Understanding Data Governance Metrics

Data governance metrics are essential for measuring the effectiveness of an organization’s data governance program. These metrics provide insights into the quality, security, and usability of data, enabling organizations to make data-driven decisions. Understanding data governance metrics is crucial for identifying areas for improvement, tracking progress, and ensuring regulatory compliance. By monitoring data governance metrics, organizations can evaluate the success of their data governance initiatives and make adjustments to optimize their data governance strategies.

Data Governance Metrics Examples

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 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.

Data Governance Program Metrics

A successful data governance program relies on a set of metrics that measure its effectiveness. These metrics include data quality metrics, data security metrics, data compliance metrics, and data usage metrics. Data quality metrics, such as data accuracy, completeness, and consistency, ensure that data is reliable and trustworthy. Data security metrics, such as security breach incidents and access control effectiveness, ensure that data is protected from unauthorized access. Data compliance metrics, such as regulatory compliance rate and policy adherence rate, ensure that data governance practices align with regulatory standards and internal policies. Data usage metrics, such as data utilization rate and data access patterns, ensure that data is being used effectively to support business decisions.

Importance of Measuring Data Quality

Ensuring high data quality is central to the success of any data governance initiatives and forms the backbone of effective decision-making. Measuring data quality is not overly complex but it is vital for establishing trust in the data being used. Commonly used data quality metrics include:

  • Accuracy Rate: Ensures that data entries correctly represent real-world values, thereby guaranteeing reliable data for strategic decisions.
  • Completeness: Tracks the percentage of required data fields populated, reducing the risk of incomplete data sets impacting business outcomes.
  • Consistency Rate: Measures alignment across data sources, ensuring consistent data usage in analytics and reporting. Data consistency is crucial for ensuring that data remains uniform across different datasets and systems, which is essential for accurate analytics and reporting.

These metrics validate the integrity of critical data assets, enabling organizations to identify non-conforming data, improve data quality scores, and strengthen data governance processes. Organizations with a strong data governance program can transform high-quality data into valuable insights, reducing the likelihood of errors that may lead to data incidents or regulatory penalties.

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.
  • 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).

Data Governance Maturity and Adoption

Data governance maturity and adoption are critical factors in ensuring the success of a data governance program. Data governance maturity refers to the level of sophistication and effectiveness of an organization’s data governance practices. Data governance adoption refers to the extent to which data governance practices are embedded in an organization’s culture and processes. By measuring data governance maturity and adoption, organizations can identify areas for improvement and develop strategies to enhance their data governance practices. This includes establishing clear data governance policies, providing data governance training and awareness, and implementing data governance processes and procedures.

Data Usage and Adoption Metrics

Promoting a data-driven culture requires consistent monitoring of data usage and the adoption of data governance practices. Organizations can use the following approaches to evaluate how effectively their data is utilized:

  • Data Usage Growth: Analyzing trends in data utilization across departments helps assess the effectiveness of ongoing data governance initiatives. Steady growth indicates successful engagement with critical data assets.
  • Data Access Patterns: Reviewing data access requests and usage metrics reveals which data sets are most frequently used, providing insights into operational priorities and the adoption of key metrics.
  • Data Literacy Programs: Implementing comprehensive data governance training ensures that employees across all levels can handle data effectively, improving overall data accuracy and consistency.

By regularly measuring these factors, organizations can enhance their data governance programs and develop strategies to maximize data utilization. Such efforts also reinforce regulatory compliance, improve metadata completeness, and foster a strong data culture.

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, including within an enterprise data management initiative. Measure to look for include:

  • 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.

Data Security Metrics

Data security is a cornerstone of a successful data governance program, ensuring sensitive data is safeguarded against unauthorized access and breaches. Effective metrics for data security include the following:

  1. Rate of Data Breaches: Regularly tracking the frequency of data breach incidents can help measure the robustness of data security measures. A lower rate often reflects strong compliance metrics and adherence to regulatory requirements.
  2. Access Control Effectiveness: This key performance indicator assesses the efficiency of data access protocols, helping organizations identify and mitigate potential vulnerabilities in their data governance practices.
  3. Data Incident Resolution Time: Measuring how quickly data incidents are resolved demonstrates the organization’s ability to maintain data integrity and protect critical data assets.

Monitoring these security metrics not only aids in managing data effectively but also supports continuous improvement in data governance strategies. Engaging a dedicated team to track and respond to these metrics ensures swift action against potential threats, reinforcing data compliance and enhancing the organization’s overall data governance maturity.

Interpreting and Acting on Data Governance Metrics

Interpreting and acting on data governance metrics is crucial for ensuring the effectiveness of a data governance program. By analyzing data governance metrics, organizations can identify areas for improvement, track progress, and make data-driven decisions. This includes identifying trends and patterns in data quality, security, and usage, and developing strategies to address any issues or concerns. Organizations should also establish a response team to respond to insights derived from data governance metrics, and regularly review and audit their data governance practices to ensure they remain effective.

Tools and Technologies for Data Governance

There are various tools and technologies available to support data governance initiatives. These include data quality tools, data security tools, data governance platforms, and data analytics tools. Data quality tools, such as data validation and data cleansing tools, ensure that the data is accurate and reliable. Data security tools, such as access control and encryption tools, ensure that data is protected from unauthorized access. Data governance platforms, such as data catalogs and data dictionaries, provide a centralized repository for data governance information. Data analytics tools, such as data visualization and data mining tools, enable organizations to analyze and interpret data governance metrics. By leveraging these tools and technologies, organizations can enhance their data governance practices and ensure the effectiveness of their data governance programs.

Conclusion

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 upon 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 the success
  • Continual data management practice and adherence to best practices are essential for success in data governance

As Lord Kelvin once famously said, “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.