Data Quality Monitoring Explained – You're Doing It Wrong

Data quality monitoring is the process of automatically monitoring the health of data pipelines and the data that runs through them. The article argues that narrowly monitoring only “golden tables” misses upstream anomalies, and advocates combining broad metadata monitoring (freshness, volume, schema across all production tables) with deep ML-based field-level monitoring on critical assets, plus end-to-end integration of logs from SQL, Airflow, dbt, and BI tools for root cause context.