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Case Study

Mitigating Risks in Risk Data Reporting


A national financial institution 

The Challenge

Executive Leadership at the institution lacked confidence in the reliability of performance and risk metrics reported to regulators and the Board to demonstrate compliance with regulatory mandates and to drive strategic decisions. 

This was a project where risk data quality was the primary objective. We were required to perform a comprehensive data quality assessment and cleanse over 100,000 data points to maximize data quality and minimize negative regulatory impact of bad risk data.

What We Did 

The solution relied on the design and implementation of hundreds of data quality rules, and then using them to identify all data inconsistencies.  

  • Designed and implemented hundreds of data quality rules, and then used the rules to identify all data inconsistencies and categorize errors into multiple groups to distinguish errors of different types. 
  • Performed data profiling to determine the root cause of the deterioration of data quality. 
  • Remediated over 100,000 data points by focusing on data standardization, de-duplication, matching, and resolution of missing values. 
  • To prevent new errors from being introduced into the data 
  • Redesigned the data entry form to improve the data collection procedures, 
  • Designed additional data rules to ensure quality during data conversion and consolidation, and 
  • Designed various controls to validate incoming data transactions. 
  • To eliminate quality deterioration during data conversion and consolidation projects, implemented procedures to ensure comprehensive data profiling and data quality assessment of the source data before mapping specifications are created. Included data quality definition and acceptable quality benchmarks into the conversion specifications. 
  • To ensure sustainability of risk data quality, designed a data quality assessment program that included creating a blueprint to monitor data quality in “live” databases on an ongoing basis to observe data quality trends, identify new data problems, and check the progress of the data quality improvements. 


  • Corrected existing errors in over 100,000 risk data points, turning degraded data into useable and trustworthy data. 
  • Improved data quality and governance by 80% to ensure accuracy and timely management reporting. 
  • Improved regulatory confidence and saved the institution potential regulatory criticism and fines for data quality issues and non-compliance issues. 
  • Streamlined Board and management reporting. 

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