Data Migration Done Right: Lessons From Moving 12 Million Records

Data migration isn't a technical problem. It's a trust problem. The business needs absolute confidence that every record arrived intact, every relationship preserved, every calculation correct. Here's how we build that confidence.

Phase 1: Discovery. Before touching any data, we spend 2 weeks documenting the source system. Not the schema — the actual data. What's in the fields? What are the edge cases? Which fields have been repurposed over the years? Which records are orphaned? This phase always reveals surprises.

Phase 2: Transformation rules. We document every mapping, every conversion, every business rule as executable code. Not spreadsheets — code. Each transformation rule has an automated test. For the banking project, we wrote 340 transformation rules and 890 tests.

Phase 3: Parallel run. The old and new systems run simultaneously for 30 days. Every transaction processed in the old system is also processed in the new. At end of day, we run automated reconciliation. Any discrepancy triggers an investigation before morning.

Phase 4: Cutover. The actual migration happens over a weekend. But because we've been running parallel for 30 days, the cutover is just flipping a switch. All data is already in the new system. We just stop writing to the old one.

The methodology adds 6-8 weeks to the project timeline. But it eliminates the 3-month "stabilisation period" that most migrations suffer through. Net result: faster time to value, higher confidence, zero data loss.