Case Study: How a Financial Services Organization Operationalized DQM to Support Data Governance


“How can clients trust us with their money when we can’t even get their name right?” With statements like this, the business stakeholders at our client, a major financial services organization, expressed their frustration with the lack of consistency, accuracy, and completeness of their data.

Informal processes, ad-hoc procedures and lack of conformance to business rules were crippling the efficiency of their data management, causing many quality issues and reducing the users’ trust in the data. These data challenges highlighted the need for improved Data Quality Management across the enterprise.


Our team stepped in to assess data quality using a robust data profiling platform. We conducted interviews with business and technology stakeholders to identify and define Data Quality Management operational needs.

Based on our understanding of our client’s requirements, we proposed relevant Data Quality rules for critical client data. We also developed recommendations to operationalize the Data Quality Management components of our client’s Data Governance framework.


At the end of the project, our client had an operational data quality service that included:

  • A comprehensive set of operational Data Quality rules
  • Alignment with their Data Governance program
  • Data profiling and monitoring service
  • Data Quality Reporting and trend analysis using a web-based dashboard.

These operational Data Quality Management capabilities allowed our client to continuously measure, control, monitor, and improve the quality of their data over time.

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