Data quality monitoring: what to track & how

This post details data quality monitoring techniques, covering both structural (accuracy, completeness, consistency, timeliness, validity, uniqueness) and semantic (semantic clarity, source trustworthiness, contextual completeness) dimensions. It outlines traditional techniques like data profiling, anomaly detection, data lineage tracking, schema monitoring, and CI/CD integration. It also introduces AI-powered monitoring, emphasizing automated baseline learning and its application to both traditional and semantic dimensions. The post highlights the importance of semantic layers for AI-driven semantic appropriateness and advocates for integrating monitoring into the analytics workflow.