Data Quality and Trust Monitor
Establish Continuous Data Trust Across Enterprise Data Lake - Autonomously profile datasets detect anomalies and monitor health converting profiling results into actionable insights. Achieve 40% reduction in data incidents through proactive quality monitoring.
Comprehensive data profiling and quality monitoring framework establishing continuous data trust and enabling proactive issue identification across data lake assets
Autonomous Dataset Profiling - Computes statistical distributions patterns cardinality missing rates and data type signatures automatically without manual configuration or model training
Rule Inference and Learning - Derives quality rules from historical data patterns and usage analytics enabling dynamic quality assessment that evolves with data characteristics
Continuous Health Monitoring - Tracks freshness completeness accuracy consistency and availability metrics across all datasets identifying degradation trends
Anomaly Detection and Reasoning - Identifies deviations from expected patterns and explains why metrics changed not just that they did providing root cause visibility
Issue Orchestration and SLA Tracking - Automatically assigns ownership of detected issues to data owners tracks resolution SLAs and verifies corrective actions
Trust Scoring System - Produces dataset-level quality reliability and fitness-for-purpose scores enabling consumers to assess data reliability
Consumer Feedback Loop - Integrates downstream consumer feedback and usage patterns improving quality rules and anomaly detection accuracy