40+ extraction checkpoints
What to verify before data ever leaves the source system — the stage where missed edge cases are cheapest to catch and most expensive to discover later.
Free resourcePDF · 150 checkpoints
150 checkpoints so a bad migration doesn’t poison a good go-live
Extraction, transformation, validation, and cutover — the phase most transformations treat as a technical afterthought and end up rebuilding trust in for a year afterward. 150 checkpoints across all four stages, nothing left to memory.
- 40+ extraction checkpoints
- 35+ transformation rules
- 45+ validation steps
- 30+ cutover activities
- 150
- Free
What's inside
Everything in Data Migration Checklist
35+ transformation rules
Checkpoints for the mapping and cleansing logic that turns source data into target-system-ready data, where silent field-level errors do the most long-term damage.
45+ validation steps
Data quality metrics and acceptance criteria — the largest single block in the checklist, because validation is where most teams cut corners under deadline pressure.
30+ cutover activities
Reconciliation procedures with sign-off templates, so go-live has a defined, auditable close rather than an implicit "looks fine" from whoever is watching the dashboard.
Why it matters
Data is the AMIGA dimension that fails silently
A broken process shows up as a missed deadline. Broken technology throws an error. Bad data migrated cleanly into a new system does neither — it just sits there, structurally valid and factually wrong, until it corrupts a report, a decision, or a customer interaction months after go-live.
That is what makes data migration disproportionately dangerous relative to how much attention it gets. Extraction and transformation are treated as engineering tasks with a clear technical finish line, so the checkpoints that actually catch quality problems — validation against business rules, reconciliation against the source of truth — are the ones most likely to get compressed when the schedule slips.
The four-phase structure here exists so nothing depends on one engineer remembering to check for it. Extraction and transformation checkpoints catch problems while data is still cheap to fix; validation is deliberately the largest block, because that is where quality actually gets confirmed rather than assumed; cutover closes the loop with a sign-off, not a shrug.
150 checkpoints is not a suggestion to run all of them on every migration — it's a library to pull from so the checkpoints you skip are a decision, not an oversight.
Who it's for
- Data leads and architects planning a migration cutover
- Program managers who own the go-live decision but not the ETL work itself
- QA and testing leads building validation criteria for a migration
- Consultants standing up a migration workstream on a new engagement
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Questions
Before you download
No. It's a library organized across extraction, transformation, validation, and cutover — most migrations use a subset. The point is that what you skip is a deliberate scoping decision, not something nobody thought to check.
No. The checkpoints are organized by migration phase and outcome, not by tooling, so it applies whether you're migrating with a dedicated ETL platform, custom scripts, or a vendor's built-in migration utility.
Because it's the phase most likely to get compressed under deadline pressure — extraction and transformation have a visible technical finish line, but validation is where data quality against business rules actually gets confirmed rather than assumed.
Yes — a free PDF, no watermark, no expiry. We ask for your name and email to send the link and, if you want it, occasional transformation guidance you can unsubscribe from any time.
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