Data Observability

AI based anomaly detection at scale.

Leverage AI to automatically detect, analyze, and report data anomalies to:

For a more in-depth explanation, refer to Data Observability Concepts.

USE CASES

	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.

AUTOMETRICS

The system offers built-in data profiling and automatic metric generation. This enables rapid scaling of observability.
SEGMENTED METRICS

Observations of large tables can be grouped into different categories, such as department or source, for easier reporting.
MACRO & MICRO METRICS

While macro-observability focuses on anomaly detection at the aggregate level, iceDQ offers additional features for micro-level data observation.
DATA FRESHNESS

Check the freshness of data and file arrival or delivery against historic patterns or SLAs.
DATA VOLUME

Compare data volumes, counts, and sums against historical patterns or user-defined rules to identify anomalies.