60% less manual effort in data quality monitoring

A leading North American bank needed to improve the efficiency and reliability of monitoring monthly alternative data sourced from an external vendor.

The process was heavily manual, time consuming, and prone to errors, with each analyst spending 4-5 days per month validating data.

The absence of an automated monitoring framework meant:

• No standardized thresholds for identifying anomalies across completeness, consistency, and accuracy
• Limited visibility into data quality issues and vendor performance
• Frequent bottlenecks that delayed downstream reporting and restricted focus on higher-value analytics

Crisil implemented a fully automated and intelligent data quality monitoring ecosystem designed to proactively detect issues and streamline workflows:

• Intelligent Anomaly Detection: Defined custom data quality parameters with historical threshold-based alerts to identify anomalies, ingestion issues, and unusual data shifts in real time
• Accelerated Remediation: Developed a dynamic data quality dashboard to systematically flag threshold breaches and highlight key issues, enabling timely detection and faster remediation
• Automated Governance and Accountability: Automated the generation and distribution of data quality reports while identifying recurring vendor issues to support proactive escalation and greater accountability