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Data Quality as an Enterprise-Wide Issue

- David Mayell, Data Quality, Morgan Stanley

Aligning Data Capture and Use (Back and Front End Operations) to Deliver Strategic Customer Data

- 4% of respondents very satisfied with their companies’ data integration and analysis
- 40% say their workers often make poor decisions because of inadequate data

Statistics: Economist Intelligence Unit- “Business Intelligence: Putting Information to Work”

Analyzing poor data is worse than not analyzing it at all.

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Data quality is not a new issue facing the enterprise. It is a critical factor in making informed business decisions, yet data quality is just moving up the priority list.

There has been, and continues to be, movement towards an enterprise-wide awareness of data quality issues, and steps are being taken to ensure that customer data is captured and analyzed/used to its fullest potential:

  1. Measuring data quality is the first step in understanding that data’s strengths and weaknesses.  It allows business users and IT to understand the implication of data usage on data driven projects.
  2. The technical group needs to work with the business group who understands the data and the business objective.  Then both IT and the business group determine how to build a good, quality database to store their data. It’s about integrating customer data into the enterprise data management strategy.

For IT, good data means more efficiency, less firefighting and more strategizing. For business, good data increases customer satisfaction which means more profit.

Software has to analyze more than simply data, it has to analyze relationships.  It’s a people issue, not simply a software issue.  A full understanding of business objectives is needed in order to provide incentive for the back end (data input) to be accurate and complete.

Take away points include:

  • How to plan and implement an enterprise-wide data quality strategy, unifying the front and back end of operations (business and IT)
  • Looking at data quality as a people issue, and not simply a technological issue:  Integrating the human aspect with the software to create a holistic solution
  • How does a company measure data quality?  Identifying a unique approach based on company-specific business objectives


VPs, Directors, Managers of:

  • Data Quality
  • Customer Data Managers
  • Reference Data Managers
  • Information/Relationship/
    Database Management
  • IT/Data Architects


  • CIOs
  • CTOs
  • CMOs
  • Chief Data Officers

David Mayell, Data Quality, Morgan Stanley

David Mayell is currently a Data Quality expert at Morgan Stanley, working within the Reference Data department of the firm. Since joining the firm, he has implemented enterprise wide standards for data quality management, including data profiling and mining techniques, explicit and implicit data monitoring, and an all-encompassing Data Governance Model. Mr. Mayell was the inaugural recipient of the Excellence in Reference Data Award, presented to employees who most embodied Morgan Stanley’s business principles.

Mr. Mayell has worked with various data quality consultants and vendors throughout his tenure at Morgan Stanley, and has previous data management experience in the media/entertainment field. He is currently leading projects to standardize and improve the Data Quality architecture at Morgan Stanley. Additionally, he is implementing a comprehensive operating model to promote Data Quality as an enterprise wide service.

Mr. Mayell holds a Bachelor of Arts degree in Management from the Isenberg School of Management at the University of Massachusetts-Amherst, and a Bachelor of Arts degree in Communications from the University of Massachusetts-Amherst.


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