Data Migration Lessons Learned
This is a practical guide to building a lessons learned for a data migration project — what went well and badly, captured to improve future projects, adapted to the realities of moving data from legacy systems into a new platform.
What a Lessons Learned is
A lessons learned is what went well and badly, captured to improve future projects. For the full concept and how it works in general, see Lessons Learned. On a data migration project it plays the same role, tuned to this kind of work.
Why it matters for a Data Migration project
Data Migration projects live or die on moving data from legacy systems into a new platform. A well-built lessons learned gives the team a shared, explicit reference for exactly that — reducing ambiguity, aligning stakeholders, and making problems visible early enough to act. Skipping it, or doing it generically, is how data migration projects drift into avoidable delay and cost.
What to include
- What happened
- Impact
- Root cause
- Recommendation
Data Migration-specific considerations
Tailor the lessons learned to the risks that most often derail data migration projects:
- Data quality and lineage
- Field mapping errors
- Reconciliation and cutover
Example
On a real data migration project, the lessons learned would be shaped by moving data from legacy systems into a new platform. In particular, it should explicitly account for the project’s biggest risks — data quality and lineage, field mapping errors, reconciliation and cutover — rather than treating them as afterthoughts.