Simplify SAP Data Cleansing with DataLark
Ensure accurate, complete, and consistent SAP data by eliminating duplicates, correcting errors, and standardizing records.
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How It Works
- 1. Connect Systems
- 2. Perform Data Assessment
- 3. Cleanse and Standardize Data
- 4. Enrich Incomplete Records
- 5. Review Reports
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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.
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FAQ
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How does DataLark help with SAP Data Cleansing?DataLark delivers an integrated solution for analyzing, deduplicating, standardizing, and validating SAP data. With seamless SAP connectivity, it ensures your data remains consistent, complete, and ready for use.
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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.
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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.
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What types of data can be cleansed?You can validate and cleanse master data, transactional data, financial records, HR data, procurement entries, and more. It ensures your data meets predefined business rules, structural requirements, and quality standards.
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Can DataLark prevent future data quality issues?Yes. DataLark supports ongoing monitoring, automated validation, and proactive data governance to prevent data issues before they occur.
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Does data cleansing impact SAP system performance?No. DataLark is designed to work efficiently within SAP environments without causing system downtime or performance degradation.
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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.
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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.