Comprehensive field guide
Data Migration Field Guide
Data Migration Field Guide organizes the decisions that matter for organizations moving important records between spreadsheets, databases, CRMs, and applications: the current workflow, ownership, implementation choices, rollout risk, and acceptance evidence.
Working artifact
Data Migration journey map
Use this map to connect visible friction to the handoff, owner, and acceptance evidence that belongs to the data Migration journey.
| Journey stage | Risk to inspect | Decision to document |
|---|---|---|
| Source-data profiling and quality assessment | Duplicate and contradictory records undermine trust | SQL, CSV, spreadsheet, and API migration tooling |
| Field mapping, normalization, and deduplication | Source fields do not map cleanly to the destination | Repeatable transformations and exception reports |
| Dry-run migration, reconciliation, and cutover | Previous imports lost relationships or history | Counts, checksums, samples, and rollback evidence |
Read the situation before naming the solution
Duplicate and contradictory records undermine trust. Confirm who encounters it, where it occurs, and what changed before it appeared. Then distinguish the visible symptom from dependencies such as SQL, CSV, spreadsheet, and API migration tooling.
- Duplicate and contradictory records undermine trust
- Source fields do not map cleanly to the destination
- Previous imports lost relationships or history
Protect the current state
For Data Migration, Cleanup & Reconciliation, confirm account ownership, current exports or backups, recovery options, and recent changes before touching production. Preserve exact errors and timestamps that may disappear after a restart or update.
- Access owner
- Current backup
- Restore method
- Change history
Define the smallest useful result
Frame the first scope around source-data profiling and quality assessment and one observable acceptance journey. Treat field mapping, normalization, and deduplication as a later phase unless the evidence shows it is a true dependency.
- Source-data profiling and quality assessment
- Field mapping, normalization, and deduplication
- Dry-run migration, reconciliation, and cutover
Compare repair, extension, and replacement
Repair fits when the core remains sound. Extension fits when the boundary around SQL, CSV, spreadsheet, and API migration tooling is understood. Replacement fits when ownership, architecture, or operating risk prevents a responsible change.
- Time to value
- Data risk
- Reversibility
- Maintenance ownership
Plan implementation and launch
Sequence work around repeatable transformations and exception reports. Protect the people affected by “Duplicate and contradictory records undermine trust,” and define the point where rollback is safer than continuing.
- Repeatable transformations and exception reports
- Counts, checksums, samples, and rollback evidence
- Responsive and accessible web application delivery
Verify and hand off
Repeat the original journey, test a nearby failure, and document the result. A successful handoff leaves organizations moving important records between spreadsheets, databases, CRMs, and applications able to understand what changed, who owns it, and what happens next.
- Acceptance evidence
- Current documentation
- Monitoring owner
- Prioritized next step