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I have outlined below few tips on best practices for Data Migration which I have learned from my experience. This article might lean towards SAP data migration but I have added perspectives on data migration from non-SAP sources also. These are based on my experiences.

Best Practices

1) Check for the data quality before starting the migration. Make sure the data quality is more than 95% as per end user needs in order for the migration to be successful.

2) Progressive data cleansing is a must . Ideally data cleansing has to start 3 to 6 months before the actual data migration. This will give enough time & space for the End Users to cleanse the data. Informatica AddressDoctor is one good tool for cleansing the addresses & creating a master list of addresses.

3) There may arise a situation when data from multiple sites/locations in the source system needs to be merged into a single target system. In this case, master data de duplication has to be performed.

4) A data migration project can also be a good point where the client can be asked to think about introducing a Master Data Management(MDM) module into their systems. There are several good MDM tools like SAP MDM, Informatica MDM which the client can make use of.

5) In case of data migration from multiple source systems from diverse Data sources like Oracle DB, SQL Server DB, SAP ERP source systems etc., the following points needs to be kept in mind before finalizing the data migration design:

a) The format,datatype, length of the primary keys/Control IDs(like Employee ID for HR module/Material ID for MM module) etc. which will form a basis needs to be finalized in the beginning itself. Consider the hypothetical scenario of a big MNC acquiring 2 or 3 small companies & wanting to merge their HR systems. One HR module may be in SAP HR, the other may be a custom built application using Oracle DB & the third may be a custom built application using SQL Server. In this scenario, it is very important to make sure to standardize the DB schema/tables of the target HR module considering the data present in all the three different source systems.

b) The driving tables,keys in each source system needs to be identified & a common platform established before starting the actual data migration. The Source to Target matrix(STM) has to be clearly defined during the HLD/Detailed Design stage.

5) Before uploading the data into target SAP system, it is a good practice for the SAP team involved in migration to check the quality/correctness/format of data. This will help prevent unnecessary iterations of loading the data again & again into target SAP system.

Data Warehousing Projects

The purpose of a Data Warehouse is to give power/enough data/KPIs to leadership to make high level business decisions.

This is achieved by the following:

1)     Generating daily/weekly/monthly reports for C –level executive decision making.

2)     Generating KPIs, scorecards for visual representation of historical/real-time data.

Data Warehouses are typically huge repositories/volumes of data gathered from multiple source systems. Implementing data warehouses requires huge investments of money with the end result/value addition achieved by management not fully known at the beginning of the project.

Data Migration Projects

Data Migration projects are executed for moving data from one/multiple source systems to a target system. There is well defined end result out of executing data migration projects. Hence, business will be very much inclined to invest money in executing Data Migration projects. This could be needed from a business perspective for one of the following reasons:

1)     Source system consolidation may happen. Multiple source systems might be merged into one.

2)     In a SAP ERP scenario, multiple ERP instances could be merged into a single instance to cut TCO (Total Cost of Ownership).

3)     Some source systems may become redundant/deprecated. Hence, those systems data has to be merged with another/new source system.

4)      Data could be migrated across database vendors, for example, from SQL Server to Oracle.

5)     Data migration from lower version to higher version say from Oracle 9i to Oracle 11g or from SAP ECC 4.6c to ECC 6.0.