Data Migration Assumptions Log
This is a practical guide to building a assumptions log for a data migration project — a register of the assumptions the plan depends on, to be validated, adapted to the realities of moving data from legacy systems into a new platform.
What a Assumptions Log is
A assumptions log is a register of the assumptions the plan depends on, to be validated. For the full concept and how it works in general, see Project Assumption. 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 assumptions log 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
- Assumption
- Impact if wrong
- Owner
- Validation status
Data Migration-specific considerations
Tailor the assumptions log 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 assumptions log 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.