Migrating from a legacy database to a modern data warehouse offers benefits such as scalable storage and faster queries, but poses significant risks if data validation isn't prioritized. Poor data quality, including schema mismatches and missing records, can undermine the migration, resulting in broken dashboards, erroneous financial reports, and operational disruptions. Automated data validation is crucial for ensuring data integrity, detecting schema drift, and preventing errors during and after migration. Traditional validation methods fall short due to their inability to handle the scale and complexity of modern data transformations. Datafold's Data Migration Agent (DMA) enhances this process by automating code conversion and data validation, ensuring data accuracy across systems. Through row-level and column-wise validation, DMA identifies and corrects discrepancies, while ongoing anomaly detection and statistical profiling maintain data quality post-migration. This approach not only facilitates successful migration but also sustains long-term data integrity and operational efficiency.