Overview
The executive dashboard shows revenue dropped 30% yesterday. The CEO calls an emergency meeting. Two hours later, someone discovers: revenue did not drop — the ETL pipeline failed at 3 AM and yesterday's data was never loaded. The dashboard displayed partial data as if it were complete. Every downstream decision made that morning was based on broken data that looked normal.
The Data Quality Monitoring Dashboard watches the data itself — confirming that pipelines ran on time, tables have expected row counts, schemas have not changed unexpectedly, and values fall within acceptable ranges. When data is bad, the quality dashboard catches it before any business dashboard displays it.
What you get: - Pipeline execution monitoring (did it run? did it complete?) - Data freshness tracking (when was each table last updated?) - Volume anomaly detection (row count deviations from expected) - Schema change detection (unexpected column additions, removals, type changes) - Value-level quality checks (nulls, duplicates, range violations) - Data quality SLA tracking
Built for: data teams whose downstream consumers (executives, analysts, product teams) make decisions from dashboards that silently display stale or incomplete data — where a data quality layer would catch problems before they reach decision-makers.