Data Monitoring Case Study of an MDM System – Investment Bank

This case study demonstrates how iceDQ’s data testing and monitoring solution, helped a major investment bank,
significantly improve the reliability of their Master Data Management (MDM) system for financial instruments and
market data.

Key Highlights:

1. Challenge: Reducing operational risk inherent in data quality issues affecting trading decisions.
2. Scope: MDM system consolidating financial instrument and market data from various internal and external
   sources.
3. Solution: Implementation of iceDQ Data Testing and Monitoring Framework.
4. Focus: Proactive monitoring, notification, and prevention of data quality problems.

Essential Metrics:

1. Issue Prevention: 80% to 90% of production breaks found and fixed before affecting downstream trading
   desks (compared to virtually none before implementation).
2. Data Classification: Critical data elements categorized as “Blocker,” “Critical,” and “Warning” based on
   business impact.
3. Comprehensive Monitoring: Checks implemented for data comparisons, reconciliations, and trend analysis.

Key Benefits:

1. Reduced Impact: Minimized the effect of bad data quality on trading desks.
2. Proactive Approach: Provided warnings and notifications before users discovered problems.
3. Visibility: Offered clear insights to both IT and business stakeholders about data issues.
4. Process Control: Established checks and controls on data movement/ETL processes.
5. Trust Building: Improved reliability and trust between IT and business units.

Implementation Highlights:

1. Data Governance: Categorized data into subject areas with assigned data stewards.
2. Rule Creation: Business SMEs provided logical rules, while IT created physical rules for data quality.
3. Process Integration: iceDQ’s rules embedded in data movement processes or scheduled to run at
   predetermined times.
4. Automated Actions: Capability to stop processes or notify concerned SMEs in case of errors.
5. Dashboard: Provided visibility into the health of the MDM environment, enabling quick decisions and
   actions.

This case study illustrates how implementing iceDQ can significantly enhance the reliability of financial data
systems, reduce operational risks, and improve decision-making processes in investment banking.

Download the full case study to learn more about:

1. The specific data testing challenges faced in managing a complex financial instrument and market data hub.
2. How iceDQ addressed these challenges in real-time.
3. The quantifiable benefits achieved by the client through iceDQ implementation.
4. The critical role of proactive data monitoring in reducing operational risk for investment banks.
5. How iceDQ can help you enhance data reliability in your financial data systems.