Which data quality validation technique is recommended during ERP data migration?

• A. Only automated ETL tests.
• B. Manual reviews without data checks.
• C. Reconciliations, sample data checks, row counts, and automated ETL tests.
• D. Data quality metrics dashboards without checks.

Answer: (C)

Validating data during ERP migration relies on combining several techniques that confirm data moves correctly and meets transformation rules. Reconciliations compare totals, aggregates, and row counts between the source and target to ensure nothing is lost or duplicated. Sample data checks look at representative records to verify that values and formats remain correct after migration and that transformations were applied properly. Row counts act as a quick sanity check to catch missing or extra records across tables. Automated ETL tests encode the migration logic and run checks across large datasets, catching regressions and ensuring consistency in how data is transformed.

Using these methods together provides a reliable, auditable validation approach that protects data integrity through the entire migration. Relying solely on automated ETL tests misses alignment between source and target data; manual reviews without data-specific checks lack objective evidence; and dashboards without actual checks don’t verify that the data itself migrated correctly. Therefore, the recommended practice is to perform reconciliations, sample data checks, row counts, and automated ETL tests.