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Identifying Business Value for Data Governance and Data Stewardship

Governance 5

Ensure that your organization can identify the actual business value data governance and data stewardship contribute to start and maintain the program


Often, data governance and data stewardship programs are cited for a lack of tangible metrics that indicate the success of the initiative.  Without identifying criteria for measuring the results of the data governance program and the activities of the data stewards and data management professionals, an organization cannot feel confident that the program is achieving its business goals or contributing quantifiable business value.  Many data governance programs are not funded fully or are cancelled after a pilot when the effort does not demonstrate detectable positive results in pre-defined criteria.

To avoid the stigma of cancellation when the program is successful but has not demonstrated that success, it is essential that every data governance and data stewardship program follow these guidelines:

Guidelines for Identifying Data Governance Business Value

1.) Create a set of business value goals for the data governance program that are approved by senior management.  This should be a short set (3-5 total), based on the business’ goals and related to how the data governance program will address them.  Example drawn from an EWSolutions client:

Business Goal Data Governance Capabilities / Contributions
Increase Revenue Improve profitability with better analytics for improved decision making
Manage Costs and Complexity Lower cost of data management and integration  through enterprise data source mapping and enterprise access to business data definitions
Manage Risk Provide better insights into fraud with improved analytics ; Improve quality of reporting to regulators and authorities through improved data processes and data management
Improve Performance Quality Improve decision making through use of trusted data; Enable process optimization with accurate data

2.) Identify the specific measurement types to be used to calculate results for the data governance program:

Business Value Measures –  ways to attribute business value to a.) implementation of the EIM and information governance programs; b.) data standardization; c.) improved data management discipline across the enterprise and in projects.  Examples of business value measures could include:

  • Increase in revenue due to ability to manage customers / members properly as a result of the management of master data according to industry standards with a defined MDM architecture and integration with all relevant applications
  • Decrease in production costs due to the reduction in the need for continued questions on the definition of data, the continued searches for analytical data and the sources of operational data of high quality, the reduction in the time to market for new applications as a result of consistent data architecture, consistent meta data management, consistent data governance.
  • Improved productivity due to the use of consistently applied data and information governance for all mission-critical data, the ability to rely on analytical data for its high quality, the ability to respond quickly to time to market decision making due to higher quality data and information from data quality improvement
  • Increased profit due to faster and more accurate decisions made with correct and more available data and information, more ability to use a wider variety of data that has been organized according to established standards
  • Acceptability and Compliance Measures – ways to directly evaluate and measure the level of adoption of: a.) enterprise data standards; b.) enterprise data management and data governance programs; c.) performance of the business data stewards and the stewardship teams. Examples of acceptability and compliance measures could include:
    • % of applications that are actively governed through the Data Governance program, master data management, meta data management, data quality management
    • % of business departments actively involved in data governance, master data management, metadata management, data quality management
    • % of applications aligned to the Enterprise Data Model (EDM)
    • Number of data attributes defined, in business and technical meta data, by entity, by subject area, and approved by the Data Governance Committee
    • Number of business rules established by functional area or subject area or other criterion, and approved by the Data Governance Committee
    • Number of subject areas modeled for the Enterprise Data Model (fully attributed) and approved by the Data Governance Committee and EDM Council
    • Number of policies written by the IG Program team and approved by company leadership
    • Number of EDM-related standards written / revised / accepted and approved by Data Governance Committee
    • % of logged data stewardship problems resolved by month, quarter, annually
    • Number of people trained as business data stewards by month, quarter, annually
    • Number of people that participate actively as business data stewards

It is essential that these metrics resonate with the business leadership, so the final measurements should be approved by the executive sponsors for the data governance program.  Many organizations focus on data quality improvements, as indicated in a Gartner study

3.) Measure the final set of metrics regularly, and report results and their meaning to all stakeholders.  Analyze trends identified by the metrics and adjust accordingly, for the program’s continuous improvement.  Communicate performance-inspired changes to demonstrate the effect the metrics have on the program.

4.) Add additional metrics as requested or as necessary, to maintain visible, demonstrated business value of the data governance program.  However, do not add measurements for their own sake.  Only measure what is considered to be important to the organization and that can be measured appropriately.


In the final analysis, just as “the unexamined life is not worth living,” the data governance program without the ability to demonstrate its business value will not prove itself worthy of being sustained.


Anne Marie Smith, Ph.D.

Anne Marie Smith, Ph.D. is an internationally recognized expert in the fields of enterprise data management, data governance, data strategy, enterprise data architecture and data warehousing. Dr. Smith is a consultant and educator with over 30 years' experience. Author of numerous articles and Fellow of the Insurance Data Management Association (FIDM), and a Fellow of the Institute for Information Management (IIM), Dr. Smith is also a well-known speaker in her areas of expertise at conferences and symposia.

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