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Data Migration Mapping Sheet
A practical field-mapping and QA framework for clean CRM migration.
Use it to make the next step clearer
Migration quality is decided before the first import. This mapping framework helps teams document source fields, target fields, transformations, ownership, exceptions, and testing so the cutover is controlled instead of improvised.
Map the field, not just the label
A source field called “Status” may mean something very different from a target field with the same name. Capture the business meaning, data type, allowed values, and destination behavior for every field that matters.
- Record source object and field alongside the target object and field.
- Define transformation rules for formats, values, dates, and currencies.
- Mark fields as migrate, archive, calculate, or intentionally exclude.
Plan associations and history
Contacts, companies, deals, tickets, activities, and owners are connected. A field map that ignores relationships can produce technically successful imports with broken context.
- Document the matching key for each object.
- Map associations, ownership, lifecycle, and activity history explicitly.
- Decide which history is required for operations and which can be archived.
Test, reconcile, and cut over
Use a controlled test batch to validate the mapping before moving the full dataset. Reconcile counts and sample records at every step, then keep a cutover log that the team can refer to after launch.
- Run a sample import and compare source-to-target values.
- Reconcile record counts, associations, owners, and critical reports.
- Document rollback, freeze windows, and post-cutover verification steps.
Resource checklist
Use these checkpoints to turn the template into a working process.
Key takeaway: A mapping sheet turns migration from a technical upload into a shared agreement about what the new CRM should mean.