Define the migration boundary
Start with the business processes that depend on the data. This prevents a project from treating every historical record as equally urgent and makes retention, archive and migration decisions explicit.
Profile data before mapping it
A target schema does not resolve duplicate records, incomplete values or incompatible codes. Profiling reveals the conditions that mappings and validation rules must handle.
Validate through business scenarios
Technical row counts matter, but they are not enough. Test the migrated data through representative transactions, reports and exception paths with the people who understand the process.
Practical example
Example: an order migration
Before moving open orders, define whether fulfilled lines, credit holds, tax codes and shipment references must be available on day one. Each decision affects mapping, reconciliation and cutover scope.
Best practices
Put the fundamentals in place.
- Assign a business owner for each critical data domain.
- Reconcile counts and key totals, then validate representative business scenarios.
- Keep transformation rules and cutover decisions versioned and reviewable.
Frequently asked questions
When should data profiling begin?
Begin before mapping is finalized. Early profiling exposes quality and format issues that can otherwise surface late in testing.
What makes migration validation credible?
Combine technical reconciliation with process-based validation by the people who use the data.
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