Exploring How to Make Data Core Compliant

Objective

After completing this lesson, you will be able to Control data to the latest standards.

Clean Data

Control Data According to Latest Standards

Having explored business processes and extensions we now turn our attention to data. The mantra here is simple: "Keep the data clean". In the digital age, information is the most powerful tool at our disposal. With the surge in Big Data analytics, businesses are increasingly seeking ways to optimize, streamline, and refine their data. In this regard, SAP S/4HANA Cloud provides a robust platform to manage, process, and apply data more effectively. However, data efficiency is only as good as the core principles governing its cleanliness. In this third lesson of the unit, we turn our attention to how to make sure data is clean core compliant.

Defining "Data"

Before delving into the specifics of clean data, it's important to make sure we have a clear definition of "data" in the context of SAP S/4HANA Cloud. An SAP S/4HANA Cloud system stores three types of data:

  • Configuration
  • Master
  • Transactional

Configuration data defines the organization's structure. Some examples are: company code, plants, or purchase organizations. Master data is a consistent and uniform set of identifiers and extended attributes that describe the core entities of the enterprise. Examples of master data that almost all organizations work with are customers, vendors, products, and general ledger accounts. Transactional data is information directly derived as a result of transactions. Examples sales orders, purchase orders, or hiring. This data always:

  • has a time dimension, such as the date of the sales order.
  • has a numerical value, such as the sales order amount
  • refers to one or more (master data) objects, such as the customer who the sales order is for

Main Aspects

Watch the video to get an overview of the dimension — data.

Mantra: "Keep the data clean".

Data Quality

In today's data-driven landscape, the principles of data quality have evolved to prioritize six key facets:

  • Accuracy
  • Completeness
  • Consistency
  • Timeliness
  • Validity
  • Uniqueness

Accuracy

Accuracy is a fundamental requirement for clean core compliance, as it ensures that the data accurately reflects the reality of the business. Inaccurate data can lead to incorrect decision-making, inefficient processes, and a loss of trust in the system. To maintain accuracy, it's essential to implement data validation rules, perform regular data quality checks, and establish data governance processes to address any issues that may arise.

Completeness

Completeness is equally important, as missing or incomplete data can hinder business operations and decision-making. To ensure completeness, it's essential to define data requirements and ensure that all relevant data is captured and maintained. This may involve establishing data collection processes, implementing data entry validation, and enforcing data entry standards to prevent incomplete data from entering the system.

Consistency

Consistency is crucial for clean core compliance, as inconsistent data can lead to confusion, errors, and inefficiencies. Establishing data standards and guidelines, conducting data profiling and cleansing activities, and implementing data integration and reconciliation processes are essential for maintaining data consistency.

Timeliness

Timeliness is another critical aspect of modern data quality requirements, as outdated or stale data can negatively impact decision-making and business operations. Implementing real-time data capture and integration processes, defining data refresh and update schedules, and setting up data monitoring and alerting mechanisms are essential for ensuring timely and up-to-date data.

Validity

Validity ensures that the data conforms to defined standards and business rules, and that it accurately represents the real-world scenario it purports to capture. Invalid data can mislead and obscure insights.

Uniqueness

Uniqueness ensures that duplicate data is identified and eliminated offering a concise and clear view of the information. Implementing data validation checks, enforcing data integrity constraints, and using data duplication and matching tools are essential for maintaining validity and uniqueness in your data.

Achieving Data Quality

Customers have a rich portfolio of tools to choose from to achieve data quality. SAP Master Data Governance provides a central hub for master data management and governance. With it customers can create a single source of truth by uniting SAP and third party data sources. In addition, various teams can own unique master data attributes and enforce validated values for specific data points through collaborative workflow routing and notification. Finally customers can Define, validate, and monitor established business rules to confirm master data readiness and analyze master data management performance. SAP Master Data Governance can be deployed on premise (on SAP S/4HANA), with SAP S/4HANA Cloud (public and private edition) and on SAP Business Technology Platform (SAP Master Data Governance, cloud edition).

In addition to SAP Master Data Governance customers can use SAP Information Steward to understand, analyze, and quantify the impact of data on their business processes to enhance operational, analytical, and data governance initiatives. SAP Datasphere is also available and is a comprehensive solution providing data capabilities such as integrating, cataloging, semantic modeling, data warehousing and virtualization. Customers who currently use SAP Data Intelligence Cloud and SAP Data Warehouse Cloud can refer to SAP Datasphere and SAP Data Intelligence Cloud - what does this mean for me? for more information on transitioning to SAP Datasphere.

Finally, customers are encouraged to adhere to the SAP One Domain Model (ODM). This model defines a common, harmonized data model to facilitate data exchange and reuse between business applications of the Intelligent Suite and its ecosystem. They are also encouraged to use the SAP Data and Analytics Advisory Methodology the purpose of which is to provide guidance in the design and validation of solution architectures for data-driven business innovations.

Data Volume Efficiency (From Creation to Retirement)

We now turn our attention to data volume efficiency. One major step towards becoming clean core data compliant involves removing redundant, outdated, or unused data. It's a straightforward concept, but it's essential in maintaining the usefulness and relevance of the database.

Data has a shelf life. As time passes, its relevance diminishes, turning it into a 'data deadweight.' This deadweight burdens the system, slows down processing, and clutters the interface, making it harder to find timely and relevant information. Therefore, it's imperative to routinely purge such data from the system. Similarly, data redundancy and unused data pose severe efficiency concerns. Redundant data offers no fresh insights but takes up valuable space and resources. At the same time, unused data represents missed opportunities. Regular data audits can help identify and eliminate such unnecessary information.

Data Privacy Compliance

The final pillar in clean data is data privacy compliance. As companies collect and analyze more personal data, concerns about privacy and data misuse have risen. It's crucial that personal master data be stored only when necessary and for justifiable purposes. Collecting unnecessary personal data may lead to misuse or unintended breaches, damaging trust, and potentially bringing legal repercussions. It's essential to respect user rights, collect only data required for operational needs, and maintain transparency about data usage.

Data privacy compliance is best addressed through SAP Information Lifecycle Managementwhich allows the automation of data archiving and retention, as well as the decommissioning of legacy systems, while balancing the total cost of ownership, risk, and legal compliance.

Goals for data:
  • Complete
  • Correct
  • Used and relevant

Benefits of Clean Data

The benefits of clean data are numerous. "Data To Value" (defined as the reliability of results when using data in processes and applications) is achieved. Also stability and quality of business process steps (business process efficiency) along with reduced TCO due to efficient data volume management becomes possible. Finally, the improved ability to exchange data between different solutions paired with a lower risk of breaching data privacy protection regulations can be accomplished.

Conclusion

Proficiency in clean data in SAP S/4HANA requires a comprehensive understanding of modern data quality requirements, strategies for ensuring data volume efficiency, and responsible management of personal master data. By adhering to these principles, organizations can improve the reliability, accuracy, and compliance of their data, ultimately leading to better business outcomes.

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