Guide

Data migration validation: nothing dropped or changed

Data migration validation compares the old system against the new — pre- vs post-migration — to prove nothing was dropped, duplicated or quietly changed. Connect both (or drop extracts) and every record is reconciled deterministically, with each difference explained.

Migrate with proof: reconcile legacy vs target balances and records automatically, flag every discrepancy with its root cause, and keep an auditable record of a clean cut-over.

How it works

1 · Connect old & newLegacy and target systems — files, databases or APIs.

2 · ReconcileA deterministic engine matches every record pre- vs post-migration.

3 · Prove a clean cut-overEvery dropped, duplicated or changed record flagged with lineage.

What you get

Pre- vs post-migration — prove balances and records match after the move.
Catch silent changes — dropped, duplicated or quietly altered rows surfaced.
Mapping-aware — the AI maps renamed columns and reshaped records across platforms.
Report parity — confirm legacy BI vs new BI reports still match.
Repeatable dry-runs — re-run the validation on every migration rehearsal.
Auditable — timestamped, exportable evidence of a clean migration.

Migration validation vs. manual sampling

Spot-checking a sample of migrated records misses the records that silently dropped or changed. Full data migration validation reconciles every record between legacy and target deterministically — handling renamed columns and reshaped data — so you can prove the whole migration, not just a sample.

This is one of 20+ checks in AI data validation — the same engine also runs reconciliation, data quality, profiling, document compare and governance. For sensitive data, the full dataset never leaves your server; see the security page.

FAQ

What is data migration validation?

Data migration validation reconciles the data in a legacy system against the new target system — pre- vs post-migration — to prove nothing was dropped, duplicated or changed. AI DataValidator matches every record deterministically and flags every discrepancy, with no code.

Can it handle columns that were renamed or reshaped during migration?

Yes. An AI copilot maps differently-named columns and reshaped records across the two platforms, so the pre/post comparison lines up even when the schema changed.

Can I validate a migration rehearsal repeatedly?

Yes — save the rules and re-run the validation on every dry-run and the final cut-over, so each rehearsal is checked the same way.

Is the result auditable for sign-off?

Every run is logged and exportable, giving you timestamped evidence of a clean migration for controls and sign-off.

Validate your data migration free

Prove nothing was dropped, duplicated or changed — on every rehearsal and the final cut-over.

Start free — 7 days, full access