Shared Services Handbook Hit the road. 3 Phase 1 – Assess feasibility. That the migration itself is managed and controlled. Data Migration Project Checklist: a Template for Effective data migration. Phase 3: Landscape Analysis. What escalation procedures will be in place? There should be clear instructions on what exactly needs to be delivered, don't. Focus on pruning data that is historical or surplus to requirements (see here for advice).
Compliance and business risk plays a significant role in the implementation of corporate information systems. The risks associated with these systems are, in general, well known. However, as part of the implementation process, many of these information systems will be populated with legacy data and the compliance and business risks associated with data and content migrations are not necessarily understood.
In this context, risks associated with data migration are a direct result of migration error. Further, industry testing strategies to mitigate such risk, or more specifically data migration error, lack consistency and are far from deterministic. This article offers thoughts and recommendations on how to create a more robust and consistent data migration testing strategy.
Before diving into the detail, a bit of background - has tested hundreds of data and content migrations, primarily in FDA regulated industries (pharmaceuticals, medical devices, bio-technologies and food products), manufacturing and autos. The information presented here includes some of the lessons learned from our clients’ quality control and the actual error history from testing the migrations of hundreds of thousands of fields and terabytes of content. The recommended approach to designing migration testing strategies is to document one’s risk, the likelihood of occurrence, and then define the means to mitigate risk via testing where appropriate. The identification of risks is tricky and much of the process will be specific to the system being migrated. Let’s review two systems to illustrate this point: • In the first case, migrating financial data in retail banking is typically defined by high volume migrations (10's, or 100's of millions of records) where the source to destination records are quite similar and involve minimal translation and little if any data enrichment. • For a second example, consider a consumer products company’s complaints management. These systems are typically not mature and newer implementations, and their associated business processes, have to adapt to varying business and compliance requirements.
These systems have modest volume in comparison (10's, or 100's of thousand of records) with complex translations, and data enrichment to complete the newer record as they are migrated. In both cases, getting the migrated data accurately represented in the destination system is critical. However, the process by which accuracy is defined will vary significantly between these two systems and their associated migrations. In the first case, the financial services industry has evolved to the point where data interchange standards exist, which simplifies this process greatly. In the case where complaint management data is migrated, significantly more upfront analysis will be required to “best fit” the legacy data in the new system. This analysis will derive data enrichment to fill in incomplete records, identify data cleansing requirements through pre-migration analysis, dry runs the migration process and verify the results sting before understanding the final data migration requirements.
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