Services · Deploy
Data migration
Your records moved from old systems to new ones, cleaned on the way and reconciled before anyone switches over.
Data migrations rarely fail at the copy step. They fail on the field that meant something different in the old system, the duplicate customers, the dates in three formats. We map every source field to its target, write repeatable transformation scripts, run trial loads and reconcile the results with you until the numbers match.
What you get
- A field-by-field mapping document from each source to the target system
- A data quality report listing the duplicates, gaps and invalid values found
- Repeatable extraction and transformation scripts kept in version control
- Trial loads into a test copy of the target, each with a reconciliation report
- Record counts, totals and checksums compared between source and target
- A cut-over plan covering the data freeze, final load and sign-off
How it works
- 01
Profile
We read the source data itself, not just the schema, to find formats, duplicates, orphans and hidden meanings.
- 02
Map
Every field gets a target, a transformation rule and an owner on your side who confirms it is right.
- 03
Rehearse
Trial loads run into a test environment until counts, balances and spot checks match the source.
- 04
Cut over
We freeze changes, run the final load, reconcile again and hand over once your team signs off.
Scope it now.
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Questions
How do we know nothing was lost?
Every load ends with reconciliation: record counts per table, financial totals, checksums on key fields and samples checked by your own staff. Any difference is traced to a rule or a source record before sign-off.
Should we clean the data before or during the move?
Usually during. We report the problems, you decide the rule for each one, and the transformation scripts apply it consistently. Cleaning by hand in the old system first is slower and often has to be redone.
Can old and new systems run in parallel?
Yes, where the business needs it. We can run delta loads that copy new and changed records until cut-over, so the old system stays in use while the new one is tested with real data.