What practice supports data integrity during data migration or upgrade?

• A. Skipping validation checks
• B. Relying on user reports
• C. Validation checks, data mapping audits, test migrations, rollback plans
• D. Deleting source data after migration

Answer: (C)

During data migration or upgrade, protecting data integrity comes from a combination of validation checks, data mapping audits, test migrations, and a rollback plan. Validation checks verify that the migrated data matches the source in accuracy, format, and business rules, catching issues like missing records, type mismatches, or data truncation. Data mapping audits ensure each source field is correctly translated to the right target field, preserving meaning and constraints across systems. Running test migrations in a safe environment helps surface problems before going live, allowing fixes without impacting users. A rollback plan provides a clear path to revert if problems are found, preventing partial migrations from leaving the system inconsistent.

Skipping validation checks leaves errors hidden; relying on user reports is unreliable for preventing issues; deleting source data after migration eliminates an important safeguard and makes verification impossible.