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    Optimize Your SAP Data Profiling with DataLark

    Uncover data quality issues hiding in your environment — and take action before they affect compliance, reporting, or business decisions.

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    5

    How It Works

    1

    Connect Systems
    Use DataLark’s built-in connectors to integrate with SAP (ECC, S/4HANA, and more) and non-SAP sources across your enterprise landscape.

    2

    Define Profiling Logic
    Choose from prebuilt or custom rules to analyze your data structure, content, relationships, and anomalies — including cross-column and cross-table checks. Rules can be written in plain text format for full control and reusability.

    3

    Run Automated Profiling
    Schedule or trigger profiling jobs to continuously assess data quality. DataLark executes checks in the background and flags critical issues such as missing values, constant fields, or unusually high cardinality.

    4

    Review Visual Reports
    Get instant visibility with clear, PDF- or HTML-based reports highlighting data completeness, correlations, unique values, and warnings. You’ll receive the report by email as soon as your profiling run finishes in DataLark.

    5

    Address Inconsistencies
    Identify data quality issues early on and flag them for cleansing or enrichment. DataLark’s profiling results can be shared with business teams for review and validation — ensuring issues are addressed before they impact your day-to-day operations.

    1

    Connect Systems
    Use DataLark’s built-in connectors to integrate with SAP (ECC, S/4HANA, and more) and non-SAP sources across your enterprise landscape.

    2

    Define Profiling Logic
    Choose from prebuilt or custom rules to analyze your data structure, content, relationships, and anomalies — including cross-column and cross-table checks. Rules can be written in plain text format for full control and reusability.

    3

    Run Automated Profiling
    Schedule or trigger profiling jobs to continuously assess data quality. DataLark executes checks in the background and flags critical issues such as missing values, constant fields, or unusually high cardinality.

    4

    Review Visual Reports
    Get instant visibility with clear, PDF- or HTML-based reports highlighting data completeness, correlations, unique values, and warnings. You’ll receive the report by email as soon as your profiling run finishes in DataLark.

    5

    Address Inconsistencies
    Identify data quality issues early on and flag them for cleansing or enrichment. DataLark’s profiling results can be shared with business teams for review and validation — ensuring issues are addressed before they impact your day-to-day operations.

    We've Earned the Trust of Global Enterprises

    Level Up Your SAP Data Validation with DataLark

    Request a Demo
    Benefits of Streamlining SAP Data Profiling with DataLark
    End-to-End Data Quality Solution
    Leverage DataLark to enhance the entire data quality management process, from inconsistencies detection to automated data cleansing and enrichment.
    Business-Specific Profiling Rules
    Define proprietary profiling rules in plain text to match your unique data structures, compliance needs, and business logic.
    Seamless Integration
    Effortlessly connect with both SAP and non-SAP systems for unified data management.
    Powered by Python Profiling Framework
    Built on top of trusted, open-source libraries optimized for large datasets, DataLark delivers fast, reliable profiling without compromising flexibility — all accessible through a no-code interface.
    Customizable & Shareable Reports
    Generate clear, PDF-based reports that provide the critical insights required for governance, compliance, and strategy — and receive them by email as soon as profiling is complete.

    Request a Demo

    Get a personal walkthrough from one of our product specialists to explore DataLark’s key features:
    Built-in Source and Target Connectors
    Robust Data Transformations
    Zero-coding
    Flexible Deployment: On-prem, Cloud, and Hybrid
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    Problem

    The client faced challenges in maintaining high-quality master data across their SAP landscape. The complexity of their operations, involving numerous SAP objects such as materials, BOMs, and others, led to:

    • Data inconsistencies across various SAP modules, affecting reporting and decision-making.
    • Manual validation efforts, requiring extensive time and resources to ensure compliance with business rules.
    • Error propagation due to lack of proactive data quality checks, impacting downstream processes like procurement, production planning, and finance.
    • Difficulty in maintaining data integrity as multiple systems contributed to data creation and updates.

    These challenges resulted in inefficiencies, operational risks, and increased costs associated with poor data quality.

     

     

    Solution

    To address these issues, the client leveraged DataLark as a platform for data quality management. The solution introduced:

    • Automated Data Extraction: Seamlessly reading and analyzing master data from SAP, covering materials, BOMs, and other key objects.
    • Business Rule Validation: Configurable rule sets to automatically detect data inconsistencies, missing attributes, duplicate records, and misaligned relationships.
    • Deviation Reporting: Reports highlighting data quality issues, allowing stakeholders to take corrective action.
    • Automated Adjustments: For predefined scenarios, the system corrected data inconsistencies automatically, reducing manual intervention.
    • Continuous Monitoring: Ongoing validation to ensure data integrity as new records were created or modified.

    This approach enabled the client to establish a proactive data governance framework, ensuring SAP master data met business and regulatory requirements.

    Results

    With DataLark, the client established a scalable and efficient data quality management process, ensuring that SAP master data remained accurate, reliable, and aligned with business objectives.

    65%

    Reduction in Manual Reconciliation Efforts: Automated validation streamlined data comparison, significantly reducing the need for manual intervention.

    50%

    Improvement in Data Consistency: Intelligent rule-based validation minimized discrepancies and enhanced data reliability.

    40%

    Faster Issue Resolution: Automated deviation reports and alerts enabled quicker identification and correction of data mismatches.

    Problem

    The client had issues with ensuring data consistency between multiple systems, including SAP and external databases. Some of the challenges were:

    • Complex data validation requirements, involving multilevel comparisons across different data sources.
    • Manual reconciliation efforts, which were time-consuming and prone to errors.
    • Lack of a standardized framework for defining and managing data validation rules, leading to inconsistencies.
    • Dependence on IT for developing alternative solutions making it difficult for data management teams to adapt validation logic to evolving needs.

    These issues resulted in delays in data processing, inaccurate reporting, and inefficiencies in operations that relied on synchronized master data.

     

     

    Solution

    To overcome these challenges, the client implemented DataLark as a data validation platform. Key features included:

    • Automated Data Extraction: Seamlessly reading data from multiple sources, including SAP and external databases.
    • Flexible Data Mapping: Providing an intuitive interface for business users to define relationships between datasets without technical expertise.
    • Configurable Multilevel Validation Rules: Enabling comparison logic across different hierarchies, including product structures, pricing conditions, and supplier information.
    • User-Friendly Rule Management: Allowing master data teams to create, modify, and manage validation rules without IT involvement.
    • Deviation Reports and Alerts: Automatically identifying discrepancies and presenting them in easily understandable reports.

    With DataLark, the client eliminated the need for custom development, enabling business users to maintain and adapt validation rules independently.

    Results

    With DataLark, the client established a scalable, efficient, and user-friendly approach to data validation, ensuring data accuracy across critical business processes.

    70%

    Reduction in Manual Reconciliation Efforts: Automated validation replaced labor-intensive data comparison, freeing up resources for higher-value tasks.

    90%

    Improvement in Data Accuracy: Faster identification and resolution of discrepancies ensured more reliable and consistent data.

    50%

    Increase in Business Agility: Master data teams could modify validation rules independently, reducing reliance on IT and accelerating response times.

    Explore Opportunities for Data Profiling Enhancement

    Talk to Our Experts

    FAQ

    • How does DataLark integrate with SAP?
      DataLark connects to SAP systems — including ECC, S/4HANA, and more — using standard communication methods such as BAPI, RFC, and OData. It supports both read and write operations, providing secure, near real-time access to your master and transactional data without custom development.
    • Does DataLark support non-SAP systems?
      Yes! In addition to SAP ECC and S/4HANA, DataLark supports a wide range of non-SAP systems — including databases, cloud platforms, enterprise applications, flat files, machine data, and message queues.
    • What is a Python profiling framework, and how does DataLark use it?
      DataLark includes integration with YData Profiling (formerly Pandas Profiling), allowing users to run advanced profiling checks — such as detecting missing values, duplicates, and structural issues — through a fast, visual, and code-free experience.
    • How does DataLark enhance data quality in SAP environments?
      DataLark helps maintain high-quality data across SAP landscapes by identifying inconsistencies, missing values, and structural issues early on. Whether you're preparing for S/4HANA migration or improving day-to-day operations, our profiling, cleansing, and validation tools ensure better decision-making and smoother business processes.
    • Is technical expertise required to set up data profiling with DataLark?
      Not at all. DataLark’s no-code, drag-and-drop interface is built for users of all technical levels — from business users to IT teams.
    • What deployment options are available?
      DataLark offers flexible deployment options — on-premise (laptops, VDIs, Windows servers), in the cloud (SAP BTP, AWS, Microsoft Azure), or hybrid — to meet your organization’s technical and security requirements.
    • How long does it take to implement DataLark?
      Implementation time primarily depends on your landscape and internal onboarding policies for new applications. Most teams are up and running with DataLark within a few days to a few weeks — including infrastructure setup, SAP prerequisites (such as creating a dedicated user role for external access or importing transports, if required), installation, and guided onboarding.