considerations.

DataLark integrates smoothly with the SAP Migration Cockpit and supports various data loading methods, including OData and RFC APIs. This compatibility ensures a cohesive migration experience and leverages existing SAP tools, enhancing overall efficiency.

DataLark’s unified data‑migration assessment & roadmap

DataLark allows you to create a consistent data migration strategy, assess data, conduct testing, and prepare a fallback plan (in case something goes wrong) via fruitful collaboration with LeverX migration experts.

This offer is a four‑week consulting sprint. We design a safe, cost‑balanced path to move your master and transactional data from SAP ECC and non‑SAP systems into SAP S/4HANA.

As a result, the following artifacts of your SAP S/4HANA will be migration-ready:

\n
• High-level migration blueprint — target architecture, data‑flow diagrams, tooling stack
\n
• Pilot results report — load metrics, issue log, remediation actions
\n
• Data quality & cleansing report — ranked by risk and impact
\n
• Cut-over playbook — a plan with fallback scenarios
\n

Conclusion

To effectively support decision-making, it's essential to blend both qualitative and quantitative evaluations of SAP S/4HANA migration and present them in a clear, leadership-friendly format. The focus should be on highlighting key business value drivers, current pain points, opportunities for improvement, and setting practical, achievable goals. Before embarking on a migration journey, business owners should validate the business case to ensure alignment and accuracy.

We hope this has provided valuable insights into creating the business case for the SAP S/4HANA migration and demonstrated why it represents a strong investment opportunity. When implemented with the right strategy, this robust ERP solution can drive efficiency and reduce costs, enhancing productivity, customer satisfaction, and overall business performance.

Create a compelling business case for SAP S/4HANA migration, enumerating process and financial benefits, justified by ROI calculation benchmarks.

Getting Started With GROW with SAP Journey: A Step-by-Step Guide

Mid-market businesses often need to modernize their ERP landscape quickly but lack the resources for long, multi-year programs. GROW with SAP is SAP's customer-journey program that accelerates time-to-value through prescriptive methodology, pre-configured content, and partner expertise. Customers purchase one of the new SAP Business Suite packages (e.g., SAP Finance Base), then expand by adding further lines of business.

This guide underlines the value of GROW with SAP for mid-market businesses and explains how the journey works, how to select the right package, and which technology pillars power a clean-core migration.

Choosing the Right SAP Business Suite Package

GROW with SAP allows mid-market businesses to streamline their ERP journey to SAP Business Suite implementation. It divides the ERP bundle into clear, modular SAP Business Suite packages that are easier for businesses to adopt. Instead of a one-size-fits-all approach, you begin with the area most critical to your business (such as Finance or Supply Chain) and grow from there.

Each package includes ready-to-run business processes, automation, and best practices specific to that function. This accelerates deployment and minimizes configuration.

Here's what you can choose, based on your business priorities and needs:

Add-ons: flexible extensions for deeper capabilities

Once you go live with your base package, you can add more functionality using Level 1 and/or Level 2 add-ons:

\n
• Level 1 Add-ons include: Closing & Consolidation, Treasury & Banking, Planning, Manufacturing, and Assets & Service
\n
• Level 2 Add-ons provide: industry-specific or region-specific enhancements (e.g., Public Sector, Automotive, Japan localization)
\n

These add-ons are optional and modular, so you only pay for what you need, when you need it.

Practical selection advice

Here are some tips to add more value to your GROW with SAP journey:

\n
• Start with the biggest pain point. If finance struggles with month-end closing, start with SAP Finance Base.
\n
• Grow organically. Add procurement, supply chain, or HR once your core package is stable.
\n
• Use pre-built best practices. Every package includes standardized workflows tested across industries, so you don’t start from scratch.
\n
• Speed up deployment with SAP Activate. This methodology includes templates, project timelines, and best practices to ensure a smooth implementation.
\n

Whether you're a growing business modernizing your first ERP or a large enterprise shifting from legacy systems, the SAP Business Suite packages give you a scalable way to start simple and evolve with your needs.

Technology Pillars To Power Your Transformation Journey

A successful GROW with SAP project relies on four complementary pillars, or products, that work together to support your business transformation.

SAP Cloud ERP

The SaaS foundation (marketing name for SAP S/4HANA Cloud Public Edition) delivers quarterly innovations and a clean-core architecture. It allows organizations to adopt best practices with minimal IT effort.

SAP Business Technology Platform

SAP BTP provides five capability areas:

\n
• Application Development — build apps without custom code
\n
• Automation — create workflows and bots to reduce manual work
\n
• Integration — connect SAP and non-SAP systems seamlessly
\n
• Data & Analytics — access real-time dashboards and KPIs
\n
• AI — embed intelligence into every business process
\n

SAP Business Data Cloud

This turns data from SAP and external systems into governed data products that are trusted, reusable and ready for reporting, planning, and AI. Built on SAP Datasphere, it enables a consistent data fabric across the enterprise.

SAP Business AI

Business AI infuses generative AI into core apps. You get AI-driven insights, copilots like SAP Joule, and automation that boosts decision-making without coding.

GROW with SAP Migration Roadmap — What To Expect and When

Migrating to SAP Cloud ERP doesn’t require a massive replatforming. Many companies follow a proven two-month approach:

Key stakeholders: Business process owners, IT data leads, partner consultants, and key SAP users from each functional area.

Common tools used:

\n
• DataLark for data extraction, validation, rule-based transformation, and loading to SAP Migration Cockpit
\n
• SAP Datasphere for real-time replication
\n
• SAP APIs for bulk loading (OData, SOAP)
\n

Choosing the Right Migration Partner

Most GROW with SAP customers benefit from expert-led migration. Mid-market businesses often lack in-house IT resources or ERP expertise. Working with a trusted partner ensures smooth migration while your team focuses on business operations.

After choosing a migration partner, preparing a readiness assessment of your landscape is essential to ensure that your legacy system is ready to transfer. Make sure to:

\n
• Evaluate data quality
\n
• Define migration scope and fallback plans
\n
• Run testing cycles
\n

DataLark’s Unified Data‑Migration Assessment & Roadmap offer is your opportunity to kill not two, but four birds with one stone; our experts will closely collaborate with you to create a data migration strategy, assess data, conduct testing, and prepare a plan of fallback scenarios.

This offer is a four‑week consulting sprint that designs a safe, cost‑balanced path to move master and transactional data from SAP ECC and non‑SAP systems into SAP ERP (public or private edition). In the case of GROW with SAP, the data will be moved from a legacy non-SAP source to SAP Cloud ERP.

Our experts combine SAP Migration Cockpit, SAP Datasphere replication flows, and SAP Integration Suite APIs to deliver an actionable migration blueprint, a two‑object pilot proof, and a cut‑over playbook. For scenarios involving complex field mappings or integrations between SAP and non-SAP systems, which is a common case for GROW with SAP, we use DataLark - an SAP-centric data management platform designed by LeverX to streamline the migration process.

Here’s what happens during these four weeks:

\n
• \n

  Week 1 – Discovery:

  \n
  \n
  • Landscape inventory (third‑party ERPs and data lakes)
  \n
  • Data volume and quality profiling
  \n
  • Stakeholder workshops and risk register
  \n
  \n
\n
• Week 2 – Design: \n
  \n
  • Migration‑path scoring (Greenfield approach for GROW).
  \n
  • Tooling mix definition: SAP Migration Cockpit, Datasphere replication, SOAP Bulk APIs, OData V4, DataLark.
  \n
  • Compliance checks: clean‑core, SAP Note 3255746 (RFC ban), public‑cloud API limits - KBAs 3542227 / 3391018.
  \n
  \n
\n
• Week 3 – Pilot proof: \n
  \n
  • Prototype migration of two representative objects (e.g., BP from a legacy non-SAP ERP, Item Master from a data lake).
  \n
  • Using direct connectors or CSV extracting when third‑party connectors are not in place.
  \n
  • Measure throughput, error rates, and delta‑load feasibility.
  \n
  \n
\n
• Week 4 – Roadmap & business case: \n
  \n
  • Migration roadmap (timeline, budget, roles)
  \n
  • Cut‑over & rollback playbook
  \n
  • Executive‑ready business case deck and Q&A
  \n
  \n
\n

As a result, you will have the following aspects of your GROW with SAP migration ready:

\n
• High-level Migration blueprin — target architecture, data‑flow diagrams, tooling stack
\n
• Pilot results report — load metrics, issue log, remediation actions
\n
• Data quality & cleansing report — ranked by risk and impact
\n
• Cut-over playbook — a plan with fallback scenarios
\n

With our offering, your business will be fully informed on how to perform data migration to the new platform.

Business Benefits of GROW with SAP in Plain Terms

Switching to SAP Cloud ERP using the GROW with SAP journey brings tangible value for mid-market businesses that can hardly be overestimated. Taking on new cloud technologies and transferring to the SAP ecosystem opens up unlimited possibilities for business scalability, overall growth, and further digital transformation, all of which are crucial for staying competitive in ever-evolving and fluctuating markets.

Streamlined operations

GROW with SAP is a digital transformation journey tailored to help mid-market businesses that aspire to enhance operational efficiency. The platform unifies critical processes from supply chain logistics to customer engagement, which allows users to get real-time insights across the organization. This unmatched level of transparency empowers businesses to pinpoint process gaps and inefficiencies, introduce data-driven improvements, and boost overall performance.

Lower TCO

As GROW with SAP operates in the cloud, using the system eliminates the need for mid-market companies to purchase costly hardware infrastructure or software. This dramatically reduces upfront IT expenses, decreasing TCO (total cost of ownership). Consequently, GROW with SAP frees up resources that can be redirected toward strategic initiatives to fuel growth and increase profitability. GROW with SAP also offers no-code development options that empower businesses to tailor applications, create custom solutions, and automate workflows without the need for a dedicated team of developers.

Improved decision-making

GROW with SAP utilizes SAP Cloud ERP, integrating sophisticated analytics and machine learning capabilities. This empowers mid-market businesses to gain deeper visibility into their operations, supporting smarter, data-driven decision-making. By harnessing these insights, companies are better positioned to accelerate growth and enhance profitability.

Enhanced scalability

GROW with SAP is built to evolve alongside mid-market businesses, offering a flexible solution that adapts to each company's specific requirements. Scalable by nature, GROW with SAP ensures that organizations can easily tap into cutting-edge technologies and innovations without the burden of heavy IT infrastructure investments.

Integrations made easy

As part of the SAP ecosystem, GROW with SAP allows companies to seamlessly implement SAP Business Suite for evolving business needs. This allows enterprises to maintain their data and operations in a single source of truth, streamlining processes and knowledge sharing with no need for third-party integrations or switching between different systems.

Enhanced customer experience

GROW with SAP delivers a set of powerful features aimed at elevating customer experience. From personalized recommendations to advanced analytics, the solution equips mid-market businesses with deep insights into customer behaviors and preferences. Armed with this knowledge, companies can fine-tune their offerings to better align with customer expectations, ultimately improving satisfaction and fostering long-term loyalty.

Better security and compliance

All SAP solutions offer outstanding security features and ensure compliance with local and global industry standards, and GROW with SAP is no exception. The solution is equipped with features like data encryption, access controls, and continuous monitoring, which helps safeguard sensitive business information. Meanwhile, the adherence to compliance standards gives mid-market companies the confidence to operate securely in a global marketplace.

Conclusion

GROW with SAP’s customer-journey program is a transformative opportunity for mid-market businesses aiming to modernize their operations, embrace cloud innovation, and future-proof their growth. By harnessing the capabilities of SAP Cloud ERP and SAP BTP, this digital transformation solution empowers companies to streamline processes, reduce costs, and make smarter data-driven decisions.

While migrating your data and processes to SAP Cloud ERP is comparatively easy, you still need a carefully prepared migration plan. Also, make sure to utilize professional data management solutions that ensure your data is fully prepared to be transferred to a new system. Combining GROW with SAP with the right guidance, tools, and support, your business can confidently transition to a cloud-first environment and unlock its full potential.

Check out a step-by-step guide on how to start your GROW with SAP journey to successfully prepare your enterprise data for migration to the new environment.

Check out a step-by-step guide on how to start your GROW with SAP journey to successfully prepare your enterprise data for migration to the new environment.

Getting Started With GROW with SAP Journey: A Step-by-Step Guide

Mid-market businesses often need to modernize their ERP landscape quickly but lack the resources for long, multi-year programs. GROW with SAP is SAP's customer-journey program that accelerates time-to-value through prescriptive methodology, pre-configured content, and partner expertise. Customers purchase one of the new SAP Business Suite packages (e.g., SAP Finance Base), then expand by adding further lines of business.

This guide underlines the value of GROW with SAP for mid-market businesses and explains how the journey works, how to select the right package, and which technology pillars power a clean-core migration.

Choosing the Right SAP Business Suite Package

GROW with SAP allows mid-market businesses to streamline their ERP journey to SAP Business Suite implementation. It divides the ERP bundle into clear, modular SAP Business Suite packages that are easier for businesses to adopt. Instead of a one-size-fits-all approach, you begin with the area most critical to your business (such as Finance or Supply Chain) and grow from there.

Each package includes ready-to-run business processes, automation, and best practices specific to that function. This accelerates deployment and minimizes configuration.

Here's what you can choose, based on your business priorities and needs:

Add-ons: flexible extensions for deeper capabilities

Once you go live with your base package, you can add more functionality using Level 1 and/or Level 2 add-ons:

\n
• Level 1 Add-ons include: Closing & Consolidation, Treasury & Banking, Planning, Manufacturing, and Assets & Service
\n
• Level 2 Add-ons provide: industry-specific or region-specific enhancements (e.g., Public Sector, Automotive, Japan localization)
\n

These add-ons are optional and modular, so you only pay for what you need, when you need it.

Practical selection advice

Here are some tips to add more value to your GROW with SAP journey:

\n
• Start with the biggest pain point. If finance struggles with month-end closing, start with SAP Finance Base.
\n
• Grow organically. Add procurement, supply chain, or HR once your core package is stable.
\n
• Use pre-built best practices. Every package includes standardized workflows tested across industries, so you don’t start from scratch.
\n
• Speed up deployment with SAP Activate. This methodology includes templates, project timelines, and best practices to ensure a smooth implementation.
\n

Whether you're a growing business modernizing your first ERP or a large enterprise shifting from legacy systems, the SAP Business Suite packages give you a scalable way to start simple and evolve with your needs.

Technology Pillars To Power Your Transformation Journey

A successful GROW with SAP project relies on four complementary pillars, or products, that work together to support your business transformation.

SAP Cloud ERP

The SaaS foundation (marketing name for SAP S/4HANA Cloud Public Edition) delivers quarterly innovations and a clean-core architecture. It allows organizations to adopt best practices with minimal IT effort.

SAP Business Technology Platform

SAP BTP provides five capability areas:

\n
• Application Development — build apps without custom code
\n
• Automation — create workflows and bots to reduce manual work
\n
• Integration — connect SAP and non-SAP systems seamlessly
\n
• Data & Analytics — access real-time dashboards and KPIs
\n
• AI — embed intelligence into every business process
\n

SAP Business Data Cloud

This turns data from SAP and external systems into governed data products that are trusted, reusable and ready for reporting, planning, and AI. Built on SAP Datasphere, it enables a consistent data fabric across the enterprise.

SAP Business AI

Business AI infuses generative AI into core apps. You get AI-driven insights, copilots like SAP Joule, and automation that boosts decision-making without coding.

GROW with SAP Migration Roadmap — What To Expect and When

Migrating to SAP Cloud ERP doesn’t require a massive replatforming. Many companies follow a proven two-month approach:

Key stakeholders: Business process owners, IT data leads, partner consultants, and key SAP users from each functional area.

Common tools used:

\n
• DataLark for data extraction, validation, rule-based transformation, and loading to SAP Migration Cockpit
\n
• SAP Datasphere for real-time replication
\n
• SAP APIs for bulk loading (OData, SOAP)
\n

Choosing the Right Migration Partner

Most GROW with SAP customers benefit from expert-led migration. Mid-market businesses often lack in-house IT resources or ERP expertise. Working with a trusted partner ensures smooth migration while your team focuses on business operations.

After choosing a migration partner, preparing a readiness assessment of your landscape is essential to ensure that your legacy system is ready to transfer. Make sure to:

\n
• Evaluate data quality
\n
• Define migration scope and fallback plans
\n
• Run testing cycles
\n

DataLark’s Unified Data‑Migration Assessment & Roadmap offer is your opportunity to kill not two, but four birds with one stone; our experts will closely collaborate with you to create a data migration strategy, assess data, conduct testing, and prepare a plan of fallback scenarios.

This offer is a four‑week consulting sprint that designs a safe, cost‑balanced path to move master and transactional data from SAP ECC and non‑SAP systems into SAP ERP (public or private edition). In the case of GROW with SAP, the data will be moved from a legacy non-SAP source to SAP Cloud ERP.

Our experts combine SAP Migration Cockpit, SAP Datasphere replication flows, and SAP Integration Suite APIs to deliver an actionable migration blueprint, a two‑object pilot proof, and a cut‑over playbook. For scenarios involving complex field mappings or integrations between SAP and non-SAP systems, which is a common case for GROW with SAP, we use DataLark - an SAP-centric data management platform designed by LeverX to streamline the migration process.

Here’s what happens during these four weeks:

\n
• \n

  Week 1 – Discovery:

  \n
  \n
  • Landscape inventory (third‑party ERPs and data lakes)
  \n
  • Data volume and quality profiling
  \n
  • Stakeholder workshops and risk register
  \n
  \n
\n
• Week 2 – Design: \n
  \n
  • Migration‑path scoring (Greenfield approach for GROW).
  \n
  • Tooling mix definition: SAP Migration Cockpit, Datasphere replication, SOAP Bulk APIs, OData V4, DataLark.
  \n
  • Compliance checks: clean‑core, SAP Note 3255746 (RFC ban), public‑cloud API limits - KBAs 3542227 / 3391018.
  \n
  \n
\n
• Week 3 – Pilot proof: \n
  \n
  • Prototype migration of two representative objects (e.g., BP from a legacy non-SAP ERP, Item Master from a data lake).
  \n
  • Using direct connectors or CSV extracting when third‑party connectors are not in place.
  \n
  • Measure throughput, error rates, and delta‑load feasibility.
  \n
  \n
\n
• Week 4 – Roadmap & business case: \n
  \n
  • Migration roadmap (timeline, budget, roles)
  \n
  • Cut‑over & rollback playbook
  \n
  • Executive‑ready business case deck and Q&A
  \n
  \n
\n

As a result, you will have the following aspects of your GROW with SAP migration ready:

\n
• High-level Migration blueprin — target architecture, data‑flow diagrams, tooling stack
\n
• Pilot results report — load metrics, issue log, remediation actions
\n
• Data quality & cleansing report — ranked by risk and impact
\n
• Cut-over playbook — a plan with fallback scenarios
\n

With our offering, your business will be fully informed on how to perform data migration to the new platform.

Business Benefits of GROW with SAP in Plain Terms

Switching to SAP Cloud ERP using the GROW with SAP journey brings tangible value for mid-market businesses that can hardly be overestimated. Taking on new cloud technologies and transferring to the SAP ecosystem opens up unlimited possibilities for business scalability, overall growth, and further digital transformation, all of which are crucial for staying competitive in ever-evolving and fluctuating markets.

Streamlined operations

GROW with SAP is a digital transformation journey tailored to help mid-market businesses that aspire to enhance operational efficiency. The platform unifies critical processes from supply chain logistics to customer engagement, which allows users to get real-time insights across the organization. This unmatched level of transparency empowers businesses to pinpoint process gaps and inefficiencies, introduce data-driven improvements, and boost overall performance.

Lower TCO

As GROW with SAP operates in the cloud, using the system eliminates the need for mid-market companies to purchase costly hardware infrastructure or software. This dramatically reduces upfront IT expenses, decreasing TCO (total cost of ownership). Consequently, GROW with SAP frees up resources that can be redirected toward strategic initiatives to fuel growth and increase profitability. GROW with SAP also offers no-code development options that empower businesses to tailor applications, create custom solutions, and automate workflows without the need for a dedicated team of developers.

Improved decision-making

GROW with SAP utilizes SAP Cloud ERP, integrating sophisticated analytics and machine learning capabilities. This empowers mid-market businesses to gain deeper visibility into their operations, supporting smarter, data-driven decision-making. By harnessing these insights, companies are better positioned to accelerate growth and enhance profitability.

Enhanced scalability

GROW with SAP is built to evolve alongside mid-market businesses, offering a flexible solution that adapts to each company's specific requirements. Scalable by nature, GROW with SAP ensures that organizations can easily tap into cutting-edge technologies and innovations without the burden of heavy IT infrastructure investments.

Integrations made easy

As part of the SAP ecosystem, GROW with SAP allows companies to seamlessly implement SAP Business Suite for evolving business needs. This allows enterprises to maintain their data and operations in a single source of truth, streamlining processes and knowledge sharing with no need for third-party integrations or switching between different systems.

Enhanced customer experience

GROW with SAP delivers a set of powerful features aimed at elevating customer experience. From personalized recommendations to advanced analytics, the solution equips mid-market businesses with deep insights into customer behaviors and preferences. Armed with this knowledge, companies can fine-tune their offerings to better align with customer expectations, ultimately improving satisfaction and fostering long-term loyalty.

Better security and compliance

All SAP solutions offer outstanding security features and ensure compliance with local and global industry standards, and GROW with SAP is no exception. The solution is equipped with features like data encryption, access controls, and continuous monitoring, which helps safeguard sensitive business information. Meanwhile, the adherence to compliance standards gives mid-market companies the confidence to operate securely in a global marketplace.

Conclusion

GROW with SAP’s customer-journey program is a transformative opportunity for mid-market businesses aiming to modernize their operations, embrace cloud innovation, and future-proof their growth. By harnessing the capabilities of SAP Cloud ERP and SAP BTP, this digital transformation solution empowers companies to streamline processes, reduce costs, and make smarter data-driven decisions.

While migrating your data and processes to SAP Cloud ERP is comparatively easy, you still need a carefully prepared migration plan. Also, make sure to utilize professional data management solutions that ensure your data is fully prepared to be transferred to a new system. Combining GROW with SAP with the right guidance, tools, and support, your business can confidently transition to a cloud-first environment and unlock its full potential.

Check out a step-by-step guide on how to start your GROW with SAP journey to successfully prepare your enterprise data for migration to the new environment.

Check out a step-by-step guide on how to start your GROW with SAP journey to successfully prepare your enterprise data for migration to the new environment.

Getting Started With GROW with SAP Journey: A Step-by-Step Guide

Mid-market businesses often need to modernize their ERP landscape quickly but lack the resources for long, multi-year programs. GROW with SAP is SAP's customer-journey program that accelerates time-to-value through prescriptive methodology, pre-configured content, and partner expertise. Customers purchase one of the new SAP Business Suite packages (e.g., SAP Finance Base), then expand by adding further lines of business.

This guide underlines the value of GROW with SAP for mid-market businesses and explains how the journey works, how to select the right package, and which technology pillars power a clean-core migration.

Choosing the Right SAP Business Suite Package

GROW with SAP allows mid-market businesses to streamline their ERP journey to SAP Business Suite implementation. It divides the ERP bundle into clear, modular SAP Business Suite packages that are easier for businesses to adopt. Instead of a one-size-fits-all approach, you begin with the area most critical to your business (such as Finance or Supply Chain) and grow from there.

Each package includes ready-to-run business processes, automation, and best practices specific to that function. This accelerates deployment and minimizes configuration.

Here's what you can choose, based on your business priorities and needs:

Add-ons: flexible extensions for deeper capabilities

Once you go live with your base package, you can add more functionality using Level 1 and/or Level 2 add-ons:

\n
• Level 1 Add-ons include: Closing & Consolidation, Treasury & Banking, Planning, Manufacturing, and Assets & Service
\n
• Level 2 Add-ons provide: industry-specific or region-specific enhancements (e.g., Public Sector, Automotive, Japan localization)
\n

These add-ons are optional and modular, so you only pay for what you need, when you need it.

Practical selection advice

Here are some tips to add more value to your GROW with SAP journey:

\n
• Start with the biggest pain point. If finance struggles with month-end closing, start with SAP Finance Base.
\n
• Grow organically. Add procurement, supply chain, or HR once your core package is stable.
\n
• Use pre-built best practices. Every package includes standardized workflows tested across industries, so you don’t start from scratch.
\n
• Speed up deployment with SAP Activate. This methodology includes templates, project timelines, and best practices to ensure a smooth implementation.
\n

Whether you're a growing business modernizing your first ERP or a large enterprise shifting from legacy systems, the SAP Business Suite packages give you a scalable way to start simple and evolve with your needs.

Technology Pillars To Power Your Transformation Journey

A successful GROW with SAP project relies on four complementary pillars, or products, that work together to support your business transformation.

SAP Cloud ERP

The SaaS foundation (marketing name for SAP S/4HANA Cloud Public Edition) delivers quarterly innovations and a clean-core architecture. It allows organizations to adopt best practices with minimal IT effort.

SAP Business Technology Platform

SAP BTP provides five capability areas:

\n
• Application Development — build apps without custom code
\n
• Automation — create workflows and bots to reduce manual work
\n
• Integration — connect SAP and non-SAP systems seamlessly
\n
• Data & Analytics — access real-time dashboards and KPIs
\n
• AI — embed intelligence into every business process
\n

SAP Business Data Cloud

This turns data from SAP and external systems into governed data products that are trusted, reusable and ready for reporting, planning, and AI. Built on SAP Datasphere, it enables a consistent data fabric across the enterprise.

SAP Business AI

Business AI infuses generative AI into core apps. You get AI-driven insights, copilots like SAP Joule, and automation that boosts decision-making without coding.

GROW with SAP Migration Roadmap — What To Expect and When

Migrating to SAP Cloud ERP doesn’t require a massive replatforming. Many companies follow a proven two-month approach:

Key stakeholders: Business process owners, IT data leads, partner consultants, and key SAP users from each functional area.

Common tools used:

\n
• DataLark for data extraction, validation, rule-based transformation, and loading to SAP Migration Cockpit
\n
• SAP Datasphere for real-time replication
\n
• SAP APIs for bulk loading (OData, SOAP)
\n

Choosing the Right Migration Partner

Most GROW with SAP customers benefit from expert-led migration. Mid-market businesses often lack in-house IT resources or ERP expertise. Working with a trusted partner ensures smooth migration while your team focuses on business operations.

After choosing a migration partner, preparing a readiness assessment of your landscape is essential to ensure that your legacy system is ready to transfer. Make sure to:

\n
• Evaluate data quality
\n
• Define migration scope and fallback plans
\n
• Run testing cycles
\n

DataLark’s Unified Data‑Migration Assessment & Roadmap offer is your opportunity to kill not two, but four birds with one stone; our experts will closely collaborate with you to create a data migration strategy, assess data, conduct testing, and prepare a plan of fallback scenarios.

This offer is a four‑week consulting sprint that designs a safe, cost‑balanced path to move master and transactional data from SAP ECC and non‑SAP systems into SAP ERP (public or private edition). In the case of GROW with SAP, the data will be moved from a legacy non-SAP source to SAP Cloud ERP.

Our experts combine SAP Migration Cockpit, SAP Datasphere replication flows, and SAP Integration Suite APIs to deliver an actionable migration blueprint, a two‑object pilot proof, and a cut‑over playbook. For scenarios involving complex field mappings or integrations between SAP and non-SAP systems, which is a common case for GROW with SAP, we use DataLark - an SAP-centric data management platform designed by LeverX to streamline the migration process.

Here’s what happens during these four weeks:

\n
• \n

  Week 1 – Discovery:

  \n
  \n
  • Landscape inventory (third‑party ERPs and data lakes)
  \n
  • Data volume and quality profiling
  \n
  • Stakeholder workshops and risk register
  \n
  \n
\n
• Week 2 – Design: \n
  \n
  • Migration‑path scoring (Greenfield approach for GROW).
  \n
  • Tooling mix definition: SAP Migration Cockpit, Datasphere replication, SOAP Bulk APIs, OData V4, DataLark.
  \n
  • Compliance checks: clean‑core, SAP Note 3255746 (RFC ban), public‑cloud API limits - KBAs 3542227 / 3391018.
  \n
  \n
\n
• Week 3 – Pilot proof: \n
  \n
  • Prototype migration of two representative objects (e.g., BP from a legacy non-SAP ERP, Item Master from a data lake).
  \n
  • Using direct connectors or CSV extracting when third‑party connectors are not in place.
  \n
  • Measure throughput, error rates, and delta‑load feasibility.
  \n
  \n
\n
• Week 4 – Roadmap & business case: \n
  \n
  • Migration roadmap (timeline, budget, roles)
  \n
  • Cut‑over & rollback playbook
  \n
  • Executive‑ready business case deck and Q&A
  \n
  \n
\n

As a result, you will have the following aspects of your GROW with SAP migration ready:

\n
• High-level Migration blueprin — target architecture, data‑flow diagrams, tooling stack
\n
• Pilot results report — load metrics, issue log, remediation actions
\n
• Data quality & cleansing report — ranked by risk and impact
\n
• Cut-over playbook — a plan with fallback scenarios
\n

With our offering, your business will be fully informed on how to perform data migration to the new platform.

Business Benefits of GROW with SAP in Plain Terms

Switching to SAP Cloud ERP using the GROW with SAP journey brings tangible value for mid-market businesses that can hardly be overestimated. Taking on new cloud technologies and transferring to the SAP ecosystem opens up unlimited possibilities for business scalability, overall growth, and further digital transformation, all of which are crucial for staying competitive in ever-evolving and fluctuating markets.

Streamlined operations

GROW with SAP is a digital transformation journey tailored to help mid-market businesses that aspire to enhance operational efficiency. The platform unifies critical processes from supply chain logistics to customer engagement, which allows users to get real-time insights across the organization. This unmatched level of transparency empowers businesses to pinpoint process gaps and inefficiencies, introduce data-driven improvements, and boost overall performance.

Lower TCO

As GROW with SAP operates in the cloud, using the system eliminates the need for mid-market companies to purchase costly hardware infrastructure or software. This dramatically reduces upfront IT expenses, decreasing TCO (total cost of ownership). Consequently, GROW with SAP frees up resources that can be redirected toward strategic initiatives to fuel growth and increase profitability. GROW with SAP also offers no-code development options that empower businesses to tailor applications, create custom solutions, and automate workflows without the need for a dedicated team of developers.

Improved decision-making

GROW with SAP utilizes SAP Cloud ERP, integrating sophisticated analytics and machine learning capabilities. This empowers mid-market businesses to gain deeper visibility into their operations, supporting smarter, data-driven decision-making. By harnessing these insights, companies are better positioned to accelerate growth and enhance profitability.

Enhanced scalability

GROW with SAP is built to evolve alongside mid-market businesses, offering a flexible solution that adapts to each company's specific requirements. Scalable by nature, GROW with SAP ensures that organizations can easily tap into cutting-edge technologies and innovations without the burden of heavy IT infrastructure investments.

Integrations made easy

As part of the SAP ecosystem, GROW with SAP allows companies to seamlessly implement SAP Business Suite for evolving business needs. This allows enterprises to maintain their data and operations in a single source of truth, streamlining processes and knowledge sharing with no need for third-party integrations or switching between different systems.

Enhanced customer experience

GROW with SAP delivers a set of powerful features aimed at elevating customer experience. From personalized recommendations to advanced analytics, the solution equips mid-market businesses with deep insights into customer behaviors and preferences. Armed with this knowledge, companies can fine-tune their offerings to better align with customer expectations, ultimately improving satisfaction and fostering long-term loyalty.

Better security and compliance

All SAP solutions offer outstanding security features and ensure compliance with local and global industry standards, and GROW with SAP is no exception. The solution is equipped with features like data encryption, access controls, and continuous monitoring, which helps safeguard sensitive business information. Meanwhile, the adherence to compliance standards gives mid-market companies the confidence to operate securely in a global marketplace.

Conclusion

GROW with SAP’s customer-journey program is a transformative opportunity for mid-market businesses aiming to modernize their operations, embrace cloud innovation, and future-proof their growth. By harnessing the capabilities of SAP Cloud ERP and SAP BTP, this digital transformation solution empowers companies to streamline processes, reduce costs, and make smarter data-driven decisions.

While migrating your data and processes to SAP Cloud ERP is comparatively easy, you still need a carefully prepared migration plan. Also, make sure to utilize professional data management solutions that ensure your data is fully prepared to be transferred to a new system. Combining GROW with SAP with the right guidance, tools, and support, your business can confidently transition to a cloud-first environment and unlock its full potential.

Check out a step-by-step guide on how to start your GROW with SAP journey to successfully prepare your enterprise data for migration to the new environment.

Getting Started With GROW with SAP Journey: A Step-by-Step Guide

Mid-market businesses often need to modernize their ERP landscape quickly but lack the resources for long, multi-year programs. GROW with SAP is SAP's customer-journey program that accelerates time-to-value through prescriptive methodology, pre-configured content, and partner expertise. Customers purchase one of the new SAP Business Suite packages (e.g., SAP Finance Base), then expand by adding further lines of business.

This guide underlines the value of GROW with SAP for mid-market businesses and explains how the journey works, how to select the right package, and which technology pillars power a clean-core migration.

Choosing the Right SAP Business Suite Package

GROW with SAP allows mid-market businesses to streamline their ERP journey to SAP Business Suite implementation. It divides the ERP bundle into clear, modular SAP Business Suite packages that are easier for businesses to adopt. Instead of a one-size-fits-all approach, you begin with the area most critical to your business (such as Finance or Supply Chain) and grow from there.

Each package includes ready-to-run business processes, automation, and best practices specific to that function. This accelerates deployment and minimizes configuration.

Here's what you can choose, based on your business priorities and needs:

Add-ons: flexible extensions for deeper capabilities

Once you go live with your base package, you can add more functionality using Level 1 and/or Level 2 add-ons:

\n
• Level 1 Add-ons include: Closing & Consolidation, Treasury & Banking, Planning, Manufacturing, and Assets & Service
\n
• Level 2 Add-ons provide: industry-specific or region-specific enhancements (e.g., Public Sector, Automotive, Japan localization)
\n

These add-ons are optional and modular, so you only pay for what you need, when you need it.

Practical selection advice

Here are some tips to add more value to your GROW with SAP journey:

\n
• Start with the biggest pain point. If finance struggles with month-end closing, start with SAP Finance Base.
\n
• Grow organically. Add procurement, supply chain, or HR once your core package is stable.
\n
• Use pre-built best practices. Every package includes standardized workflows tested across industries, so you don’t start from scratch.
\n
• Speed up deployment with SAP Activate. This methodology includes templates, project timelines, and best practices to ensure a smooth implementation.
\n

Whether you're a growing business modernizing your first ERP or a large enterprise shifting from legacy systems, the SAP Business Suite packages give you a scalable way to start simple and evolve with your needs.

Technology Pillars To Power Your Transformation Journey

A successful GROW with SAP project relies on four complementary pillars, or products, that work together to support your business transformation.

SAP Cloud ERP

The SaaS foundation (marketing name for SAP S/4HANA Cloud Public Edition) delivers quarterly innovations and a clean-core architecture. It allows organizations to adopt best practices with minimal IT effort.

SAP Business Technology Platform

SAP BTP provides five capability areas:

\n
• Application Development — build apps without custom code
\n
• Automation — create workflows and bots to reduce manual work
\n
• Integration — connect SAP and non-SAP systems seamlessly
\n
• Data & Analytics — access real-time dashboards and KPIs
\n
• AI — embed intelligence into every business process
\n

SAP Business Data Cloud

This turns data from SAP and external systems into governed data products that are trusted, reusable and ready for reporting, planning, and AI. Built on SAP Datasphere, it enables a consistent data fabric across the enterprise.

SAP Business AI

Business AI infuses generative AI into core apps. You get AI-driven insights, copilots like SAP Joule, and automation that boosts decision-making without coding.

GROW with SAP Migration Roadmap — What To Expect and When

Migrating to SAP Cloud ERP doesn’t require a massive replatforming. Many companies follow a proven two-month approach:

Key stakeholders: Business process owners, IT data leads, partner consultants, and key SAP users from each functional area.

Common tools used:

\n
• DataLark for data extraction, validation, rule-based transformation, and loading to SAP Migration Cockpit
\n
• SAP Datasphere for real-time replication
\n
• SAP APIs for bulk loading (OData, SOAP)
\n

Choosing the Right Migration Partner

Most GROW with SAP customers benefit from expert-led migration. Mid-market businesses often lack in-house IT resources or ERP expertise. Working with a trusted partner ensures smooth migration while your team focuses on business operations.

After choosing a migration partner, preparing a readiness assessment of your landscape is essential to ensure that your legacy system is ready to transfer. Make sure to:

\n
• Evaluate data quality
\n
• Define migration scope and fallback plans
\n
• Run testing cycles
\n

DataLark’s Unified Data‑Migration Assessment & Roadmap offer is your opportunity to kill not two, but four birds with one stone; our experts will closely collaborate with you to create a data migration strategy, assess data, conduct testing, and prepare a plan of fallback scenarios.

This offer is a four‑week consulting sprint that designs a safe, cost‑balanced path to move master and transactional data from SAP ECC and non‑SAP systems into SAP ERP (public or private edition). In the case of GROW with SAP, the data will be moved from a legacy non-SAP source to SAP Cloud ERP.

Our experts combine SAP Migration Cockpit, SAP Datasphere replication flows, and SAP Integration Suite APIs to deliver an actionable migration blueprint, a two‑object pilot proof, and a cut‑over playbook. For scenarios involving complex field mappings or integrations between SAP and non-SAP systems, which is a common case for GROW with SAP, we use DataLark - an SAP-centric data management platform designed by LeverX to streamline the migration process.

Here’s what happens during these four weeks:

\n
• \n

  Week 1 – Discovery:

  \n
  \n
  • Landscape inventory (third‑party ERPs and data lakes)
  \n
  • Data volume and quality profiling
  \n
  • Stakeholder workshops and risk register
  \n
  \n
\n
• Week 2 – Design: \n
  \n
  • Migration‑path scoring (Greenfield approach for GROW).
  \n
  • Tooling mix definition: SAP Migration Cockpit, Datasphere replication, SOAP Bulk APIs, OData V4, DataLark.
  \n
  • Compliance checks: clean‑core, SAP Note 3255746 (RFC ban), public‑cloud API limits - KBAs 3542227 / 3391018.
  \n
  \n
\n
• Week 3 – Pilot proof: \n
  \n
  • Prototype migration of two representative objects (e.g., BP from a legacy non-SAP ERP, Item Master from a data lake).
  \n
  • Using direct connectors or CSV extracting when third‑party connectors are not in place.
  \n
  • Measure throughput, error rates, and delta‑load feasibility.
  \n
  \n
\n
• Week 4 – Roadmap & business case: \n
  \n
  • Migration roadmap (timeline, budget, roles)
  \n
  • Cut‑over & rollback playbook
  \n
  • Executive‑ready business case deck and Q&A
  \n
  \n
\n

As a result, you will have the following aspects of your GROW with SAP migration ready:

\n
• High-level Migration blueprin — target architecture, data‑flow diagrams, tooling stack
\n
• Pilot results report — load metrics, issue log, remediation actions
\n
• Data quality & cleansing report — ranked by risk and impact
\n
• Cut-over playbook — a plan with fallback scenarios
\n

With our offering, your business will be fully informed on how to perform data migration to the new platform.

Business Benefits of GROW with SAP in Plain Terms

Switching to SAP Cloud ERP using the GROW with SAP journey brings tangible value for mid-market businesses that can hardly be overestimated. Taking on new cloud technologies and transferring to the SAP ecosystem opens up unlimited possibilities for business scalability, overall growth, and further digital transformation, all of which are crucial for staying competitive in ever-evolving and fluctuating markets.

Streamlined operations

GROW with SAP is a digital transformation journey tailored to help mid-market businesses that aspire to enhance operational efficiency. The platform unifies critical processes from supply chain logistics to customer engagement, which allows users to get real-time insights across the organization. This unmatched level of transparency empowers businesses to pinpoint process gaps and inefficiencies, introduce data-driven improvements, and boost overall performance.

Lower TCO

As GROW with SAP operates in the cloud, using the system eliminates the need for mid-market companies to purchase costly hardware infrastructure or software. This dramatically reduces upfront IT expenses, decreasing TCO (total cost of ownership). Consequently, GROW with SAP frees up resources that can be redirected toward strategic initiatives to fuel growth and increase profitability. GROW with SAP also offers no-code development options that empower businesses to tailor applications, create custom solutions, and automate workflows without the need for a dedicated team of developers.

Improved decision-making

GROW with SAP utilizes SAP Cloud ERP, integrating sophisticated analytics and machine learning capabilities. This empowers mid-market businesses to gain deeper visibility into their operations, supporting smarter, data-driven decision-making. By harnessing these insights, companies are better positioned to accelerate growth and enhance profitability.

Enhanced scalability

GROW with SAP is built to evolve alongside mid-market businesses, offering a flexible solution that adapts to each company's specific requirements. Scalable by nature, GROW with SAP ensures that organizations can easily tap into cutting-edge technologies and innovations without the burden of heavy IT infrastructure investments.

Integrations made easy

As part of the SAP ecosystem, GROW with SAP allows companies to seamlessly implement SAP Business Suite for evolving business needs. This allows enterprises to maintain their data and operations in a single source of truth, streamlining processes and knowledge sharing with no need for third-party integrations or switching between different systems.

Enhanced customer experience

GROW with SAP delivers a set of powerful features aimed at elevating customer experience. From personalized recommendations to advanced analytics, the solution equips mid-market businesses with deep insights into customer behaviors and preferences. Armed with this knowledge, companies can fine-tune their offerings to better align with customer expectations, ultimately improving satisfaction and fostering long-term loyalty.

Better security and compliance

All SAP solutions offer outstanding security features and ensure compliance with local and global industry standards, and GROW with SAP is no exception. The solution is equipped with features like data encryption, access controls, and continuous monitoring, which helps safeguard sensitive business information. Meanwhile, the adherence to compliance standards gives mid-market companies the confidence to operate securely in a global marketplace.

Conclusion

GROW with SAP’s customer-journey program is a transformative opportunity for mid-market businesses aiming to modernize their operations, embrace cloud innovation, and future-proof their growth. By harnessing the capabilities of SAP Cloud ERP and SAP BTP, this digital transformation solution empowers companies to streamline processes, reduce costs, and make smarter data-driven decisions.

While migrating your data and processes to SAP Cloud ERP is comparatively easy, you still need a carefully prepared migration plan. Also, make sure to utilize professional data management solutions that ensure your data is fully prepared to be transferred to a new system. Combining GROW with SAP with the right guidance, tools, and support, your business can confidently transition to a cloud-first environment and unlock its full potential.

Check out a step-by-step guide on how to start your GROW with SAP journey to successfully prepare your enterprise data for migration to the new environment.

Check out a step-by-step guide on how to start your GROW with SAP journey to successfully prepare your enterprise data for migration to the new environment.

Check out a step-by-step guide on how to start your GROW with SAP journey to successfully prepare your enterprise data for migration to the new environment.

Getting Started With GROW with SAP Journey: A Step-by-Step Guide

Mid-market businesses often need to modernize their ERP landscape quickly but lack the resources for long, multi-year programs. GROW with SAP is SAP's customer-journey program that accelerates time-to-value through prescriptive methodology, pre-configured content, and partner expertise. Customers purchase one of the new SAP Business Suite packages (e.g., SAP Finance Base), then expand by adding further lines of business.

This guide underlines the value of GROW with SAP for mid-market businesses and explains how the journey works, how to select the right package, and which technology pillars power a clean-core migration.

Choosing the Right SAP Business Suite Package

GROW with SAP allows mid-market businesses to streamline their ERP journey to SAP Business Suite implementation. It divides the ERP bundle into clear, modular SAP Business Suite packages that are easier for businesses to adopt. Instead of a one-size-fits-all approach, you begin with the area most critical to your business (such as Finance or Supply Chain) and grow from there.

Each package includes ready-to-run business processes, automation, and best practices specific to that function. This accelerates deployment and minimizes configuration.

Here's what you can choose, based on your business priorities and needs:

Add-ons: flexible extensions for deeper capabilities

Once you go live with your base package, you can add more functionality using Level 1 and/or Level 2 add-ons:

\n
• Level 1 Add-ons include: Closing & Consolidation, Treasury & Banking, Planning, Manufacturing, and Assets & Service
\n
• Level 2 Add-ons provide: industry-specific or region-specific enhancements (e.g., Public Sector, Automotive, Japan localization)
\n

These add-ons are optional and modular, so you only pay for what you need, when you need it.

Practical selection advice

Here are some tips to add more value to your GROW with SAP journey:

\n
• Start with the biggest pain point. If finance struggles with month-end closing, start with SAP Finance Base.
\n
• Grow organically. Add procurement, supply chain, or HR once your core package is stable.
\n
• Use pre-built best practices. Every package includes standardized workflows tested across industries, so you don’t start from scratch.
\n
• Speed up deployment with SAP Activate. This methodology includes templates, project timelines, and best practices to ensure a smooth implementation.
\n

Whether you're a growing business modernizing your first ERP or a large enterprise shifting from legacy systems, the SAP Business Suite packages give you a scalable way to start simple and evolve with your needs.

Technology Pillars To Power Your Transformation Journey

A successful GROW with SAP project relies on four complementary pillars, or products, that work together to support your business transformation.

SAP Cloud ERP

The SaaS foundation (marketing name for SAP S/4HANA Cloud Public Edition) delivers quarterly innovations and a clean-core architecture. It allows organizations to adopt best practices with minimal IT effort.

SAP Business Technology Platform

SAP BTP provides five capability areas:

\n
• Application Development — build apps without custom code
\n
• Automation — create workflows and bots to reduce manual work
\n
• Integration — connect SAP and non-SAP systems seamlessly
\n
• Data & Analytics — access real-time dashboards and KPIs
\n
• AI — embed intelligence into every business process
\n

SAP Business Data Cloud

This turns data from SAP and external systems into governed data products that are trusted, reusable and ready for reporting, planning, and AI. Built on SAP Datasphere, it enables a consistent data fabric across the enterprise.

SAP Business AI

Business AI infuses generative AI into core apps. You get AI-driven insights, copilots like SAP Joule, and automation that boosts decision-making without coding.

GROW with SAP Migration Roadmap — What To Expect and When

Migrating to SAP Cloud ERP doesn’t require a massive replatforming. Many companies follow a proven two-month approach:

Key stakeholders: Business process owners, IT data leads, partner consultants, and key SAP users from each functional area.

Common tools used:

\n
• DataLark for data extraction, validation, rule-based transformation, and loading to SAP Migration Cockpit
\n
• SAP Datasphere for real-time replication
\n
• SAP APIs for bulk loading (OData, SOAP)
\n

Choosing the Right Migration Partner

Most GROW with SAP customers benefit from expert-led migration. Mid-market businesses often lack in-house IT resources or ERP expertise. Working with a trusted partner ensures smooth migration while your team focuses on business operations.

After choosing a migration partner, preparing a readiness assessment of your landscape is essential to ensure that your legacy system is ready to transfer. Make sure to:

\n
• Evaluate data quality
\n
• Define migration scope and fallback plans
\n
• Run testing cycles
\n

DataLark’s Unified Data‑Migration Assessment & Roadmap offer is your opportunity to kill not two, but four birds with one stone; our experts will closely collaborate with you to create a data migration strategy, assess data, conduct testing, and prepare a plan of fallback scenarios.

This offer is a four‑week consulting sprint that designs a safe, cost‑balanced path to move master and transactional data from SAP ECC and non‑SAP systems into SAP ERP (public or private edition). In the case of GROW with SAP, the data will be moved from a legacy non-SAP source to SAP Cloud ERP.

Our experts combine SAP Migration Cockpit, SAP Datasphere replication flows, and SAP Integration Suite APIs to deliver an actionable migration blueprint, a two‑object pilot proof, and a cut‑over playbook. For scenarios involving complex field mappings or integrations between SAP and non-SAP systems, which is a common case for GROW with SAP, we use DataLark - an SAP-centric data management platform designed by LeverX to streamline the migration process.

Here’s what happens during these four weeks:

\n
• \n

  Week 1 – Discovery:

  \n
  \n
  • Landscape inventory (third‑party ERPs and data lakes)
  \n
  • Data volume and quality profiling
  \n
  • Stakeholder workshops and risk register
  \n
  \n
\n
• Week 2 – Design: \n
  \n
  • Migration‑path scoring (Greenfield approach for GROW).
  \n
  • Tooling mix definition: SAP Migration Cockpit, Datasphere replication, SOAP Bulk APIs, OData V4, DataLark.
  \n
  • Compliance checks: clean‑core, SAP Note 3255746 (RFC ban), public‑cloud API limits - KBAs 3542227 / 3391018.
  \n
  \n
\n
• Week 3 – Pilot proof: \n
  \n
  • Prototype migration of two representative objects (e.g., BP from a legacy non-SAP ERP, Item Master from a data lake).
  \n
  • Using direct connectors or CSV extracting when third‑party connectors are not in place.
  \n
  • Measure throughput, error rates, and delta‑load feasibility.
  \n
  \n
\n
• Week 4 – Roadmap & business case: \n
  \n
  • Migration roadmap (timeline, budget, roles)
  \n
  • Cut‑over & rollback playbook
  \n
  • Executive‑ready business case deck and Q&A
  \n
  \n
\n

As a result, you will have the following aspects of your GROW with SAP migration ready:

\n
• High-level Migration blueprin — target architecture, data‑flow diagrams, tooling stack
\n
• Pilot results report — load metrics, issue log, remediation actions
\n
• Data quality & cleansing report — ranked by risk and impact
\n
• Cut-over playbook — a plan with fallback scenarios
\n

With our offering, your business will be fully informed on how to perform data migration to the new platform.

Business Benefits of GROW with SAP in Plain Terms

Switching to SAP Cloud ERP using the GROW with SAP journey brings tangible value for mid-market businesses that can hardly be overestimated. Taking on new cloud technologies and transferring to the SAP ecosystem opens up unlimited possibilities for business scalability, overall growth, and further digital transformation, all of which are crucial for staying competitive in ever-evolving and fluctuating markets.

Streamlined operations

GROW with SAP is a digital transformation journey tailored to help mid-market businesses that aspire to enhance operational efficiency. The platform unifies critical processes from supply chain logistics to customer engagement, which allows users to get real-time insights across the organization. This unmatched level of transparency empowers businesses to pinpoint process gaps and inefficiencies, introduce data-driven improvements, and boost overall performance.

Lower TCO

As GROW with SAP operates in the cloud, using the system eliminates the need for mid-market companies to purchase costly hardware infrastructure or software. This dramatically reduces upfront IT expenses, decreasing TCO (total cost of ownership). Consequently, GROW with SAP frees up resources that can be redirected toward strategic initiatives to fuel growth and increase profitability. GROW with SAP also offers no-code development options that empower businesses to tailor applications, create custom solutions, and automate workflows without the need for a dedicated team of developers.

Improved decision-making

GROW with SAP utilizes SAP Cloud ERP, integrating sophisticated analytics and machine learning capabilities. This empowers mid-market businesses to gain deeper visibility into their operations, supporting smarter, data-driven decision-making. By harnessing these insights, companies are better positioned to accelerate growth and enhance profitability.

Enhanced scalability

GROW with SAP is built to evolve alongside mid-market businesses, offering a flexible solution that adapts to each company's specific requirements. Scalable by nature, GROW with SAP ensures that organizations can easily tap into cutting-edge technologies and innovations without the burden of heavy IT infrastructure investments.

Integrations made easy

As part of the SAP ecosystem, GROW with SAP allows companies to seamlessly implement SAP Business Suite for evolving business needs. This allows enterprises to maintain their data and operations in a single source of truth, streamlining processes and knowledge sharing with no need for third-party integrations or switching between different systems.

Enhanced customer experience

GROW with SAP delivers a set of powerful features aimed at elevating customer experience. From personalized recommendations to advanced analytics, the solution equips mid-market businesses with deep insights into customer behaviors and preferences. Armed with this knowledge, companies can fine-tune their offerings to better align with customer expectations, ultimately improving satisfaction and fostering long-term loyalty.

Better security and compliance

All SAP solutions offer outstanding security features and ensure compliance with local and global industry standards, and GROW with SAP is no exception. The solution is equipped with features like data encryption, access controls, and continuous monitoring, which helps safeguard sensitive business information. Meanwhile, the adherence to compliance standards gives mid-market companies the confidence to operate securely in a global marketplace.

Conclusion

GROW with SAP’s customer-journey program is a transformative opportunity for mid-market businesses aiming to modernize their operations, embrace cloud innovation, and future-proof their growth. By harnessing the capabilities of SAP Cloud ERP and SAP BTP, this digital transformation solution empowers companies to streamline processes, reduce costs, and make smarter data-driven decisions.

While migrating your data and processes to SAP Cloud ERP is comparatively easy, you still need a carefully prepared migration plan. Also, make sure to utilize professional data management solutions that ensure your data is fully prepared to be transferred to a new system. Combining GROW with SAP with the right guidance, tools, and support, your business can confidently transition to a cloud-first environment and unlock its full potential.

Check out a step-by-step guide on how to start your GROW with SAP journey to successfully prepare your enterprise data for migration to the new environment.

Check out a step-by-step guide on how to start your GROW with SAP journey to successfully prepare your enterprise data for migration to the new environment.

Check out a step-by-step guide on how to start your GROW with SAP journey to successfully prepare your enterprise data for migration to the new environment.

Getting Started With GROW with SAP Journey: A Step-by-Step Guide

Mid-market businesses often need to modernize their ERP landscape quickly but lack the resources for long, multi-year programs. GROW with SAP is SAP's customer-journey program that accelerates time-to-value through prescriptive methodology, pre-configured content, and partner expertise. Customers purchase one of the new SAP Business Suite packages (e.g., SAP Finance Base), then expand by adding further lines of business.

This guide underlines the value of GROW with SAP for mid-market businesses and explains how the journey works, how to select the right package, and which technology pillars power a clean-core migration.

Choosing the Right SAP Business Suite Package

GROW with SAP allows mid-market businesses to streamline their ERP journey to SAP Business Suite implementation. It divides the ERP bundle into clear, modular SAP Business Suite packages that are easier for businesses to adopt. Instead of a one-size-fits-all approach, you begin with the area most critical to your business (such as Finance or Supply Chain) and grow from there.

Each package includes ready-to-run business processes, automation, and best practices specific to that function. This accelerates deployment and minimizes configuration.

Here's what you can choose, based on your business priorities and needs:

Add-ons: flexible extensions for deeper capabilities

Once you go live with your base package, you can add more functionality using Level 1 and/or Level 2 add-ons:

\n
• Level 1 Add-ons include: Closing & Consolidation, Treasury & Banking, Planning, Manufacturing, and Assets & Service
\n
• Level 2 Add-ons provide: industry-specific or region-specific enhancements (e.g., Public Sector, Automotive, Japan localization)
\n

These add-ons are optional and modular, so you only pay for what you need, when you need it.

Practical selection advice

Here are some tips to add more value to your GROW with SAP journey:

\n
• Start with the biggest pain point. If finance struggles with month-end closing, start with SAP Finance Base.
\n
• Grow organically. Add procurement, supply chain, or HR once your core package is stable.
\n
• Use pre-built best practices. Every package includes standardized workflows tested across industries, so you don’t start from scratch.
\n
• Speed up deployment with SAP Activate. This methodology includes templates, project timelines, and best practices to ensure a smooth implementation.
\n

Whether you're a growing business modernizing your first ERP or a large enterprise shifting from legacy systems, the SAP Business Suite packages give you a scalable way to start simple and evolve with your needs.

Technology Pillars To Power Your Transformation Journey

A successful GROW with SAP project relies on four complementary pillars, or products, that work together to support your business transformation.

SAP Cloud ERP

The SaaS foundation (marketing name for SAP S/4HANA Cloud Public Edition) delivers quarterly innovations and a clean-core architecture. It allows organizations to adopt best practices with minimal IT effort.

SAP Business Technology Platform

SAP BTP provides five capability areas:

\n
• Application Development — build apps without custom code
\n
• Automation — create workflows and bots to reduce manual work
\n
• Integration — connect SAP and non-SAP systems seamlessly
\n
• Data & Analytics — access real-time dashboards and KPIs
\n
• AI — embed intelligence into every business process
\n

SAP Business Data Cloud

This turns data from SAP and external systems into governed data products that are trusted, reusable and ready for reporting, planning, and AI. Built on SAP Datasphere, it enables a consistent data fabric across the enterprise.

SAP Business AI

Business AI infuses generative AI into core apps. You get AI-driven insights, copilots like SAP Joule, and automation that boosts decision-making without coding.

GROW with SAP Migration Roadmap — What To Expect and When

Migrating to SAP Cloud ERP doesn’t require a massive replatforming. Many companies follow a proven two-month approach:

Key stakeholders: Business process owners, IT data leads, partner consultants, and key SAP users from each functional area.

Common tools used:

\n
• DataLark for data extraction, validation, rule-based transformation, and loading to SAP Migration Cockpit
\n
• SAP Datasphere for real-time replication
\n
• SAP APIs for bulk loading (OData, SOAP)
\n

Choosing the Right Migration Partner

Most GROW with SAP customers benefit from expert-led migration. Mid-market businesses often lack in-house IT resources or ERP expertise. Working with a trusted partner ensures smooth migration while your team focuses on business operations.

After choosing a migration partner, preparing a readiness assessment of your landscape is essential to ensure that your legacy system is ready to transfer. Make sure to:

\n
• Evaluate data quality
\n
• Define migration scope and fallback plans
\n
• Run testing cycles
\n

DataLark’s Unified Data‑Migration Assessment & Roadmap offer is your opportunity to kill not two, but four birds with one stone; our experts will closely collaborate with you to create a data migration strategy, assess data, conduct testing, and prepare a plan of fallback scenarios.

This offer is a four‑week consulting sprint that designs a safe, cost‑balanced path to move master and transactional data from SAP ECC and non‑SAP systems into SAP ERP (public or private edition). In the case of GROW with SAP, the data will be moved from a legacy non-SAP source to SAP Cloud ERP.

Our experts combine SAP Migration Cockpit, SAP Datasphere replication flows, and SAP Integration Suite APIs to deliver an actionable migration blueprint, a two‑object pilot proof, and a cut‑over playbook. For scenarios involving complex field mappings or integrations between SAP and non-SAP systems, which is a common case for GROW with SAP, we use DataLark - an SAP-centric data management platform designed by LeverX to streamline the migration process.

Here’s what happens during these four weeks:

\n
• \n

  Week 1 – Discovery:

  \n
  \n
  • Landscape inventory (third‑party ERPs and data lakes)
  \n
  • Data volume and quality profiling
  \n
  • Stakeholder workshops and risk register
  \n
  \n
\n
• Week 2 – Design: \n
  \n
  • Migration‑path scoring (Greenfield approach for GROW).
  \n
  • Tooling mix definition: SAP Migration Cockpit, Datasphere replication, SOAP Bulk APIs, OData V4, DataLark.
  \n
  • Compliance checks: clean‑core, SAP Note 3255746 (RFC ban), public‑cloud API limits - KBAs 3542227 / 3391018.
  \n
  \n
\n
• Week 3 – Pilot proof: \n
  \n
  • Prototype migration of two representative objects (e.g., BP from a legacy non-SAP ERP, Item Master from a data lake).
  \n
  • Using direct connectors or CSV extracting when third‑party connectors are not in place.
  \n
  • Measure throughput, error rates, and delta‑load feasibility.
  \n
  \n
\n
• Week 4 – Roadmap & business case: \n
  \n
  • Migration roadmap (timeline, budget, roles)
  \n
  • Cut‑over & rollback playbook
  \n
  • Executive‑ready business case deck and Q&A
  \n
  \n
\n

As a result, you will have the following aspects of your GROW with SAP migration ready:

\n
• High-level Migration blueprin — target architecture, data‑flow diagrams, tooling stack
\n
• Pilot results report — load metrics, issue log, remediation actions
\n
• Data quality & cleansing report — ranked by risk and impact
\n
• Cut-over playbook — a plan with fallback scenarios
\n

With our offering, your business will be fully informed on how to perform data migration to the new platform.

Business Benefits of GROW with SAP in Plain Terms

Switching to SAP Cloud ERP using the GROW with SAP journey brings tangible value for mid-market businesses that can hardly be overestimated. Taking on new cloud technologies and transferring to the SAP ecosystem opens up unlimited possibilities for business scalability, overall growth, and further digital transformation, all of which are crucial for staying competitive in ever-evolving and fluctuating markets.

Streamlined operations

GROW with SAP is a digital transformation journey tailored to help mid-market businesses that aspire to enhance operational efficiency. The platform unifies critical processes from supply chain logistics to customer engagement, which allows users to get real-time insights across the organization. This unmatched level of transparency empowers businesses to pinpoint process gaps and inefficiencies, introduce data-driven improvements, and boost overall performance.

Lower TCO

As GROW with SAP operates in the cloud, using the system eliminates the need for mid-market companies to purchase costly hardware infrastructure or software. This dramatically reduces upfront IT expenses, decreasing TCO (total cost of ownership). Consequently, GROW with SAP frees up resources that can be redirected toward strategic initiatives to fuel growth and increase profitability. GROW with SAP also offers no-code development options that empower businesses to tailor applications, create custom solutions, and automate workflows without the need for a dedicated team of developers.

Improved decision-making

GROW with SAP utilizes SAP Cloud ERP, integrating sophisticated analytics and machine learning capabilities. This empowers mid-market businesses to gain deeper visibility into their operations, supporting smarter, data-driven decision-making. By harnessing these insights, companies are better positioned to accelerate growth and enhance profitability.

Enhanced scalability

GROW with SAP is built to evolve alongside mid-market businesses, offering a flexible solution that adapts to each company's specific requirements. Scalable by nature, GROW with SAP ensures that organizations can easily tap into cutting-edge technologies and innovations without the burden of heavy IT infrastructure investments.

Integrations made easy

As part of the SAP ecosystem, GROW with SAP allows companies to seamlessly implement SAP Business Suite for evolving business needs. This allows enterprises to maintain their data and operations in a single source of truth, streamlining processes and knowledge sharing with no need for third-party integrations or switching between different systems.

Enhanced customer experience

GROW with SAP delivers a set of powerful features aimed at elevating customer experience. From personalized recommendations to advanced analytics, the solution equips mid-market businesses with deep insights into customer behaviors and preferences. Armed with this knowledge, companies can fine-tune their offerings to better align with customer expectations, ultimately improving satisfaction and fostering long-term loyalty.

Better security and compliance

All SAP solutions offer outstanding security features and ensure compliance with local and global industry standards, and GROW with SAP is no exception. The solution is equipped with features like data encryption, access controls, and continuous monitoring, which helps safeguard sensitive business information. Meanwhile, the adherence to compliance standards gives mid-market companies the confidence to operate securely in a global marketplace.

Conclusion

GROW with SAP’s customer-journey program is a transformative opportunity for mid-market businesses aiming to modernize their operations, embrace cloud innovation, and future-proof their growth. By harnessing the capabilities of SAP Cloud ERP and SAP BTP, this digital transformation solution empowers companies to streamline processes, reduce costs, and make smarter data-driven decisions.

While migrating your data and processes to SAP Cloud ERP is comparatively easy, you still need a carefully prepared migration plan. Also, make sure to utilize professional data management solutions that ensure your data is fully prepared to be transferred to a new system. Combining GROW with SAP with the right guidance, tools, and support, your business can confidently transition to a cloud-first environment and unlock its full potential.

Check out a step-by-step guide on how to start your GROW with SAP journey to successfully prepare your enterprise data for migration to the new environment.

Unlocking the Future of Enterprise AI: DataLark’s Takeaways from SAP Sapphire 2025

The energy at SAP Sapphire 2025 in Orlando was electrifying. This year’s event solidified its role as the premier platform for showcasing the innovations shaping the future of digital business. For the DataLark team, exhibiting at Sapphire was not just an opportunity to demonstrate our capabilities — it was a chance to connect with thought leaders, explore groundbreaking technologies, and gain valuable insight into the trends driving the next wave of enterprise transformation.

We’re excited to share our top takeaways from the event and highlight how DataLark is helping customers thrive in this new era of intelligent enterprise.

Agentic AI is Taking Center Stage

SAP’s introduction of Joule Agents and the expanded AI Foundation on BTP was a highlight of Sapphire 2025. These agents aren't just automation scripts — they reason, act, and interact across SAP and non-SAP systems (like Gmail and ServiceNow), making AI a front-line business enabler. With features like Prompt Optimizer, SAP is eliminating the complexity of prompt engineering and ushering in what it calls “benchmark engineering.”

At DataLark, we’re already on this path — delivering the clean, structured, SAP-ready data that fuels agentic workflows and AI decisions.

The Era of Intelligent Automation Demands High-Quality Data

One consistent theme across keynotes, panels, and customer conversations was the crucial role of data quality in AI success. As organizations increasingly rely on AI-driven insights to steer operations, the cost of poor data governance becomes too high to ignore. Inaccurate, incomplete, or inconsistent data undermines trust in automation and can lead to misguided decisions.

DataLark’s commitment to delivering robust SAP data quality management solutions has never been more relevant. Our tools help enterprises cleanse, validate, and govern their data, ensuring a strong foundation for AI initiatives. We empower our customers to make better decisions, faster — backed by clean, trustworthy data.

Clean Core Strategies Are Accelerating

Another key trend at Sapphire 2025 was the widespread adoption of clean core principles. SAP’s emphasis on modular, cloud-native architectures through the RISE and GROW with SAP programs is reshaping how businesses think about system landscapes. The clean core approach reduces complexity, enhances agility, and facilitates innovation by minimizing customizations and leveraging standardized extensions.

DataLark is proud to support customers on this journey. Our cloud-ready solutions are designed to integrate seamlessly into clean core architectures, offering flexibility without compromise. Whether you’re transitioning to S/4HANA or optimizing your existing SAP environment, we’re here to help streamline the path to a scalable, future-proof enterprise.

Faster Time-to-Value Will Define the Winners

Across Sapphire, one message was loud and clear: organizations want to implement AI fast. SAP’s goal to make business users 30% more productive through natural language, not deep transactional screens, is bold - but realistic with the right data and tooling.

Partners who can shorten implementation timelines and enable clean-core adoption without heavy customization will be in high demand. That’s where DataLark fits in - our platform is built to accelerate delivery, reduce integration friction, and get your data working from day one.

Looking Ahead: Innovating with Purpose

We extend our heartfelt thanks to everyone who stopped by the DataLark booth, participated in demos, and shared their insights. Your engagement fuels our mission to deliver smarter, simpler, and more sustainable SAP solutions.

SAP Sapphire 2025 reaffirmed a core belief: the future of enterprise isn’t about stacking tools - it’s about simplifying, streamlining, and scaling through intelligent, connected systems. At DataLark, we’re committed to making that future real - with clean data, faster projects, and smarter automation.

Check out our top takeaways from SAP Sapphire 2025 in Orlando. Learn how SAP is leveling up enterprise AI, automation, and clean core strategies.

Check out our top takeaways from SAP Sapphire 2025 in Orlando. Learn how SAP is leveling up enterprise AI, automation, and clean core strategies.

Unlocking the Future of Enterprise AI: DataLark’s Takeaways from SAP Sapphire 2025

The energy at SAP Sapphire 2025 in Orlando was electrifying. This year’s event solidified its role as the premier platform for showcasing the innovations shaping the future of digital business. For the DataLark team, exhibiting at Sapphire was not just an opportunity to demonstrate our capabilities — it was a chance to connect with thought leaders, explore groundbreaking technologies, and gain valuable insight into the trends driving the next wave of enterprise transformation.

We’re excited to share our top takeaways from the event and highlight how DataLark is helping customers thrive in this new era of intelligent enterprise.

Agentic AI is Taking Center Stage

SAP’s introduction of Joule Agents and the expanded AI Foundation on BTP was a highlight of Sapphire 2025. These agents aren't just automation scripts — they reason, act, and interact across SAP and non-SAP systems (like Gmail and ServiceNow), making AI a front-line business enabler. With features like Prompt Optimizer, SAP is eliminating the complexity of prompt engineering and ushering in what it calls “benchmark engineering.”

At DataLark, we’re already on this path — delivering the clean, structured, SAP-ready data that fuels agentic workflows and AI decisions.

The Era of Intelligent Automation Demands High-Quality Data

One consistent theme across keynotes, panels, and customer conversations was the crucial role of data quality in AI success. As organizations increasingly rely on AI-driven insights to steer operations, the cost of poor data governance becomes too high to ignore. Inaccurate, incomplete, or inconsistent data undermines trust in automation and can lead to misguided decisions.

DataLark’s commitment to delivering robust SAP data quality management solutions has never been more relevant. Our tools help enterprises cleanse, validate, and govern their data, ensuring a strong foundation for AI initiatives. We empower our customers to make better decisions, faster — backed by clean, trustworthy data.

Clean Core Strategies Are Accelerating

Another key trend at Sapphire 2025 was the widespread adoption of clean core principles. SAP’s emphasis on modular, cloud-native architectures through the RISE and GROW with SAP programs is reshaping how businesses think about system landscapes. The clean core approach reduces complexity, enhances agility, and facilitates innovation by minimizing customizations and leveraging standardized extensions.

DataLark is proud to support customers on this journey. Our cloud-ready solutions are designed to integrate seamlessly into clean core architectures, offering flexibility without compromise. Whether you’re transitioning to S/4HANA or optimizing your existing SAP environment, we’re here to help streamline the path to a scalable, future-proof enterprise.

Faster Time-to-Value Will Define the Winners

Across Sapphire, one message was loud and clear: organizations want to implement AI fast. SAP’s goal to make business users 30% more productive through natural language, not deep transactional screens, is bold - but realistic with the right data and tooling.

Partners who can shorten implementation timelines and enable clean-core adoption without heavy customization will be in high demand. That’s where DataLark fits in - our platform is built to accelerate delivery, reduce integration friction, and get your data working from day one.

Looking Ahead: Innovating with Purpose

We extend our heartfelt thanks to everyone who stopped by the DataLark booth, participated in demos, and shared their insights. Your engagement fuels our mission to deliver smarter, simpler, and more sustainable SAP solutions.

SAP Sapphire 2025 reaffirmed a core belief: the future of enterprise isn’t about stacking tools - it’s about simplifying, streamlining, and scaling through intelligent, connected systems. At DataLark, we’re committed to making that future real - with clean data, faster projects, and smarter automation.

Check out our top takeaways from SAP Sapphire 2025 in Orlando. Learn how SAP is leveling up enterprise AI, automation, and clean core strategies.

Check out our top takeaways from SAP Sapphire 2025 in Orlando. Learn how SAP is leveling up enterprise AI, automation, and clean core strategies.

Unlocking the Future of Enterprise AI: DataLark’s Takeaways from SAP Sapphire 2025

The energy at SAP Sapphire 2025 in Orlando was electrifying. This year’s event solidified its role as the premier platform for showcasing the innovations shaping the future of digital business. For the DataLark team, exhibiting at Sapphire was not just an opportunity to demonstrate our capabilities — it was a chance to connect with thought leaders, explore groundbreaking technologies, and gain valuable insight into the trends driving the next wave of enterprise transformation.

We’re excited to share our top takeaways from the event and highlight how DataLark is helping customers thrive in this new era of intelligent enterprise.

Agentic AI is Taking Center Stage

SAP’s introduction of Joule Agents and the expanded AI Foundation on BTP was a highlight of Sapphire 2025. These agents aren't just automation scripts — they reason, act, and interact across SAP and non-SAP systems (like Gmail and ServiceNow), making AI a front-line business enabler. With features like Prompt Optimizer, SAP is eliminating the complexity of prompt engineering and ushering in what it calls “benchmark engineering.”

At DataLark, we’re already on this path — delivering the clean, structured, SAP-ready data that fuels agentic workflows and AI decisions.

The Era of Intelligent Automation Demands High-Quality Data

One consistent theme across keynotes, panels, and customer conversations was the crucial role of data quality in AI success. As organizations increasingly rely on AI-driven insights to steer operations, the cost of poor data governance becomes too high to ignore. Inaccurate, incomplete, or inconsistent data undermines trust in automation and can lead to misguided decisions.

DataLark’s commitment to delivering robust SAP data quality management solutions has never been more relevant. Our tools help enterprises cleanse, validate, and govern their data, ensuring a strong foundation for AI initiatives. We empower our customers to make better decisions, faster — backed by clean, trustworthy data.

Clean Core Strategies Are Accelerating

Another key trend at Sapphire 2025 was the widespread adoption of clean core principles. SAP’s emphasis on modular, cloud-native architectures through the RISE and GROW with SAP programs is reshaping how businesses think about system landscapes. The clean core approach reduces complexity, enhances agility, and facilitates innovation by minimizing customizations and leveraging standardized extensions.

DataLark is proud to support customers on this journey. Our cloud-ready solutions are designed to integrate seamlessly into clean core architectures, offering flexibility without compromise. Whether you’re transitioning to S/4HANA or optimizing your existing SAP environment, we’re here to help streamline the path to a scalable, future-proof enterprise.

Faster Time-to-Value Will Define the Winners

Across Sapphire, one message was loud and clear: organizations want to implement AI fast. SAP’s goal to make business users 30% more productive through natural language, not deep transactional screens, is bold - but realistic with the right data and tooling.

Partners who can shorten implementation timelines and enable clean-core adoption without heavy customization will be in high demand. That’s where DataLark fits in - our platform is built to accelerate delivery, reduce integration friction, and get your data working from day one.

Looking Ahead: Innovating with Purpose

We extend our heartfelt thanks to everyone who stopped by the DataLark booth, participated in demos, and shared their insights. Your engagement fuels our mission to deliver smarter, simpler, and more sustainable SAP solutions.

SAP Sapphire 2025 reaffirmed a core belief: the future of enterprise isn’t about stacking tools - it’s about simplifying, streamlining, and scaling through intelligent, connected systems. At DataLark, we’re committed to making that future real - with clean data, faster projects, and smarter automation.

Check out our top takeaways from SAP Sapphire 2025 in Orlando. Learn how SAP is leveling up enterprise AI, automation, and clean core strategies.

Unlocking the Future of Enterprise AI: DataLark’s Takeaways from SAP Sapphire 2025

The energy at SAP Sapphire 2025 in Orlando was electrifying. This year’s event solidified its role as the premier platform for showcasing the innovations shaping the future of digital business. For the DataLark team, exhibiting at Sapphire was not just an opportunity to demonstrate our capabilities — it was a chance to connect with thought leaders, explore groundbreaking technologies, and gain valuable insight into the trends driving the next wave of enterprise transformation.

We’re excited to share our top takeaways from the event and highlight how DataLark is helping customers thrive in this new era of intelligent enterprise.

Agentic AI is Taking Center Stage

SAP’s introduction of Joule Agents and the expanded AI Foundation on BTP was a highlight of Sapphire 2025. These agents aren't just automation scripts — they reason, act, and interact across SAP and non-SAP systems (like Gmail and ServiceNow), making AI a front-line business enabler. With features like Prompt Optimizer, SAP is eliminating the complexity of prompt engineering and ushering in what it calls “benchmark engineering.”

At DataLark, we’re already on this path — delivering the clean, structured, SAP-ready data that fuels agentic workflows and AI decisions.

The Era of Intelligent Automation Demands High-Quality Data

One consistent theme across keynotes, panels, and customer conversations was the crucial role of data quality in AI success. As organizations increasingly rely on AI-driven insights to steer operations, the cost of poor data governance becomes too high to ignore. Inaccurate, incomplete, or inconsistent data undermines trust in automation and can lead to misguided decisions.

DataLark’s commitment to delivering robust SAP data quality management solutions has never been more relevant. Our tools help enterprises cleanse, validate, and govern their data, ensuring a strong foundation for AI initiatives. We empower our customers to make better decisions, faster — backed by clean, trustworthy data.

Clean Core Strategies Are Accelerating

Another key trend at Sapphire 2025 was the widespread adoption of clean core principles. SAP’s emphasis on modular, cloud-native architectures through the RISE and GROW with SAP programs is reshaping how businesses think about system landscapes. The clean core approach reduces complexity, enhances agility, and facilitates innovation by minimizing customizations and leveraging standardized extensions.

DataLark is proud to support customers on this journey. Our cloud-ready solutions are designed to integrate seamlessly into clean core architectures, offering flexibility without compromise. Whether you’re transitioning to S/4HANA or optimizing your existing SAP environment, we’re here to help streamline the path to a scalable, future-proof enterprise.

Faster Time-to-Value Will Define the Winners

Across Sapphire, one message was loud and clear: organizations want to implement AI fast. SAP’s goal to make business users 30% more productive through natural language, not deep transactional screens, is bold - but realistic with the right data and tooling.

Partners who can shorten implementation timelines and enable clean-core adoption without heavy customization will be in high demand. That’s where DataLark fits in - our platform is built to accelerate delivery, reduce integration friction, and get your data working from day one.

Looking Ahead: Innovating with Purpose

We extend our heartfelt thanks to everyone who stopped by the DataLark booth, participated in demos, and shared their insights. Your engagement fuels our mission to deliver smarter, simpler, and more sustainable SAP solutions.

SAP Sapphire 2025 reaffirmed a core belief: the future of enterprise isn’t about stacking tools - it’s about simplifying, streamlining, and scaling through intelligent, connected systems. At DataLark, we’re committed to making that future real - with clean data, faster projects, and smarter automation.

Check out our top takeaways from SAP Sapphire 2025 in Orlando. Learn how SAP is leveling up enterprise AI, automation, and clean core strategies.

Check out our top takeaways from SAP Sapphire 2025 in Orlando. Learn how SAP is leveling up enterprise AI, automation, and clean core strategies.

Check out our top takeaways from SAP Sapphire 2025 in Orlando. Learn how SAP is leveling up enterprise AI, automation, and clean core strategies.

Unlocking the Future of Enterprise AI: DataLark’s Takeaways from SAP Sapphire 2025

The energy at SAP Sapphire 2025 in Orlando was electrifying. This year’s event solidified its role as the premier platform for showcasing the innovations shaping the future of digital business. For the DataLark team, exhibiting at Sapphire was not just an opportunity to demonstrate our capabilities — it was a chance to connect with thought leaders, explore groundbreaking technologies, and gain valuable insight into the trends driving the next wave of enterprise transformation.

We’re excited to share our top takeaways from the event and highlight how DataLark is helping customers thrive in this new era of intelligent enterprise.

Agentic AI is Taking Center Stage

SAP’s introduction of Joule Agents and the expanded AI Foundation on BTP was a highlight of Sapphire 2025. These agents aren't just automation scripts — they reason, act, and interact across SAP and non-SAP systems (like Gmail and ServiceNow), making AI a front-line business enabler. With features like Prompt Optimizer, SAP is eliminating the complexity of prompt engineering and ushering in what it calls “benchmark engineering.”

At DataLark, we’re already on this path — delivering the clean, structured, SAP-ready data that fuels agentic workflows and AI decisions.

The Era of Intelligent Automation Demands High-Quality Data

One consistent theme across keynotes, panels, and customer conversations was the crucial role of data quality in AI success. As organizations increasingly rely on AI-driven insights to steer operations, the cost of poor data governance becomes too high to ignore. Inaccurate, incomplete, or inconsistent data undermines trust in automation and can lead to misguided decisions.

DataLark’s commitment to delivering robust SAP data quality management solutions has never been more relevant. Our tools help enterprises cleanse, validate, and govern their data, ensuring a strong foundation for AI initiatives. We empower our customers to make better decisions, faster — backed by clean, trustworthy data.

Clean Core Strategies Are Accelerating

Another key trend at Sapphire 2025 was the widespread adoption of clean core principles. SAP’s emphasis on modular, cloud-native architectures through the RISE and GROW with SAP programs is reshaping how businesses think about system landscapes. The clean core approach reduces complexity, enhances agility, and facilitates innovation by minimizing customizations and leveraging standardized extensions.

DataLark is proud to support customers on this journey. Our cloud-ready solutions are designed to integrate seamlessly into clean core architectures, offering flexibility without compromise. Whether you’re transitioning to S/4HANA or optimizing your existing SAP environment, we’re here to help streamline the path to a scalable, future-proof enterprise.

Faster Time-to-Value Will Define the Winners

Across Sapphire, one message was loud and clear: organizations want to implement AI fast. SAP’s goal to make business users 30% more productive through natural language, not deep transactional screens, is bold - but realistic with the right data and tooling.

Partners who can shorten implementation timelines and enable clean-core adoption without heavy customization will be in high demand. That’s where DataLark fits in - our platform is built to accelerate delivery, reduce integration friction, and get your data working from day one.

Looking Ahead: Innovating with Purpose

We extend our heartfelt thanks to everyone who stopped by the DataLark booth, participated in demos, and shared their insights. Your engagement fuels our mission to deliver smarter, simpler, and more sustainable SAP solutions.

SAP Sapphire 2025 reaffirmed a core belief: the future of enterprise isn’t about stacking tools - it’s about simplifying, streamlining, and scaling through intelligent, connected systems. At DataLark, we’re committed to making that future real - with clean data, faster projects, and smarter automation.

Check out our top takeaways from SAP Sapphire 2025 in Orlando. Learn how SAP is leveling up enterprise AI, automation, and clean core strategies.

Check out our top takeaways from SAP Sapphire 2025 in Orlando. Learn how SAP is leveling up enterprise AI, automation, and clean core strategies.

Check out our top takeaways from SAP Sapphire 2025 in Orlando. Learn how SAP is leveling up enterprise AI, automation, and clean core strategies.

Unlocking the Future of Enterprise AI: DataLark’s Takeaways from SAP Sapphire 2025

The energy at SAP Sapphire 2025 in Orlando was electrifying. This year’s event solidified its role as the premier platform for showcasing the innovations shaping the future of digital business. For the DataLark team, exhibiting at Sapphire was not just an opportunity to demonstrate our capabilities — it was a chance to connect with thought leaders, explore groundbreaking technologies, and gain valuable insight into the trends driving the next wave of enterprise transformation.

We’re excited to share our top takeaways from the event and highlight how DataLark is helping customers thrive in this new era of intelligent enterprise.

Agentic AI is Taking Center Stage

SAP’s introduction of Joule Agents and the expanded AI Foundation on BTP was a highlight of Sapphire 2025. These agents aren't just automation scripts — they reason, act, and interact across SAP and non-SAP systems (like Gmail and ServiceNow), making AI a front-line business enabler. With features like Prompt Optimizer, SAP is eliminating the complexity of prompt engineering and ushering in what it calls “benchmark engineering.”

At DataLark, we’re already on this path — delivering the clean, structured, SAP-ready data that fuels agentic workflows and AI decisions.

The Era of Intelligent Automation Demands High-Quality Data

One consistent theme across keynotes, panels, and customer conversations was the crucial role of data quality in AI success. As organizations increasingly rely on AI-driven insights to steer operations, the cost of poor data governance becomes too high to ignore. Inaccurate, incomplete, or inconsistent data undermines trust in automation and can lead to misguided decisions.

DataLark’s commitment to delivering robust SAP data quality management solutions has never been more relevant. Our tools help enterprises cleanse, validate, and govern their data, ensuring a strong foundation for AI initiatives. We empower our customers to make better decisions, faster — backed by clean, trustworthy data.

Clean Core Strategies Are Accelerating

Another key trend at Sapphire 2025 was the widespread adoption of clean core principles. SAP’s emphasis on modular, cloud-native architectures through the RISE and GROW with SAP programs is reshaping how businesses think about system landscapes. The clean core approach reduces complexity, enhances agility, and facilitates innovation by minimizing customizations and leveraging standardized extensions.

DataLark is proud to support customers on this journey. Our cloud-ready solutions are designed to integrate seamlessly into clean core architectures, offering flexibility without compromise. Whether you’re transitioning to S/4HANA or optimizing your existing SAP environment, we’re here to help streamline the path to a scalable, future-proof enterprise.

Faster Time-to-Value Will Define the Winners

Across Sapphire, one message was loud and clear: organizations want to implement AI fast. SAP’s goal to make business users 30% more productive through natural language, not deep transactional screens, is bold - but realistic with the right data and tooling.

Partners who can shorten implementation timelines and enable clean-core adoption without heavy customization will be in high demand. That’s where DataLark fits in - our platform is built to accelerate delivery, reduce integration friction, and get your data working from day one.

Looking Ahead: Innovating with Purpose

We extend our heartfelt thanks to everyone who stopped by the DataLark booth, participated in demos, and shared their insights. Your engagement fuels our mission to deliver smarter, simpler, and more sustainable SAP solutions.

SAP Sapphire 2025 reaffirmed a core belief: the future of enterprise isn’t about stacking tools - it’s about simplifying, streamlining, and scaling through intelligent, connected systems. At DataLark, we’re committed to making that future real - with clean data, faster projects, and smarter automation.

Check out our top takeaways from SAP Sapphire 2025 in Orlando. Learn how SAP is leveling up enterprise AI, automation, and clean core strategies.

DataLark Leverages Agentic AI to Elevate S/4HANA Migration

DataLark has always supported a proactive approach to incorporating innovative technologies. For the second year in a row, we’re participating in the SAP Innovation Awards. Last year, we were honored to be selected as finalists — an achievement that inspired us to push the boundaries even further.

To accelerate digital transformation and simplify S/4HANA data migration, we introduced a new agentic AI approach in DataLark. Let’s take a closer look at how it works — and how it helps you reach your migration goals faster and with less effort.

What is Agentic AI?

Agentic AI is an artificial intelligence system that takes actions autonomously to achieve specific goals. Agentic AI systems are designed not only for processing information and making data-driven decisions, but also to act on those decisions, interact with the world, and adapt to its changing conditions.

Key capabilities of Agentic AI:

\n
• Makes decisions based on the environment and input data.
\n
• Learns from outcomes and adapts over time.
\n
• Operates with minimal human supervision.
\n
• Perform actions, ranging from GPS autopiloting (such as delivery robots) to executing tasks in a software environment.
\n

The most prominent examples of agentic AI include autonomous vehicles, software agents that handle end-to-end business processes, and AI in games or simulations where the AI character can interact with the environment and other entities to achieve the goals set.

Agentic AI vs. Generative AI

While both Agentic AI and Generative AI are subsets of artificial intelligence, they serve very different purposes. Generative AI creates content (like text or code) based on input, but it cannot act on its own. Agentic AI, on the other hand, is designed to operate autonomously, making decisions and executing actions with minimal human involvement. This makes it ideal for automating complex, goal-oriented workflows — such as SAP data migration.

The key differences between Agentic AI and Generative AI are summarized in the side-by-side comparison table below.

Why AI Matters in SAP S/4HANA Migration

SAP S/4HANA migration projects require significant time, cost, and resources. Migrating data from heterogeneous legacy systems demands extensive planning, skilled personnel, and significant manual effort to ensure data integrity, compliance, and system compatibility. The key challenges include accelerating data migration, reducing manual workload, and increasing data quality, all while ensuring a seamless transition to SAP S/4HANA with minimal downtime.

Aside from this, many companies lack the expertise to integrate AI-driven automation into ERP migrations, which slows down their digital transformation process and as a result, may leave them behind competitors. All this leads to expensive overprovisioning, business downtime risks, and compliance issues.

How DataLark Leverages Agentic AI

DataLark brings agentic AI to the forefront of SAP S/4HANA migration, transforming the process into a highly automated, intelligent workflow. Built on SAP BTP and integrated with SAP AI Core/AI Foundation, DataLark enables AI-powered orchestration, decision-making, and continuous optimization — reducing manual effort and speeding up every stage of the migration.

What makes it different:

\n
• End-to-end automation: From extraction and transformation to validation and loading, DataLark automates migration flows for both SAP and non-SAP systems.
\n
• Dynamic mapping & validation: AI continuously refines transformation rules and validates data in real time — minimizing rework and errors.
\n
• Built-in intelligence: Self-correcting workflows and anomaly detection reduce reliance on skilled personnel and ensure data quality.
\n
• Seamless SAP integration: The solution works with SAP Migration Cockpit and other standard tools, requiring no extensive custom development.
\n
• Faster, smoother go-lives: With intelligent task sequencing and reduced downtime, businesses transition to SAP S/4HANA with greater speed and confidence.
\n

Agentic AI in Action: Real-World Use Case

As businesses modernize with SAP S/4HANA, seamless data migration becomes a key success factor. While traditional migration approaches require significant manual effort, our customers and implementation teams sought a smarter, more automated way to accelerate the transition while ensuring data accuracy and integrity.

By combining agentic AI and SAP BTP, DataLark allows businesses to redefine their migration experience. DataLark, deployed on SAP BTP, enables AI-driven visual data flows, intelligent mapping, and automated validation, reducing complexity and human effort. SAP AI Core, integrated with external AI providers, enhances data quality, anomaly detection, and self-correcting workflows, ensuring a smooth, error-free migration.

What’s powering the transformation:

\n
• SAP AI Core processes structured and unstructured data, dynamically refining transformation rules with OpenAI-powered (other providers can also be used) processing.
\n
• AI Agents handle automated validation and error resolution, detecting anomalies, self-correcting inconsistencies, and reducing rework.
\n
• Intelligent Workflow Optimization sequences migration steps dynamically, minimizing downtime and ensuring seamless cutovers.
\n

This way, DataLark ensures seamless connectivity between legacy systems and SAP S/4HANA, enabling our customers to achieve faster, more efficient, and cost-effective migrations, and setting a new benchmark for AI-powered SAP transformations.

DataLark’s Agentic AI Benefits

Here are the key business and IT benefits of using agentic AI in DataLark’s data migration workflows.

For Business Teams:

\n
• AI-driven automation reduces manual effort by minimizing human intervention in data mapping, validation, and error resolution.
\n
• Faster data reconciliation with AI-powered validation that detects and corrects discrepancies, significantly reducing time spent on data consistency checks.
\n
• Lower migration costs through reduced manual work, fewer errors, and less rework.
\n

For IT Teams:

\n
• Faster migration through AI-driven automation of repetitive tasks.
\n
• Built-in compliance and anomaly detection using SAP BTP infrastructure.
\n
• Smarter cloud use via intelligent resource optimization.
\n

Bottom Line: AI That Works for You

Digital transformation shouldn’t be a burden — it should be a breakthrough. With agentic AI inside, DataLark shifts the focus from firefighting errors to strategic innovation.

If you’re still weighing your SAP S/4HANA migration options, let this post be your sign: Agentic AI isn’t the future. It’s happening now — with DataLark.

Learn how to leverage DataLark’s agentic AI features to simplify and accelerate your SAP S/4HANA data migration.

Learn how to leverage DataLark’s agentic AI features to simplify and accelerate your SAP S/4HANA data migration.

DataLark Leverages Agentic AI to Elevate S/4HANA Migration

DataLark has always supported a proactive approach to incorporating innovative technologies. For the second year in a row, we’re participating in the SAP Innovation Awards. Last year, we were honored to be selected as finalists — an achievement that inspired us to push the boundaries even further.

To accelerate digital transformation and simplify S/4HANA data migration, we introduced a new agentic AI approach in DataLark. Let’s take a closer look at how it works — and how it helps you reach your migration goals faster and with less effort.

What is Agentic AI?

Agentic AI is an artificial intelligence system that takes actions autonomously to achieve specific goals. Agentic AI systems are designed not only for processing information and making data-driven decisions, but also to act on those decisions, interact with the world, and adapt to its changing conditions.

Key capabilities of Agentic AI:

\n
• Makes decisions based on the environment and input data.
\n
• Learns from outcomes and adapts over time.
\n
• Operates with minimal human supervision.
\n
• Perform actions, ranging from GPS autopiloting (such as delivery robots) to executing tasks in a software environment.
\n

The most prominent examples of agentic AI include autonomous vehicles, software agents that handle end-to-end business processes, and AI in games or simulations where the AI character can interact with the environment and other entities to achieve the goals set.

Agentic AI vs. Generative AI

While both Agentic AI and Generative AI are subsets of artificial intelligence, they serve very different purposes. Generative AI creates content (like text or code) based on input, but it cannot act on its own. Agentic AI, on the other hand, is designed to operate autonomously, making decisions and executing actions with minimal human involvement. This makes it ideal for automating complex, goal-oriented workflows — such as SAP data migration.

The key differences between Agentic AI and Generative AI are summarized in the side-by-side comparison table below.

Why AI Matters in SAP S/4HANA Migration

SAP S/4HANA migration projects require significant time, cost, and resources. Migrating data from heterogeneous legacy systems demands extensive planning, skilled personnel, and significant manual effort to ensure data integrity, compliance, and system compatibility. The key challenges include accelerating data migration, reducing manual workload, and increasing data quality, all while ensuring a seamless transition to SAP S/4HANA with minimal downtime.

Aside from this, many companies lack the expertise to integrate AI-driven automation into ERP migrations, which slows down their digital transformation process and as a result, may leave them behind competitors. All this leads to expensive overprovisioning, business downtime risks, and compliance issues.

How DataLark Leverages Agentic AI

DataLark brings agentic AI to the forefront of SAP S/4HANA migration, transforming the process into a highly automated, intelligent workflow. Built on SAP BTP and integrated with SAP AI Core/AI Foundation, DataLark enables AI-powered orchestration, decision-making, and continuous optimization — reducing manual effort and speeding up every stage of the migration.

What makes it different:

\n
• End-to-end automation: From extraction and transformation to validation and loading, DataLark automates migration flows for both SAP and non-SAP systems.
\n
• Dynamic mapping & validation: AI continuously refines transformation rules and validates data in real time — minimizing rework and errors.
\n
• Built-in intelligence: Self-correcting workflows and anomaly detection reduce reliance on skilled personnel and ensure data quality.
\n
• Seamless SAP integration: The solution works with SAP Migration Cockpit and other standard tools, requiring no extensive custom development.
\n
• Faster, smoother go-lives: With intelligent task sequencing and reduced downtime, businesses transition to SAP S/4HANA with greater speed and confidence.
\n

Agentic AI in Action: Real-World Use Case

As businesses modernize with SAP S/4HANA, seamless data migration becomes a key success factor. While traditional migration approaches require significant manual effort, our customers and implementation teams sought a smarter, more automated way to accelerate the transition while ensuring data accuracy and integrity.

By combining agentic AI and SAP BTP, DataLark allows businesses to redefine their migration experience. DataLark, deployed on SAP BTP, enables AI-driven visual data flows, intelligent mapping, and automated validation, reducing complexity and human effort. SAP AI Core, integrated with external AI providers, enhances data quality, anomaly detection, and self-correcting workflows, ensuring a smooth, error-free migration.

What’s powering the transformation:

\n
• SAP AI Core processes structured and unstructured data, dynamically refining transformation rules with OpenAI-powered (other providers can also be used) processing.
\n
• AI Agents handle automated validation and error resolution, detecting anomalies, self-correcting inconsistencies, and reducing rework.
\n
• Intelligent Workflow Optimization sequences migration steps dynamically, minimizing downtime and ensuring seamless cutovers.
\n

This way, DataLark ensures seamless connectivity between legacy systems and SAP S/4HANA, enabling our customers to achieve faster, more efficient, and cost-effective migrations, and setting a new benchmark for AI-powered SAP transformations.

DataLark’s Agentic AI Benefits

Here are the key business and IT benefits of using agentic AI in DataLark’s data migration workflows.

For Business Teams:

\n
• AI-driven automation reduces manual effort by minimizing human intervention in data mapping, validation, and error resolution.
\n
• Faster data reconciliation with AI-powered validation that detects and corrects discrepancies, significantly reducing time spent on data consistency checks.
\n
• Lower migration costs through reduced manual work, fewer errors, and less rework.
\n

For IT Teams:

\n
• Faster migration through AI-driven automation of repetitive tasks.
\n
• Built-in compliance and anomaly detection using SAP BTP infrastructure.
\n
• Smarter cloud use via intelligent resource optimization.
\n

Bottom Line: AI That Works for You

Digital transformation shouldn’t be a burden — it should be a breakthrough. With agentic AI inside, DataLark shifts the focus from firefighting errors to strategic innovation.

If you’re still weighing your SAP S/4HANA migration options, let this post be your sign: Agentic AI isn’t the future. It’s happening now — with DataLark.

Learn how to leverage DataLark’s agentic AI features to simplify and accelerate your SAP S/4HANA data migration.

Learn how to leverage DataLark’s agentic AI features to simplify and accelerate your SAP S/4HANA data migration.

DataLark Leverages Agentic AI to Elevate S/4HANA Migration

DataLark has always supported a proactive approach to incorporating innovative technologies. For the second year in a row, we’re participating in the SAP Innovation Awards. Last year, we were honored to be selected as finalists — an achievement that inspired us to push the boundaries even further.

To accelerate digital transformation and simplify S/4HANA data migration, we introduced a new agentic AI approach in DataLark. Let’s take a closer look at how it works — and how it helps you reach your migration goals faster and with less effort.

What is Agentic AI?

Agentic AI is an artificial intelligence system that takes actions autonomously to achieve specific goals. Agentic AI systems are designed not only for processing information and making data-driven decisions, but also to act on those decisions, interact with the world, and adapt to its changing conditions.

Key capabilities of Agentic AI:

\n
• Makes decisions based on the environment and input data.
\n
• Learns from outcomes and adapts over time.
\n
• Operates with minimal human supervision.
\n
• Perform actions, ranging from GPS autopiloting (such as delivery robots) to executing tasks in a software environment.
\n

The most prominent examples of agentic AI include autonomous vehicles, software agents that handle end-to-end business processes, and AI in games or simulations where the AI character can interact with the environment and other entities to achieve the goals set.

Agentic AI vs. Generative AI

While both Agentic AI and Generative AI are subsets of artificial intelligence, they serve very different purposes. Generative AI creates content (like text or code) based on input, but it cannot act on its own. Agentic AI, on the other hand, is designed to operate autonomously, making decisions and executing actions with minimal human involvement. This makes it ideal for automating complex, goal-oriented workflows — such as SAP data migration.

The key differences between Agentic AI and Generative AI are summarized in the side-by-side comparison table below.

Why AI Matters in SAP S/4HANA Migration

SAP S/4HANA migration projects require significant time, cost, and resources. Migrating data from heterogeneous legacy systems demands extensive planning, skilled personnel, and significant manual effort to ensure data integrity, compliance, and system compatibility. The key challenges include accelerating data migration, reducing manual workload, and increasing data quality, all while ensuring a seamless transition to SAP S/4HANA with minimal downtime.

Aside from this, many companies lack the expertise to integrate AI-driven automation into ERP migrations, which slows down their digital transformation process and as a result, may leave them behind competitors. All this leads to expensive overprovisioning, business downtime risks, and compliance issues.

How DataLark Leverages Agentic AI

DataLark brings agentic AI to the forefront of SAP S/4HANA migration, transforming the process into a highly automated, intelligent workflow. Built on SAP BTP and integrated with SAP AI Core/AI Foundation, DataLark enables AI-powered orchestration, decision-making, and continuous optimization — reducing manual effort and speeding up every stage of the migration.

What makes it different:

\n
• End-to-end automation: From extraction and transformation to validation and loading, DataLark automates migration flows for both SAP and non-SAP systems.
\n
• Dynamic mapping & validation: AI continuously refines transformation rules and validates data in real time — minimizing rework and errors.
\n
• Built-in intelligence: Self-correcting workflows and anomaly detection reduce reliance on skilled personnel and ensure data quality.
\n
• Seamless SAP integration: The solution works with SAP Migration Cockpit and other standard tools, requiring no extensive custom development.
\n
• Faster, smoother go-lives: With intelligent task sequencing and reduced downtime, businesses transition to SAP S/4HANA with greater speed and confidence.
\n

Agentic AI in Action: Real-World Use Case

As businesses modernize with SAP S/4HANA, seamless data migration becomes a key success factor. While traditional migration approaches require significant manual effort, our customers and implementation teams sought a smarter, more automated way to accelerate the transition while ensuring data accuracy and integrity.

By combining agentic AI and SAP BTP, DataLark allows businesses to redefine their migration experience. DataLark, deployed on SAP BTP, enables AI-driven visual data flows, intelligent mapping, and automated validation, reducing complexity and human effort. SAP AI Core, integrated with external AI providers, enhances data quality, anomaly detection, and self-correcting workflows, ensuring a smooth, error-free migration.

What’s powering the transformation:

\n
• SAP AI Core processes structured and unstructured data, dynamically refining transformation rules with OpenAI-powered (other providers can also be used) processing.
\n
• AI Agents handle automated validation and error resolution, detecting anomalies, self-correcting inconsistencies, and reducing rework.
\n
• Intelligent Workflow Optimization sequences migration steps dynamically, minimizing downtime and ensuring seamless cutovers.
\n

This way, DataLark ensures seamless connectivity between legacy systems and SAP S/4HANA, enabling our customers to achieve faster, more efficient, and cost-effective migrations, and setting a new benchmark for AI-powered SAP transformations.

DataLark’s Agentic AI Benefits

Here are the key business and IT benefits of using agentic AI in DataLark’s data migration workflows.

For Business Teams:

\n
• AI-driven automation reduces manual effort by minimizing human intervention in data mapping, validation, and error resolution.
\n
• Faster data reconciliation with AI-powered validation that detects and corrects discrepancies, significantly reducing time spent on data consistency checks.
\n
• Lower migration costs through reduced manual work, fewer errors, and less rework.
\n

For IT Teams:

\n
• Faster migration through AI-driven automation of repetitive tasks.
\n
• Built-in compliance and anomaly detection using SAP BTP infrastructure.
\n
• Smarter cloud use via intelligent resource optimization.
\n

Bottom Line: AI That Works for You

Digital transformation shouldn’t be a burden — it should be a breakthrough. With agentic AI inside, DataLark shifts the focus from firefighting errors to strategic innovation.

If you’re still weighing your SAP S/4HANA migration options, let this post be your sign: Agentic AI isn’t the future. It’s happening now — with DataLark.

Learn how to leverage DataLark’s agentic AI features to simplify and accelerate your SAP S/4HANA data migration.

DataLark Leverages Agentic AI to Elevate S/4HANA Migration

DataLark has always supported a proactive approach to incorporating innovative technologies. For the second year in a row, we’re participating in the SAP Innovation Awards. Last year, we were honored to be selected as finalists — an achievement that inspired us to push the boundaries even further.

To accelerate digital transformation and simplify S/4HANA data migration, we introduced a new agentic AI approach in DataLark. Let’s take a closer look at how it works — and how it helps you reach your migration goals faster and with less effort.

What is Agentic AI?

Agentic AI is an artificial intelligence system that takes actions autonomously to achieve specific goals. Agentic AI systems are designed not only for processing information and making data-driven decisions, but also to act on those decisions, interact with the world, and adapt to its changing conditions.

Key capabilities of Agentic AI:

\n
• Makes decisions based on the environment and input data.
\n
• Learns from outcomes and adapts over time.
\n
• Operates with minimal human supervision.
\n
• Perform actions, ranging from GPS autopiloting (such as delivery robots) to executing tasks in a software environment.
\n

The most prominent examples of agentic AI include autonomous vehicles, software agents that handle end-to-end business processes, and AI in games or simulations where the AI character can interact with the environment and other entities to achieve the goals set.

Agentic AI vs. Generative AI

While both Agentic AI and Generative AI are subsets of artificial intelligence, they serve very different purposes. Generative AI creates content (like text or code) based on input, but it cannot act on its own. Agentic AI, on the other hand, is designed to operate autonomously, making decisions and executing actions with minimal human involvement. This makes it ideal for automating complex, goal-oriented workflows — such as SAP data migration.

The key differences between Agentic AI and Generative AI are summarized in the side-by-side comparison table below.

Why AI Matters in SAP S/4HANA Migration

SAP S/4HANA migration projects require significant time, cost, and resources. Migrating data from heterogeneous legacy systems demands extensive planning, skilled personnel, and significant manual effort to ensure data integrity, compliance, and system compatibility. The key challenges include accelerating data migration, reducing manual workload, and increasing data quality, all while ensuring a seamless transition to SAP S/4HANA with minimal downtime.

Aside from this, many companies lack the expertise to integrate AI-driven automation into ERP migrations, which slows down their digital transformation process and as a result, may leave them behind competitors. All this leads to expensive overprovisioning, business downtime risks, and compliance issues.

How DataLark Leverages Agentic AI

DataLark brings agentic AI to the forefront of SAP S/4HANA migration, transforming the process into a highly automated, intelligent workflow. Built on SAP BTP and integrated with SAP AI Core/AI Foundation, DataLark enables AI-powered orchestration, decision-making, and continuous optimization — reducing manual effort and speeding up every stage of the migration.

What makes it different:

\n
• End-to-end automation: From extraction and transformation to validation and loading, DataLark automates migration flows for both SAP and non-SAP systems.
\n
• Dynamic mapping & validation: AI continuously refines transformation rules and validates data in real time — minimizing rework and errors.
\n
• Built-in intelligence: Self-correcting workflows and anomaly detection reduce reliance on skilled personnel and ensure data quality.
\n
• Seamless SAP integration: The solution works with SAP Migration Cockpit and other standard tools, requiring no extensive custom development.
\n
• Faster, smoother go-lives: With intelligent task sequencing and reduced downtime, businesses transition to SAP S/4HANA with greater speed and confidence.
\n

Agentic AI in Action: Real-World Use Case

As businesses modernize with SAP S/4HANA, seamless data migration becomes a key success factor. While traditional migration approaches require significant manual effort, our customers and implementation teams sought a smarter, more automated way to accelerate the transition while ensuring data accuracy and integrity.

By combining agentic AI and SAP BTP, DataLark allows businesses to redefine their migration experience. DataLark, deployed on SAP BTP, enables AI-driven visual data flows, intelligent mapping, and automated validation, reducing complexity and human effort. SAP AI Core, integrated with external AI providers, enhances data quality, anomaly detection, and self-correcting workflows, ensuring a smooth, error-free migration.

What’s powering the transformation:

\n
• SAP AI Core processes structured and unstructured data, dynamically refining transformation rules with OpenAI-powered (other providers can also be used) processing.
\n
• AI Agents handle automated validation and error resolution, detecting anomalies, self-correcting inconsistencies, and reducing rework.
\n
• Intelligent Workflow Optimization sequences migration steps dynamically, minimizing downtime and ensuring seamless cutovers.
\n

This way, DataLark ensures seamless connectivity between legacy systems and SAP S/4HANA, enabling our customers to achieve faster, more efficient, and cost-effective migrations, and setting a new benchmark for AI-powered SAP transformations.

DataLark’s Agentic AI Benefits

Here are the key business and IT benefits of using agentic AI in DataLark’s data migration workflows.

For Business Teams:

\n
• AI-driven automation reduces manual effort by minimizing human intervention in data mapping, validation, and error resolution.
\n
• Faster data reconciliation with AI-powered validation that detects and corrects discrepancies, significantly reducing time spent on data consistency checks.
\n
• Lower migration costs through reduced manual work, fewer errors, and less rework.
\n

For IT Teams:

\n
• Faster migration through AI-driven automation of repetitive tasks.
\n
• Built-in compliance and anomaly detection using SAP BTP infrastructure.
\n
• Smarter cloud use via intelligent resource optimization.
\n

Bottom Line: AI That Works for You

Digital transformation shouldn’t be a burden — it should be a breakthrough. With agentic AI inside, DataLark shifts the focus from firefighting errors to strategic innovation.

If you’re still weighing your SAP S/4HANA migration options, let this post be your sign: Agentic AI isn’t the future. It’s happening now — with DataLark.

Learn how to leverage DataLark’s agentic AI features to simplify and accelerate your SAP S/4HANA data migration.

Learn how to leverage DataLark’s agentic AI features to simplify and accelerate your SAP S/4HANA data migration.

Learn how to leverage DataLark’s agentic AI features to simplify and accelerate your SAP S/4HANA data migration.

DataLark Leverages Agentic AI to Elevate S/4HANA Migration

DataLark has always supported a proactive approach to incorporating innovative technologies. For the second year in a row, we’re participating in the SAP Innovation Awards. Last year, we were honored to be selected as finalists — an achievement that inspired us to push the boundaries even further.

To accelerate digital transformation and simplify S/4HANA data migration, we introduced a new agentic AI approach in DataLark. Let’s take a closer look at how it works — and how it helps you reach your migration goals faster and with less effort.

What is Agentic AI?

Agentic AI is an artificial intelligence system that takes actions autonomously to achieve specific goals. Agentic AI systems are designed not only for processing information and making data-driven decisions, but also to act on those decisions, interact with the world, and adapt to its changing conditions.

Key capabilities of Agentic AI:

\n
• Makes decisions based on the environment and input data.
\n
• Learns from outcomes and adapts over time.
\n
• Operates with minimal human supervision.
\n
• Perform actions, ranging from GPS autopiloting (such as delivery robots) to executing tasks in a software environment.
\n

The most prominent examples of agentic AI include autonomous vehicles, software agents that handle end-to-end business processes, and AI in games or simulations where the AI character can interact with the environment and other entities to achieve the goals set.

Agentic AI vs. Generative AI

While both Agentic AI and Generative AI are subsets of artificial intelligence, they serve very different purposes. Generative AI creates content (like text or code) based on input, but it cannot act on its own. Agentic AI, on the other hand, is designed to operate autonomously, making decisions and executing actions with minimal human involvement. This makes it ideal for automating complex, goal-oriented workflows — such as SAP data migration.

The key differences between Agentic AI and Generative AI are summarized in the side-by-side comparison table below.

Why AI Matters in SAP S/4HANA Migration

SAP S/4HANA migration projects require significant time, cost, and resources. Migrating data from heterogeneous legacy systems demands extensive planning, skilled personnel, and significant manual effort to ensure data integrity, compliance, and system compatibility. The key challenges include accelerating data migration, reducing manual workload, and increasing data quality, all while ensuring a seamless transition to SAP S/4HANA with minimal downtime.

Aside from this, many companies lack the expertise to integrate AI-driven automation into ERP migrations, which slows down their digital transformation process and as a result, may leave them behind competitors. All this leads to expensive overprovisioning, business downtime risks, and compliance issues.

How DataLark Leverages Agentic AI

DataLark brings agentic AI to the forefront of SAP S/4HANA migration, transforming the process into a highly automated, intelligent workflow. Built on SAP BTP and integrated with SAP AI Core/AI Foundation, DataLark enables AI-powered orchestration, decision-making, and continuous optimization — reducing manual effort and speeding up every stage of the migration.

What makes it different:

\n
• End-to-end automation: From extraction and transformation to validation and loading, DataLark automates migration flows for both SAP and non-SAP systems.
\n
• Dynamic mapping & validation: AI continuously refines transformation rules and validates data in real time — minimizing rework and errors.
\n
• Built-in intelligence: Self-correcting workflows and anomaly detection reduce reliance on skilled personnel and ensure data quality.
\n
• Seamless SAP integration: The solution works with SAP Migration Cockpit and other standard tools, requiring no extensive custom development.
\n
• Faster, smoother go-lives: With intelligent task sequencing and reduced downtime, businesses transition to SAP S/4HANA with greater speed and confidence.
\n

Agentic AI in Action: Real-World Use Case

As businesses modernize with SAP S/4HANA, seamless data migration becomes a key success factor. While traditional migration approaches require significant manual effort, our customers and implementation teams sought a smarter, more automated way to accelerate the transition while ensuring data accuracy and integrity.

By combining agentic AI and SAP BTP, DataLark allows businesses to redefine their migration experience. DataLark, deployed on SAP BTP, enables AI-driven visual data flows, intelligent mapping, and automated validation, reducing complexity and human effort. SAP AI Core, integrated with external AI providers, enhances data quality, anomaly detection, and self-correcting workflows, ensuring a smooth, error-free migration.

What’s powering the transformation:

\n
• SAP AI Core processes structured and unstructured data, dynamically refining transformation rules with OpenAI-powered (other providers can also be used) processing.
\n
• AI Agents handle automated validation and error resolution, detecting anomalies, self-correcting inconsistencies, and reducing rework.
\n
• Intelligent Workflow Optimization sequences migration steps dynamically, minimizing downtime and ensuring seamless cutovers.
\n

This way, DataLark ensures seamless connectivity between legacy systems and SAP S/4HANA, enabling our customers to achieve faster, more efficient, and cost-effective migrations, and setting a new benchmark for AI-powered SAP transformations.

DataLark’s Agentic AI Benefits

Here are the key business and IT benefits of using agentic AI in DataLark’s data migration workflows.

For Business Teams:

\n
• AI-driven automation reduces manual effort by minimizing human intervention in data mapping, validation, and error resolution.
\n
• Faster data reconciliation with AI-powered validation that detects and corrects discrepancies, significantly reducing time spent on data consistency checks.
\n
• Lower migration costs through reduced manual work, fewer errors, and less rework.
\n

For IT Teams:

\n
• Faster migration through AI-driven automation of repetitive tasks.
\n
• Built-in compliance and anomaly detection using SAP BTP infrastructure.
\n
• Smarter cloud use via intelligent resource optimization.
\n

Bottom Line: AI That Works for You

Digital transformation shouldn’t be a burden — it should be a breakthrough. With agentic AI inside, DataLark shifts the focus from firefighting errors to strategic innovation.

If you’re still weighing your SAP S/4HANA migration options, let this post be your sign: Agentic AI isn’t the future. It’s happening now — with DataLark.

Learn how to leverage DataLark’s agentic AI features to simplify and accelerate your SAP S/4HANA data migration.

Learn how to leverage DataLark’s agentic AI features to simplify and accelerate your SAP S/4HANA data migration.

Learn how to leverage DataLark’s agentic AI features to simplify and accelerate your SAP S/4HANA data migration.

DataLark Leverages Agentic AI to Elevate S/4HANA Migration

DataLark has always supported a proactive approach to incorporating innovative technologies. For the second year in a row, we’re participating in the SAP Innovation Awards. Last year, we were honored to be selected as finalists — an achievement that inspired us to push the boundaries even further.

To accelerate digital transformation and simplify S/4HANA data migration, we introduced a new agentic AI approach in DataLark. Let’s take a closer look at how it works — and how it helps you reach your migration goals faster and with less effort.

What is Agentic AI?

Agentic AI is an artificial intelligence system that takes actions autonomously to achieve specific goals. Agentic AI systems are designed not only for processing information and making data-driven decisions, but also to act on those decisions, interact with the world, and adapt to its changing conditions.

Key capabilities of Agentic AI:

\n
• Makes decisions based on the environment and input data.
\n
• Learns from outcomes and adapts over time.
\n
• Operates with minimal human supervision.
\n
• Perform actions, ranging from GPS autopiloting (such as delivery robots) to executing tasks in a software environment.
\n

The most prominent examples of agentic AI include autonomous vehicles, software agents that handle end-to-end business processes, and AI in games or simulations where the AI character can interact with the environment and other entities to achieve the goals set.

Agentic AI vs. Generative AI

While both Agentic AI and Generative AI are subsets of artificial intelligence, they serve very different purposes. Generative AI creates content (like text or code) based on input, but it cannot act on its own. Agentic AI, on the other hand, is designed to operate autonomously, making decisions and executing actions with minimal human involvement. This makes it ideal for automating complex, goal-oriented workflows — such as SAP data migration.

The key differences between Agentic AI and Generative AI are summarized in the side-by-side comparison table below.

Why AI Matters in SAP S/4HANA Migration

SAP S/4HANA migration projects require significant time, cost, and resources. Migrating data from heterogeneous legacy systems demands extensive planning, skilled personnel, and significant manual effort to ensure data integrity, compliance, and system compatibility. The key challenges include accelerating data migration, reducing manual workload, and increasing data quality, all while ensuring a seamless transition to SAP S/4HANA with minimal downtime.

Aside from this, many companies lack the expertise to integrate AI-driven automation into ERP migrations, which slows down their digital transformation process and as a result, may leave them behind competitors. All this leads to expensive overprovisioning, business downtime risks, and compliance issues.

How DataLark Leverages Agentic AI

DataLark brings agentic AI to the forefront of SAP S/4HANA migration, transforming the process into a highly automated, intelligent workflow. Built on SAP BTP and integrated with SAP AI Core/AI Foundation, DataLark enables AI-powered orchestration, decision-making, and continuous optimization — reducing manual effort and speeding up every stage of the migration.

What makes it different:

\n
• End-to-end automation: From extraction and transformation to validation and loading, DataLark automates migration flows for both SAP and non-SAP systems.
\n
• Dynamic mapping & validation: AI continuously refines transformation rules and validates data in real time — minimizing rework and errors.
\n
• Built-in intelligence: Self-correcting workflows and anomaly detection reduce reliance on skilled personnel and ensure data quality.
\n
• Seamless SAP integration: The solution works with SAP Migration Cockpit and other standard tools, requiring no extensive custom development.
\n
• Faster, smoother go-lives: With intelligent task sequencing and reduced downtime, businesses transition to SAP S/4HANA with greater speed and confidence.
\n

Agentic AI in Action: Real-World Use Case

As businesses modernize with SAP S/4HANA, seamless data migration becomes a key success factor. While traditional migration approaches require significant manual effort, our customers and implementation teams sought a smarter, more automated way to accelerate the transition while ensuring data accuracy and integrity.

By combining agentic AI and SAP BTP, DataLark allows businesses to redefine their migration experience. DataLark, deployed on SAP BTP, enables AI-driven visual data flows, intelligent mapping, and automated validation, reducing complexity and human effort. SAP AI Core, integrated with external AI providers, enhances data quality, anomaly detection, and self-correcting workflows, ensuring a smooth, error-free migration.

What’s powering the transformation:

\n
• SAP AI Core processes structured and unstructured data, dynamically refining transformation rules with OpenAI-powered (other providers can also be used) processing.
\n
• AI Agents handle automated validation and error resolution, detecting anomalies, self-correcting inconsistencies, and reducing rework.
\n
• Intelligent Workflow Optimization sequences migration steps dynamically, minimizing downtime and ensuring seamless cutovers.
\n

This way, DataLark ensures seamless connectivity between legacy systems and SAP S/4HANA, enabling our customers to achieve faster, more efficient, and cost-effective migrations, and setting a new benchmark for AI-powered SAP transformations.

DataLark’s Agentic AI Benefits

Here are the key business and IT benefits of using agentic AI in DataLark’s data migration workflows.

For Business Teams:

\n
• AI-driven automation reduces manual effort by minimizing human intervention in data mapping, validation, and error resolution.
\n
• Faster data reconciliation with AI-powered validation that detects and corrects discrepancies, significantly reducing time spent on data consistency checks.
\n
• Lower migration costs through reduced manual work, fewer errors, and less rework.
\n

For IT Teams:

\n
• Faster migration through AI-driven automation of repetitive tasks.
\n
• Built-in compliance and anomaly detection using SAP BTP infrastructure.
\n
• Smarter cloud use via intelligent resource optimization.
\n

Bottom Line: AI That Works for You

Digital transformation shouldn’t be a burden — it should be a breakthrough. With agentic AI inside, DataLark shifts the focus from firefighting errors to strategic innovation.

If you’re still weighing your SAP S/4HANA migration options, let this post be your sign: Agentic AI isn’t the future. It’s happening now — with DataLark.

Learn how to leverage DataLark’s agentic AI features to simplify and accelerate your SAP S/4HANA data migration.