Data Migration Strategy

It's important to document the activities that are both in scope and out of scope so that deliverables can be monitored. It is also important to list the Systems that are in and out of scope.

Plan as many test loads as possible and ensure the first test load does not support a testing cycle. Engage business resources are engaged as early as possible and ensure they understand the Business Owns The Data.  Assume the target design will be late and/or incomplete and document these before you start the test load.

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One of the most important elements of data migration is to validate the data that has been loaded and also document how (and who) will reconcile and approve financial values. This needs to be  compliant with any audit requirements. 

Cleansing is a core activity required to support data migration to enable the data to load successfully but also to ensure the business operates correctly. You need to assess your legacy quality as early in the project as possible as it is a resource hungry and time consuming activity needing business support.

Data faces risks as well as creating them due to the number of dependencies data has on other workstreams as well as other workstreams depending on data.

While these are all important risks that need to be managed during the project lifecycle, the most important risks to manage are those that could adversely impact your business at go live. Wrong or incomplete data can adversely impact your business.

Data has more dependencies than any other workstream yet is always on the critical path. They include the target design, environments, business data delivery, cleansing, user training and more besides. The data plan not only needs to align to the project plan but also with other workstream plans with all dependencies clearly documented.