SAP data migrations

The reasons for a data migration can be manifold. They range from the introduction of new software to the digitization of analog processes or complete technology changes. However, all data migrations have one thing in common: In order to make a data transfer successful, existing information must be adapted to a new data structure.

Data migrations are not only a necessary evil, but also offer great opportunities for business. They represent an ideal opportunity to clean up and tidy up old data stocks and, if necessary, to enrich them with external information.

No matter what the exact reason for a data migration is, it's usually about improving performance and competitiveness. But you have to get it right. Less successful data migrations can result in inaccurate data with redundancies and imponderables. This can happen even with fully usable and appropriate source data. In addition, problems that were already present in the source data can have an increased impact when migrating to a new, more complex system.

With a comprehensive strategy, you can ensure that a data migration runs smoothly and that problems are considered and resolved in advance.

• Perform data migrations as a separate project
  A data migration must be carried out gradually and systematically as an independent project. If a data migration is treated as an independent project, tools and methods are usually available to correctly plan, control and monitor processes.
• Involve departments
  Nobody knows their data better than the user - the specialist department. No migration can therefore be carried out by IT staff alone. Specialist departments should be involved in processes and informed about them from the very beginning. They can also take responsibility for the quality of the data. Problem cases and subsequent analyses can be prevented or at least limited.
• Knowledge of the data
  Prior to data migration, the source data must be subjected to a complete check. If this step is omitted, unexpected problems may occur. It is therefore advisable to schedule the time of the data check correctly.
• Cleanup
  If you have identified problems with the source data, they must be corrected. This may require additional software tools and resources.
• Maintenance
  Data gets worse and worse over time and therefore unreliable. Therefore, controls must be in place to maintain data quality.
• Governance
  Data quality tracking and reporting are important because they provide a better understanding of data integrity. The processes and tools used to provide this information should be user-friendly and include automated functions.