Data migration moves data from one system to another. A company upgrades its ERP. A team moves from on-premises databases to the cloud. Two organizations merge and must combine their systems. The migration transfers data, transforms it to fit the new schema, and verifies that nothing was lost or corrupted. It sounds straightforward. It rarely is.

Migrations fail for predictable reasons. The source data is dirtier than expected. The target schema does not map cleanly to the source. Business rules embedded in the old system are not documented. The migration takes longer than planned and the old system gets decommissioned before the new one is ready. Downtime is required and the business cannot afford it. The best migrations start with profiling. Understand the source data: its quality, its quirks, its volume. Map every field to the target. Write transformation rules. Test with a subset before migrating everything. Run parallel systems for a period. Verify record counts, totals, and sample records. Have a rollback plan. Migrations are risky because they touch everything. A missed field can break a downstream process. A corrupted record can go unnoticed for months. The planning matters more than the execution. By the time data moves, the hard decisions should already be made.

Migration phases

1. Profile — understand source data quality and structure
2. Map — define source-to-target field mappings
3. Transform — write rules to convert data
4. Test — migrate a subset and verify
5. Execute — move the full dataset
6. Validate — confirm counts, totals, and samples

Data migration is a project, not a task. Treat it with the planning it deserves.