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Have you ever wondered what happens to the data not being migrated to the target SAP S/4HANA system?
Let us start with what we do with the data per migration approach. In the case of system conversion-led migration, you begin with reviewing the data that is irrelevant to the target system, such as in-active company codes, etc., and run pre-projects to remove those data from the source system. With selective data transition-led transformation, as evident from the name itself, you only take over relevant data to the target system (either specific org unit data, specific time-slice data, data with specific statuses or process-specific data). For a new implementation, the typical strategy is to take over only master data and open documents. Irrespective of the migration approach, it is critical to manage data so one does not lose on leveraging them to make business operations efficient. In a typical transformation scenario, the ambition is to take over only relevant data to the target system. In such a case, what happens to the data that is not migrated to the target system and left behind? How can we continue leveraging such data for machine learning-enabled applications, predictive analytics, historical trend analysis, and reporting?
Data plays an essential role in the success of the SAP S/4HANA transformation journey, irrespective of the migration approach. In today’s world, we cannot afford to not capitalize on the value of data. It is, therefore, vital that the right level of attention is given to studying the current data situation of the source system and appropriate measures are incorporated in the transformation roadmap that will result in operational efficiency and higher acceptance and satisfaction among end-users. I recommend categorizing data in the source system as relevant for:
I will outline a few key considerations to build a solution to manage data as part of your transformation journey.
The requirements for leveraging may vary from customer to customer. Hence, there is a need to have a comprehensive solution to address the entire data scope as part of the SAP ERP transformation. Understanding the customer situation and building a solution in the context is essential.
Do share your feedback if you have worked on a similar requirements around managing data as part of your transformation journey.
Let us start with what we do with the data per migration approach. In the case of system conversion-led migration, you begin with reviewing the data that is irrelevant to the target system, such as in-active company codes, etc., and run pre-projects to remove those data from the source system. With selective data transition-led transformation, as evident from the name itself, you only take over relevant data to the target system (either specific org unit data, specific time-slice data, data with specific statuses or process-specific data). For a new implementation, the typical strategy is to take over only master data and open documents. Irrespective of the migration approach, it is critical to manage data so one does not lose on leveraging them to make business operations efficient. In a typical transformation scenario, the ambition is to take over only relevant data to the target system. In such a case, what happens to the data that is not migrated to the target system and left behind? How can we continue leveraging such data for machine learning-enabled applications, predictive analytics, historical trend analysis, and reporting?
Data plays an essential role in the success of the SAP S/4HANA transformation journey, irrespective of the migration approach. In today’s world, we cannot afford to not capitalize on the value of data. It is, therefore, vital that the right level of attention is given to studying the current data situation of the source system and appropriate measures are incorporated in the transformation roadmap that will result in operational efficiency and higher acceptance and satisfaction among end-users. I recommend categorizing data in the source system as relevant for:
- Business: Data that is required for day-to-day business operations.
- Analytics: Data you want to leverage for your analytical, predictive or machine learning applications.
- Compliance: To meet legal and country-specific requirements, you must keep data read-only.
- De-commissioning: Data that is no longer required and can be de-commissioned.
Data Categories in ERP Transformation
I will outline a few key considerations to build a solution to manage data as part of your transformation journey.
- Know your data well: Run a pre-study for data transparency in your source systems. Work with stakeholders to categorize the data as business-relevant, analytics-relevant, and relevant for compliance and de-commissioning.
- Decide on storage format for analytics-relevant data: Do you want to keep the data in source format or transform it to the target solution structure (which may have significantly changed due to org structure transformation, process harmonization, etc.)? Keeping in the source data format is a disadvantage for your downstream applications as they must never forget the mapping between source and target data for any analytics and reporting.
- Decide where to store left-behind data: Do you want to keep the data in a read-only SAP ERP (SAP ECC if no transformation is required or SAP S/4HANA if the change of the left-over data is needed) system? Keeping data in SAP systems may be a very resource and cost-intensive approach. Other options could be to store the data in cheaper storage like Data Lake and access it via applications.
- Leveraging left-behind data: How and where you want to leverage these data depends. For a reporting requirement, your application can access business and analytics relevant data and present the required information in specific dimensions. You can also use analytics-relevant data to train your machine learning-enabled applications.
The requirements for leveraging may vary from customer to customer. Hence, there is a need to have a comprehensive solution to address the entire data scope as part of the SAP ERP transformation. Understanding the customer situation and building a solution in the context is essential.
Do share your feedback if you have worked on a similar requirements around managing data as part of your transformation journey.
