Data Observability
AI based anomaly detection at scale.
Leverage AI to automatically detect, analyze, and report data anomalies to:
- – Minimize Downtime
- – Improve Customer Experience
- – Ensure Business Continuity
For a more in-depth explanation, refer to Data Observability Concepts.
HOW IT WORKS?
KEY BENEFITS
 | Improve Productivity with AI |  | iceDQ learns the data patterns and automatically creates metrics to detect and notify data anomalies. This enables data certification at enterprise scale with minimal human effort. |
 | Support Complex Use cases |  | The data observability platform supports both AI and rules-based methodologies to uncover complex data issues. |
 | Powerful Triage Capabilities |  | Unlike simpler data observability platforms limited to working at the aggregate level, iceDQ offers pre-built capabilities to identify exceptions at both the record and column level. |
 | Built for Data Governance |  | Provides feedback to data stewards and data owners with quality scores for effective data governance. |
 | Integrated with Production Monitoring |  | Eliminate silos with iceDQ's integrated data testing, monitoring, and observability platform. This enables quick diagnosis and remediation of data issues before they impact business processes or user experience. |
FEATURES
- AUTOMETRICSThe system offers built-in data profiling and automatic metric generation. This enables rapid scaling of observability.
- SEGMENTED METRICSObservations of large tables can be grouped into different categories, such as department or source, for easier reporting.
- MACRO & MICRO METRICSWhile macro-observability focuses on anomaly detection at the aggregate level, iceDQ offers additional features for micro-level data observation.
- DATA FRESHNESSCheck the freshness of data and file arrival or delivery against historic patterns or SLAs.
- DATA VOLUMECompare data volumes, counts, and sums against historical patterns or user-defined rules to identify anomalies.
