Which approach best validates data mappings before go-live?

• A. Rely on automated field name matching only.
• B. Confirm results only after go-live.
• C. Compare counts at the table level only.
• D. Run representative migrations, perform record-level checks, reconcile totals, and validate mappings.

Answer: (D)

Validating data mappings before go-live requires end-to-end checks that verify the mapping logic works as intended across the actual data, not just surface-level cues. Running representative migrations simulates a real load so you can see how data moves from source to target in practice. Record-level checks probe each individual row to confirm values land in the correct destination fields and that transformations (such as date formats, currency, or code mappings) are applied accurately. Reconciliating totals ensures you haven’t lost or duplicated rows and that row counts align across key tables. Together, these steps validate both the data content and the mapping rules, catching issues like incorrect field associations, transformation errors, or data quality problems before the system goes live. Relying solely on automated field name matching can miss semantic gaps, waiting until after go-live is too risky, and checking only table counts may hide record-level mismatches.