Data migration is often described as moving information from one system to another. In practice, it is a business transformation activity involving definitions, ownership, quality, history, compliance, and user trust.
Profile before planning
Start by understanding record counts, completeness, duplicates, formats, relationships, historical depth, attachments, inactive records, and known exceptions. Estimates based only on database size rarely reflect the real effort.
Define what should move
Not every record belongs in the target system. Establish retention rules, archival requirements, date ranges, inactive-record policies, and legal constraints. Moving less data can improve quality and reduce risk when the decision is intentional and documented.
Create a business-owned mapping
Field mapping should include source, target, transformation, required status, default behavior, lookup logic, data owner, validation rule, and exception handling. Business stakeholders must confirm what values mean—not just where they are stored.
Clean data at the right stage
Some issues are best corrected in the source, others during transformation, and others after loading. The team should avoid uncontrolled manual cleanup that cannot be repeated during later test cycles.
Test relationships and sequencing
Accounts, contacts, opportunities, cases, donations, programs, activities, files, and custom records often depend on one another. Load sequencing and external identifiers should be designed to preserve those relationships across repeated runs.
Reconcile every migration cycle
- Compare source, transformed, accepted, rejected, and loaded record counts.
- Validate financial and operational totals where applicable.
- Review representative records with business users.
- Track rejected records and disposition decisions.
- Confirm ownership, sharing, and reporting behavior.
Plan for cutover
Define data-freeze timing, delta migration, user communications, rollback criteria, final validation, and post-launch support. Cutover should be rehearsed using the same tools and sequence intended for production.
Make governance the final deliverable
The migration should leave behind duplicate-prevention rules, ownership responsibilities, integration controls, data-quality monitoring, and documented standards. Otherwise, the new system will gradually reproduce the same problems.