Legacy System Migration Mapping
Migrating off a legacy system means someone has to work out how every field in the old schema maps to the equivalent field in the new one, and legacy systems are rarely well documented, so a lot of that mapping is reverse-engineered by looking at sample data and asking whoever's been around longest what a cryptically named column actually means. The mapping is usually built once, by hand, in a spreadsheet, and treated as final — but source systems keep changing during the migration project (a field gets renamed, a new required field appears, a data type changes), and if the mapping isn't re-validated against the live schema right before cutover, records fail to migrate or migrate with silently wrong values, and nobody notices until someone downstream can't find a record that should exist.
STARTING PRICE
From €299
Standard tier · Multi-step workflow with AI extraction/decisioning and 2-3 integrations.
Get a quote →Saves roughly 15-25 hrs on a typical migration project, plus avoided post-migration data cleanup.
How the automation works
We build and continuously validate the field mapping between your legacy system and the new platform, starting from an automated schema comparison rather than a hand-built spreadsheet, so every field, data type, and relationship is captured and any legacy fields with no clear destination are flagged explicitly rather than silently dropped. As the migration progresses, the mapping is re-validated against both schemas on a schedule, catching schema drift (a field renamed or resized in either system) before it causes failed or corrupted migrations. A sample-based validation pass migrates a representative batch of real records first and reports discrepancies field by field, so mapping errors surface on a test run rather than during the actual cutover.
Process flow
- 01
Schema pull from both systems trigger
The current schema of the legacy source system and the target system are pulled directly, rather than relying on outdated documentation.
- 02
Propose field mappings ai
Fields are matched between source and target based on name similarity, data type, and sample value patterns, with a confidence score per mapped pair.
- 03
Flag unmapped and ambiguous fields ai
Legacy fields with no clear target equivalent, or multiple plausible matches, are flagged explicitly for a human to resolve rather than guessed at.
- 04
Re-validate on a schedule ai
The mapping is re-checked against both live schemas periodically through the migration project, catching drift like renamed, resized, or newly required fields before cutover.
- 05
Run sample migration batch integration
A representative sample of real records is migrated using the current mapping, and results are compared field by field against the source to surface discrepancies.
- 06
Deliver validated mapping and migration report output
The final mapping document and a migration validation report are delivered, ready to support the actual cutover with known risk areas called out.
Inputs
- Legacy system schema and sample data
- Target system schema
- Business rules for required and transformed fields
- Representative sample record set for test migration
Outputs
- Field mapping document with confidence scores
- Unmapped/ambiguous field report for human resolution
- Schema drift alerts through the project timeline
- Sample migration validation report
Works with
Prefer a fully custom build instead of an off-the-shelf integration? We scope both options during your free consultation — most jobs like this one work fine on standard connectors, but higher-volume or non-standard systems sometimes need bespoke API work, reflected in the complex tier.
Where this goes wrong if you get it wrong
- Schema drift during a long migration project is the single biggest cause of failed cutovers — the source or target system keeps evolving while the mapping was built once at the start, so mapping needs to be re-validated against the live schema on a schedule, not treated as a one-time deliverable.
- Fields that look like an obvious match by name can carry different meaning or units between systems (a 'status' field with different value sets, a currency field in cents in one system and whole units in the other), and matching on name similarity alone will migrate the data cleanly while getting the meaning wrong.
- Legacy systems often have data quality issues baked in over years, orphaned foreign keys, inconsistent enum values, free-text fields that should have been structured, and the mapping process needs to surface these before migration, because the new system's constraints will reject or silently coerce what the old one tolerated.
- A migration that 'succeeds' on a clean sample batch can still fail on edge cases that weren't in the sample, records with null values in unexpected fields, unusually long text fields, or historical records created under an older version of the source schema — the sample needs to be deliberately drawn to include known-messy record types, not just recent, well-formed ones.
Frequently asked questions
What if the legacy system has no documentation at all?
The mapping process starts from the live schema and sample data rather than documentation, so undocumented systems are the normal case we work with, not a blocker.
How do you handle fields that don't have a clear equivalent in the new system?
They're flagged explicitly with the source data shown, so your team can decide whether to create a custom field, merge it into an existing one, or archive that data separately rather than losing it silently.
Can this catch problems before the actual cutover, not just after?
Yes, the sample migration batch is designed specifically to surface field-level discrepancies on a test run, while there's still time to fix the mapping before the real cutover happens.
Does this handle migrations between very different systems, like an on-premise database to a cloud SaaS platform?
Yes, the schema comparison and mapping logic works regardless of whether source and target are similar or structurally very different systems.