Continuing my previous post on detailing the phases of the building a migration utility, this post is going to be on the second phase  i.e. Data Assessment and Mapping. As part of the planning process the team will take an initial look at the source and target systems. The objective is to know enough about both systems to make an estimate of the effort to complete mapping and migration.

The biggest success factor in the data migration is the quality of source data, which is also the biggest risk. The most important phase in a data migration project involves the task needed to understand the data content. Our experience in building data migration utility has made us realize that projects fail or are delayed upon discovering that the data is not fit to use.  Getting a sample database from the client during the initial stages itself will give an idea of the kind of data issues that might arise.  Data profiling technology can be used to systematically scan the tables of interest to quantify any data quality and data integrity issues such as: missing values, ranges and outliers, data integrity analysis, parsing requirements to enable mapping of source columns to the target columns etc.

Some of the things to consider for data mapping would be:

  • Some of the target requirements will require significant transformations or the creation of new data content/
  • Table to Table copy or SDK API Mapping

For any matching that is done, each subject area mapped must have a reconciliation base validating that all records extracted were processed. If the work of scoping, mapping and data quality assessment have been completed then building the migration utility will be easier. More on building the utility in the next post.

(Additional inputs taken from SAP Thought Leadership - Data Migration)
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