Data migration is the activity in an ERP implementation that most consistently underestimated, most frequently delayed, and most reliably responsible for go-live failures when it goes wrong. Moving data from legacy systems into a new ERP is not a technical transfer — it is a business-critical transformation that requires analytical rigor, business validation, and multiple rehearsal cycles before it can be trusted for production. Projects that treat data migration as a technical task discover its business significance the hard way.
The Migration Readiness Assessment
Before migration planning begins, the source data needs to be assessed for quality. Source data assessment examines completeness — are the records that are supposed to exist actually present? Accuracy — do the data values match the real-world entities they represent? Consistency — are related data elements consistent with each other? And currency — are records that should have been updated, closed, or deleted actually in the correct state? Source data assessment almost always reveals quality problems that need to be corrected before migration can proceed.
The Migration Approach Decision
Not all historical data needs to migrate into the new system. The migration scope decision — what data to migrate, in how much detail, and over what historical period — is a business decision with significant cost and complexity implications. Migrating full transaction history from a twenty-year-old system is expensive and often unnecessary; migrating open balances, current master data, and a defined period of recent transaction history is usually sufficient and significantly less complex. Making this decision explicitly and early prevents the scope creep that turns data migration from a manageable workload into a project within a project.
Migration Rehearsals Are Non-Negotiable
The data migration that runs only once — on go-live weekend — is the data migration that discovers its problems in production. Migration rehearsals — full, end-to-end test migrations that load data into a system environment configured identically to production — reveal the technical failures, the transformation errors, and the validation gaps that exist in the migration scripts. Each rehearsal produces a list of issues to fix. Each subsequent rehearsal validates that the fixes worked and reveals any new issues introduced by the fixes. Organizations that run three or more full rehearsals before go-live consistently have cleaner migrations than organizations that run fewer. The rehearsal investment is repaid in go-live confidence.