SAP Data Quality
The Importance of Quality Data in SAP and Key Considerations
Quality data becomes increasingly important as cross-departmental collaboration and remote work increases. Having a solution that can provide a single source of truth – and sort through data silos, multiple versions of data, and inaccuracies from manual entries and erroneous data – is a key component to achieving quality data.
Several SAP solutions support quality data for different business functions. These include SAP S/4HANA Cloud for group reporting, SAP’s strategic consolidation solution; SAP Master Data Governance, which provides data quality management capabilities; SAP Data Quality Management, which enables detection and correction of master data errors and inconsistencies; and others, including SAP HANA smart data quality, SAP Data Services, SAP Information Steward, and SAP Agile Data Preparation.
Several SAP vendors offer solutions to help SAP customers improve data quality. For example, Prospecta offers Data Intelligence Workbench to help organizations profile, analyze, govern, and monitor data assets. And Qlik provides test data management solutions that help organizations maintain quality test data.
The Importance of Quality Data in SAP and Key Considerations
Quality data becomes increasingly important as cross-departmental collaboration and remote work increases. Having a solution that can provide a single source of truth – and sort through data silos, multiple versions of data, and inaccuracies from manual entries and erroneous data – is a key component to achieving quality data.
Several SAP solutions support quality data for different business functions. These include SAP S/4HANA Cloud for group reporting, SAP’s strategic consolidation solution; SAP Master Data Governance, which provides data quality management capabilities; SAP Data Quality Management, which enables detection and correction of master data errors and inconsistencies; and others, including SAP HANA smart data quality, SAP Data Services, SAP Information Steward, and SAP Agile Data Preparation.
Several SAP vendors offer solutions to help SAP customers improve data quality. For example, Prospecta offers Data Intelligence Workbench to help organizations profile, analyze, govern, and monitor data assets. And Qlik provides test data management solutions that help organizations maintain quality test data.
Key Quality Data Considerations for SAPinsiders
Develop data-correction processes to prevent data errors. KMD, an NEC company and the largest IT firm in Denmark, was able to reduce its closing time from eight days to four days while also improving its data quality. During its implementation of SAP S/4HANA Cloud for group reporting, the team encountered a pain point around data quality from its source system. The challenge interfered with the company’s cash-flow automation. To address the challenge, KMD developed data-correction processes for local controllers that help avoid propagating data errors upstream to Group Finance. Read more about how KMD used testing and integration to confirm data quality.
Use SAP Integration Suite for enhanced data quality when connecting various ERP systems to the cloud. Lloyd’s Register is one of the world’s leading providers of professional services for engineering and technology that improves the safety and performance of critical infrastructure worldwide. It is building a software-as-a-service (SaaS) technology engine on Microsoft Azure and SAP Cloud — SAP’s portfolio of cloud solutions — to support services heavily utilized by its customers. To achieve enhanced data quality and real-time information, Lloyd’s connected its cloud solution to various ERP systems using SAP Integration Suite.
Improve supply chain operations with quality data. Although quality data affects all lines of business, it is particularly impactful for the supply chain. Ranjan Bakshi, CEO of Prospecta, says that automation, collaboration, and effective communication to suppliers are important for achieving digital transformation. And all these requirements rely on quality data. “You don’t need a business case for a data quality solution. If you have billions invested in transformation and other areas, you just need quality data. It becomes a prerequisite, not a business case by itself,” Bakshi says.
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Get Data From Any Table in Another SAP System: A Generic Way in ABAP
Learn a new way to load data from any table in any SAP system to any other SAP system. See how to use a Remote Function Call (RFC) function module call, Runtime Type Service (RTTS), and JavaScript Object Notation (JSON) serialization/deserialization. Key Concept A Remote Function Call (RFC)-enabled function module is a function module that...…
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SAP Data Services Part 3: How to Extract, Transform, and Load Data into SAP HANA
Examine and compare the classic SAP Data Services approach for extracting, transforming, and loading (ETL) processes to the functionality that is native to SAP HANA to determine optimal integration. Key Concept Certain extract, transform, and load (ETL) processes have been pushed to SAP HANA, which directly affects the integration with SAP Data Services. ETL is...…
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Define Test Requirements and Create and Execute Testing Components as Part of Your Automated Testing Solution
ManagerLearn a step-by-step procedure for defining and transferring test requirements from SAP Solution Manager to SAP Quality Center by HP. Create test components in SAP Test Acceleration and Optimization and execute the test cases in SAP Quality Center. Key Concept The business process testing model supports the workflow of the user interface design that allows...…
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Exploit SAP Quality Management to Handle Subcontracting Activities
Learn about the main functionalities relevant for subcontracting activities during the production process in the case of discrete manufacturing, using the materials management (MM), Production Planning (PP), Quality Management (QM), and Financial Accounting (FI) modules. Follow details about the main customizing settings to be done in the system from a financial point of view. Key...…
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Role of Data Quality in Supply Chain Digital Transformation
Quality Data Lays the Foundation As supply chains become increasingly complex, so does the associated data landscape. Millions of data points must be managed in modern supply chains today due to the wide gamut of systems used. From Enterprise resource planning (ERP) systems that manage an organization’s basic processes to customer relationship process (CRM) systems...…
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SAP Data Services: How to Extract, Transform, and Load Data into SAP HANA
Examine and compare the classic SAP Data Services approach for extracting, transforming, and loading (ETL) processes to the functionality that is native to SAP HANA to determine optimal integration.
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Secrets to Successful Data Conversions
Converting data from a legacy system to an SAP system can be a daunting task. Follow experienced advice for planning and executing your data conversion strategy, from developing project scope to testing and monitoring the data conversion. Included with these tips are two downloads: a sample Microsoft Visio data dependency planning chart and a conversion...…
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How to Create Outgoing Quality Certificates
The option to print the results of a quality inspection of a material optimizes the business processes of a company by integrating supply chain activities. Learn how to set up the quality management (QM) master data needed to create an outgoing quality certificate. Key Concept An outgoing quality certificate is a company’s official document to...…
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Accurate and Affordable Data Cleansing in the Cloud
High quality and trusted data is the differentiation between a company that is surviving in the digital age and one that is thriving. To help customers enhance data management within their own companies, SAP has brought it’s on-premise data quality expertise to the cloud. Hear how SAP Data Quality Management, microservices for location data, helps…
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Selective Data Transition vs. Full Migration: Choosing the Right Approach for SAP S/4HANA
Migrating from SAP ECC to SAP S/4HANA involves choosing between Selective Data Transition, which allows for flexible and tailored data migration, and Full Migration, which fully integrates existing systems but requires significant reconfiguration, with specific strategies and thorough preparations critical for successful execution.
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