Data Observability platforms monitor data pipelines and datasets for behavioral anomalies using unsupervised ML models that learn normal freshness, volume, schema, and distribution patterns and alert when those patterns deviate. Core capabilities include automatic baseline learning without rule configuration, freshness monitoring (is data arriving on time?), volume and row-count anomaly detection, schema-drift detection, field-level distribution monitoring, lineage-based impact analysis (what downstream assets are affected by this break?), incident triage workflows, and integration with alerting and on-call tooling. The category targets the production-monitoring side of data reliability. Leading vendors include Monte Carlo, Metaplane, Bigeye, Acceldata, Anomalo, Sifflet, and Databand (IBM).