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Performance Metrics for Data Governance and Data Stewardship

05 April, 2017 | Anne Marie Smith, Ph.D. | Data Governance

Metrics and the measurements they create are essential to the success of every data governance program and every data stewardship effort.

Introduction

Organizations that implement any new initiative must be able to measure its success so the program’s leadership can deliver progress reports to stakeholders and sponsors.  Communicating success based on measured facts enables the program to demonstrate its effectiveness, to identify areas where improvement is needed and to justify the continuing funding it receives.  Without a set of established performance metrics, any new initiative will not be able to prove its value, and data governance is no exception to this rule.  Following is a set of suggested performance metrics based on the work performed by EWSolutions consultants on numerous data governance engagements, and included in the EWSolutions’ G3 Data Governance methodology.

Data Governance Metrics Examples

Data Governance Council Metrics these metrics evaluate the performance of the Data Governance Council, which is the governing body for the Data Governance program.  Measuring its effectiveness is essential to determine whether the council is operating as it should and if the Data Governance program has appointed the right members to the council.

  • METRIC 1: Advocacy success measure.
    • Getting each Council member to recognize that their role is not a passive one.   To remain on the Council, they are expected to be “data management improvement proselytizers” – e.g., identifying sufficient, competent and performing business data stewards for their line of business, and speaking at their team meetings about the new policies concerning the management of data and information, progress in enterprise data and information management across the organization, and changes to the data governance program, etc.

 

  • METRIC 2: Meeting success measure.
    • Demonstration of commitment.  This can be accomplished by an early vote to have and enforce a policy that a Council member will be “disinvited” for lack of attendance.  Attendance and participation at no less than 75% of all scheduled and held Data Governance Council meetings is required of all DG Council members each calendar year.

 

  • METRIC 3: Each Council Member must bring a Data or Process Issue request to the Council at least every quarter.
    • Demonstration that the Council member understands what is an appropriate process and/or a data issue that warrants attention from the DG Council.  They must be willing to push skeletons in their own business areas in front of their peers for resolution.

 

  • METRIC 4: Number of Data Governance Policies Established.
    • Enterprise data governance policies serve as the basis for prying systemic data issues away from the silo-minded lines of business. In the first years, typical policies include defining the list of governed data elements; approving Unique Identifier data elements (e.g., Unique Provider, Unique Institution, Unique Member); establishing USPS Address Standardization; conforming Provider Specialty Taxonomy to CMS labels.  Assignment of resolution to the appropriate Business Data Stewardship team is an accompanying metric.

 

  • METRIC 5: Education and Training measure.
    • The Data Governance Council should demonstrate proficiency in their role as they advocate the cause of data governance in the organization.  Each member of the Data Governance Council must register and attend at least 8 hours of formal training / education in data management and data governance in every calendar year to remain as a member of the Data Governance Council.

Business Data Governance and Data Stewardship Team Metrics

Business Value Measures – these metrics attribute business value to the implementation of the data governance program, data rationalization and standardization, and improved data management discipline.  Some examples of business value would include increases of revenue and profitability, reduction of cost and improvements in productivity.

  • Within 2 years, enterprise senior management should have the ability to evaluate organizational compliance of all financial data across departmental program areas rather than by division.
  • Within 3 years, the enterprise should be able to evaluate results through cross-department data exploration.
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  • Within one year, the enterprise should be able to determine the reduction in fines / penalties incurred as a result of fewer regulatory infractions due to incorrect filings in regulated reports (improved data quality due to improved data governance and metadata management).

Accountability and Compliance Measures – these metrics evaluate and measure the level of adoption of enterprise data standards and the performance of the data governance program

  • Percentage / Number of departments where a data standard (for a specific piece of data or data element) is accepted, by subject area
  • Percentage / Number of information systems data elements that share a data standard, by subject area
  • Percentage / Number of business processes that utilize data standard, by subject area
  • Percentage / Number of production reports (outputs) that utilize data standard, by subject area
  • Percentage / Number of people that use data standard elements, by subject area
  • Percentage / Number of integrated business processes by subject area

It is important to note that 100% compliance is not expected from any of these metrics, especially at the beginning of any program.  Success depends on the following points:

  • Measurement must be accepted as an enterprise endeavor and supported by the organization, including senior leadership
  • Metrics must be proposed and agreed at the start of the program, with a select set chosen as the initial measurement points (essential measurements)
  • Reports must be released regularly, showing success and areas of improvement for each metric
  • Interaction among the Data Governance Program Office, Data Governance Council, Business Data Stewards teams and related stakeholders is essential to success
  • Continual data management practice, and adherence to best practices (e.g., DAMA-DMBOK©) is essential for success in data governance

Conclusion

As said famously by Lord Kelvin “One cannot manage what has not been measured.”  Establish metrics for the data governance program and for the data stewardship teams during development to ensure that success can be tracked and great performance maintained.

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