THE STARTING POINT
Prepare a data migration with source checks, field mapping, validation, reconciliation and cutover responsibilities. Work through a practical planning example.
Build a source inventory.
List the datasets being moved and the people who understand them. Identify record counts, date ranges, relationships and unusual values. A sample export can reveal formatting problems, but it does not establish that the full dataset is consistent. Plan a wider review before committing to a migration schedule.
Agree how values will translate.
Document the mapping from source fields to destination fields, including empty values and historical formats. Separate a business decision, such as merging duplicate customers, from a technical conversion. Keep the original data available for comparison and record who approves each transformation rule.
Reconcile before the handover.
Define comparisons that reflect the business records: counts, selected totals and representative linked transactions. Plan how exceptions are recorded and corrected. Agree who decides whether the result is acceptable, and document the recovery approach before changing the system people rely on.
Rehearse extraction, transformation and reconciliation
Create an inventory of source tables or files and assign a business owner to their meaning. Inspect representative records, including missing values, duplicates, historic formats and relationships. Write transformation rules explicitly so a reviewer can explain why the destination value differs from the source.
Run a rehearsal in a suitable non-production environment. Record rejected records and validate relationships, important totals and selected record details. Agree how changes made during the transition are captured. Time the rehearsal to inform the cutover discussion, then review restoration and recovery options. Obtain approval for unresolved exceptions before scheduling the production move, rather than treating them as cleanup that somebody will handle later.
A migration mapping and cutover worksheet
Maintain the worksheet alongside the migration scripts. Use a separate decision log where different departments must agree a mapping.
- Source and destination: dataset, field, type, identifier and responsible business owner.
- Transformation: format conversion, default, lookup or rule, with a concrete example.
- Exception policy: duplicates, missing mandatory values, invalid references and rejection ownership.
- Validation: record comparisons, relationship checks, totals and agreed acceptance limits.
- Cutover: write restrictions, final extract, change capture and communication responsibilities.
- Recovery: decision authority, restoration procedure, verification and treatment of new writes.
An import count is not a complete validation
Equal row counts do not establish that values, relationships or business totals are correct. Include checks reflecting how the destination will be used. A migration can load every row and still associate a transaction with the wrong account.
Do not assume rollback is simply restoring yesterday’s backup. If the destination has accepted new work, a recovery decision must address those writes. Keep the source available as agreed, restrict sensitive data appropriately and rehearse the recovery approach before the production cutover.
Can we clean all data during the move?
Only include cleanup rules that have a clear owner and agreed interpretation. Unresolved business decisions can make a migration unpredictable. Separate essential transformations from later quality improvements, and preserve a record of changes so reviewers can reconcile the migrated result.
ILLUSTRATIVE PLANNING EXAMPLE
Example: migrating product records
Export a representative sample of product records from the old system. Check duplicate identifiers, missing units and categories that do not exist in the destination. Write mapping rules and test them in a non-production environment. Include discontinued products and unusual prices in the review rather than testing only clean records.
Agree reconciliation totals, rejected-record handling and who approves the cutover. Document the recovery decision if validation fails; a successful import alone is not acceptance.
TAKE THE NEXT STEP
Your discovery checklist
- Inventory source records, relationships and date ranges.
- Approve field mappings and transformation rules.
- Agree counts, totals and exception checks for reconciliation.
FURTHER READING
Official resources to explore
Explore the related official guidance below. The templates and examples are practical starting points to adapt to your own project.