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.

USE CASES

HOW IT WORKS?

KEY BENEFITS

Improve Productivity with AI - iceDQ Improve Productivity with AI Triple Arrow - iceDQ 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 - iceDQ Support Complex Use cases Triple Arrow - iceDQ The data observability platform supports both AI and rules-based methodologies to uncover complex data issues.
Powerful Triage Capabilities  - iceDQ Powerful Triage Capabilities Triple Arrow - iceDQ 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 - iceDQ - iceDQ Built for Data Governance Triple Arrow - iceDQ Provides feedback to data stewards and data owners with quality scores for effective data governance.
Integrated with Production Monitoring - iceDQ   - iceDQ Integrated with Production Monitoring Triple Arrow - iceDQ 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

  • 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. This enables identification of trends and exceptions at both the record and column level.
  • 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.
Autometrics-iceDQ
Segment Metrics - iceDQ
Macro and Micro Metrics - iceDQ
Data Freshness - iceDQ
Data Valume - iceDQ
iceDQ Tool Page CTA

FAQs

No. We have our proprietary High-performance in-memory engine and a scalable big-data engine. This helps lower your database load and costs.

Companies can opt for the SaaS option on AWS or the self-managed on-premises options.

No. The customer controls data access for both the SaaS and on-premises option.

It’s a consumption-based license per user per server or enterprise.