Most migrations fail on unvalidated assumptions, not data volume. The method that works: validate at every step, run old and new in parallel until the numbers reconcile, and treat undocumented business logic as the real migration object.
According to Gartner, 83% of data migration projects fail outright or exceed budget and timeline. Legacy compatibility issues affect two-thirds of enterprise migrations, and timeline estimates miss by 40–60%. The cause is rarely the data volume — it is unvalidated assumptions about what the data means.
Field-level rules catch errors at extraction — not at go-live billing, when fixing them costs 100×.
Old and new systems run side by side; cutover happens when results match across full business cycles, not when the calendar says so.
Decades of undocumented rules live in legacy data. We map what the data means operationally — with the people who use it — before a single record moves.
Every transformation recorded: what moved, what changed, what was excluded and why. Regulators ask; the answer should be a report, not a meeting.
For insurance cores we plan 6–12 months including parallel running — but the honest answer depends on data quality discovered in week one. We scope after a data assessment, not before.
Yes — that is the point of parallel running. The old system stays the system of record until the new one proves itself on real cycles: billing runs, renewals, claims settlements.
It is mapped, transformed with recorded rules, or consciously archived with documented rationale — never silently dropped. The audit trail covers every record.
How long does a data migration usually take?
How do you guarantee zero data loss during migration?
Can you migrate from a legacy core insurance system?