Data Migration Checklist Template: What to Actually Put in It
A working data migration checklist template needs six core fields for every data element being migrated: source field, target field, transformation rule, data type, validation status, and a named owner — tracked as live rows in a spreadsheet or system, not a static document reviewed once and forgotten. Gartner research cited in Oracle's own data migration white paper puts the failure rate for data migration projects above 80%, and a template that exists as a one-time checklist rather than a living tracker is a common contributor to that number.
Why a Checklist Isn't the Same as a Template
A checklist tells you what categories to cover — extraction, mapping, validation, cutover. A template is the actual working document those categories get executed through, with specific fields tracked row by row for every data element in scope. If you're looking for the checklist categories themselves, the full 150-point Data Migration Checklist covers that. This piece is about what the template built from those categories actually needs to contain.
What Fields Does a Real Data Migration Template Need?
| Field | Purpose |
|---|---|
| Source field | The exact field name and system the data currently lives in |
| Target field | Where that data maps to in the new system |
| Transformation rule | Any conversion logic — unit changes, code mappings, format changes — applied during migration |
| Data type | Confirms source and target types are compatible before migration, not discovered as a runtime error |
| Validation status | Not yet validated / passed / failed — tracked per field, not just per table |
| Owner | The specific person accountable for that field's accuracy, not a team name |
| Reconciliation method | How this field will be checked post-migration — row counts, control totals, or sample-based deep validation |
How Is This Different From a Generic Spreadsheet Checklist?
Most teams start with a generic checklist copied from a template library, with column headers that don't reflect their actual source and target systems. A working template is specific — it's built after an initial data discovery pass, with real field names from the real legacy system, not placeholder rows waiting to be filled in later under deadline pressure.
Who Should Own Each Row in the Template?
Every row needs exactly one named owner — the same principle that makes a decision rights matrix work. A field with "IT team" as its owner tends to be the field nobody actually validates before cutover, because no single person feels direct accountability for it.
How Do You Move From a Static Template to a Live Tracker?
A spreadsheet works for smaller migrations, but it breaks down once a program is tracking hundreds or thousands of fields across multiple source systems — version control problems, stale data, and no visibility into which fields are actually blocking cutover readiness. This is the gap the AMIGO platform was built to close: it turns the static template into a live, AI-enhanced data migration tracker, integrated with the same program's governance and RAID log, so a field marked "validation failed" shows up as a program risk automatically rather than staying buried in a spreadsheet tab nobody reopens until the week before go-live.
Where This Fits in the Bigger Picture
Data migration template tracking is a core practice within the Data dimension of the AMIGA Framework, and it connects directly to Governance — a stalled validation status is exactly the kind of item that belongs in a RAID log, not just a spreadsheet cell.
Get the full 150-point Data Migration Checklist → · See how AMIGO operationalizes it →
Frequently Asked Questions
Should the template be built before or after initial data discovery?
After — a template built before discovery tends to use generic placeholder field names rather than the real source and target fields, which means it has to be substantially rebuilt once discovery is complete anyway.
How granular should tracking be — per table or per field?
Per field. Tracking validation status only at the table level hides exactly the kind of partial failures — one bad field in an otherwise clean table — that commonly slip through to production.
Is a spreadsheet template sufficient for a large migration?
It can work for smaller migrations, but spreadsheets tend to break down at scale due to version control issues and lack of real-time visibility — larger programs generally benefit from a connected tracking system rather than a standalone file.
What's the biggest mistake teams make when building this template?
Leaving the owner field generic — assigning a team name instead of a specific individual — which is one of the most common reasons validation gaps aren't caught before cutover.