Data Migration Methodology

Large enterprise data migrations fail more often than vendors admit. Not because of technical complexity — though there's plenty of that — but because nobody had an accurate picture of the source data before the programme started.

The problems that kill migration projects tend to surface mid-execution. Records that weren't in any inventory. Primary keys missing across entire table sets. Data quality issues that were manageable in the source system but become blockers when you try to load them somewhere else. Sensitive data scattered across fields that weren't flagged in the original scope. And by the time any of this is visible, the go-live date is fixed and the budget is committed.

When the source landscape spans Salesforce, SAP — both S/4HANA and ECC — Siebel, and custom platforms built over decades, each with its own data model and its own accumulated mess, the inventory problem alone is substantial. Add phased delivery across geographies, business units, and divisions, and reconciliation becomes a programme in its own right. Most organisations don't find out how bad the source data actually is until they're already in trouble.

The methodology starts before any data moves. That's the part that matters most and gets skipped most often.

Source identification and extraction preparation come first — building an accurate picture of what exists, where it lives, and what condition it's in. Metadata extraction follows, mapping table structures, relationships, and field-level data quality across all source systems. Nothing moves until that picture is complete.

Validation runs against the extracted data before any load. Quality issues that would cause reconciliation failures downstream are identified and resolved at source — not discovered after a test load fails at 2am the night before go-live.

Data masking is applied early in the process, not bolted on as a final compliance step. Sensitive fields are catalogued, validated, and masked before the data ever moves toward a production environment. The compliance requirement is treated as an extraction constraint, not a post-migration task.

Test loads run against real data under real conditions. By the time the production migration executes, the team has already seen the data behave across the full pipeline. The final load is not the first time anything surprises them.

These numbers are from a live engagement — not projections: