What Is Data Quality Management? Key Insights for Data Engineers

Data Quality Management (DQM) is a framework of practices including profiling, cleansing, validation, and monitoring that ensures organizational data remains accurate, reliable, and compliant. Poor data quality costs organizations an average of $12.9–15M annually, and Gartner projects 60% of AI projects will fail by 2026 due to insufficient data readiness. The nine standard dimensions of data quality (accuracy, completeness, consistency, timeliness, etc.) are outlined, along with common challenges like data silos, human error, and lack of standardized processes. The post also promotes Decube's platform as a solution offering automated metadata crawling, data lineage visualization, and ML-powered quality monitoring.