For organizations moving to SAP S/4HANA or RISE with SAP, one of the most pressing questions is around managing data. With many existing ERP customers having years of data in their existing systems, data preparation can be a critical step in ensuring a successful move to SAP S/4HANA. Whether it is years of historical data or a rapidly increasing volume of new data, making the right data choices before and during the transformation may be the difference between a more successful or less successful transition.
Effective Data Preparation
While there is a general understanding of some data changes that need to be made when moving to SAP S/4HANA – including data cleansing and harmonization, plus the transition to the Universal Journal – this only covers part of the data equation. The SAP S/4HANA data model is simplified compared to that used in SAP ECC due to the change from using a traditional row-based relational database to the SAP HANA in-memory columnar database. But this is only part of the difference between SAP ECC and SAP S/4HANA. Although the old SAP ECC data model is simulated in SAP S/4HANA using a virtual model consisting of CDS views, cleaning up and restructuring data to simplify the transition process makes a significant long-term difference.
A bigger challenge for organizations with substantial volumes of data is cost. Since SAP S/4HANA requires SAP HANA and the licensing is based on data volumes, the more an organization limits the amount of data that is transitioned, the greater the potential to minimize unexpected costs. Most organizations categorize data into classifications such as hot, warm, and cold based on how frequently that data will be accessed. While hot data, which is frequently accessed, needs to move into SAP S/4HANA, the same may not be true of warm or cold data.
However, although warm data may not be required for daily operations, organizations need to find a way to ensure that this data is accessible within a reasonable timeframe because it can still be required for reporting, analysis, and auditing purposes. Not moving data into SAP S/4HANA but ensuring that it remains available requires not only classifying and preparing the data, but also using the right data storage tools to keep the data accessible.
Beyond data classification, one of the biggest challenges identified by those moving to SAP S/4HANA is that of cleansing and improving data quality the right way. This is because data cleansing and harmonization involves steps such as duplicate removal, standardization, validation, enrichment, and error correction. While there is an obvious need to ensure that data is of the highest quality when moving to SAP S/4HANA, those that are moving to RISE with SAP should pay particular attention to this step since the quality of data in the system will have an impact on the potential use of AI in the future.
What This Means for SAPinsiders
- Take the time to understand existing data landscapes to kickstart future ERP plans. The more time that is spent understanding, preparing, and cleansing data in an existing landscape, the easier it will be to complete any future transformation or transition plans. From a data perspective, initial preparation work that is done is not wasted when it comes to future planning, no matter what that might be.
- Explore the benefits of a proactive approach to data management. Many organizations have accumulated years or decades worth of data in their ERP systems. A proactive approach to data management allows organizations to archive or decommission data that is no longer actively used in the system, which can both improve system performance and streamline future transformation plans. Taking the time to understand how much benefit can be achieved by a proactive strategy is crucial.
- Learn more about options for right-sizing and preparing data for the move to solutions like SAP S/4HANA and RISE with SAP. With multiple options available and organizations under pressure from a timeline perspective, there has never been a better time to learn more about data management and data preparation. One example is the upcoming webinar hosted by SNP on how to best prepare data for an ERP transformation, managing data after the move, and best practices for decommissioning.
