Data observability is the capability to understand the health and state of data systems by measuring signals and metrics across pipelines, enabling proactive detection and resolution of data quality issues before they impact downstream consumers. Built on five core pillars—freshness, volume, schema, distribution, and lineage—it extends traditional monitoring by providing context-aware insights into why data issues occur, not just what went wrong. In 2026, as organizations rely increasingly on AI-driven decision systems and real-time analytics, data observability has shifted from reactive incident response to autonomous trust enforcement, with automated remediation now preventing 80% of quality incidents before they reach production.