Data Migration Checklist: Mapping, Validation and Cutover

Construir un inventario de origen.

Enumere los conjuntos de datos que se están moviendo y las personas que los entienden. Identifique recuentos de registros, rangos de fechas, relaciones y valores inusuales. Una exportación de muestra puede revelar problemas de formato, pero no establece que el conjunto de datos completo sea coherente. Planifique una revisión más amplia antes de comprometerse con un cronograma de migración.

Acuerde cómo se traducirán los valores.

Documente la asignación de los campos de origen a los campos de destino, incluidos los valores vacíos y los formatos históricos. Separe una decisión empresarial, como fusionar clientes duplicados, de una conversión técnica. Mantenga los datos originales disponibles para comparar y registrar quién aprueba cada regla de transformación.

Conciliar antes de la entrega.

Defina comparaciones que reflejen los registros comerciales: recuentos, totales seleccionados y transacciones representativas vinculadas. Planifique cómo se registran y corrigen las excepciones. Acuerde quién decide si el resultado es aceptable y documente el enfoque de recuperación antes de cambiar el sistema en el que confía la gente.

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.

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.

• AWS DMS: data validation