Building a Strong Data Governance Program – Part 3 – Metrics & KPI’s

Now you have started building a collaborative data governance program and took steps to get everyone involved, it’s time to start thinking about how you can measure the success of your Data Governance Program.

Establishing Data Governance Key Performance Indicators (KPIs) will help you assess the effectiveness of your data governance efforts. To make them efficient, choose specific metrics that align with your organization's data governance objectives and the aspects of data usage that are most important to your business.

The below are examples and might or might not be applicable to your data governance program and the accompanying visuals are meant as examples only. Start with a small set of metrics that track your most important goals or the highest risk areas or quality issues.

After getting started, regularly review and adjust these KPIs to ensure they remain relevant and provide meaningful insights into your Data Governance Program's success and impact. Also, once you have established a set of metrics, add to them as your program matures, and you learn more about what insights could help other data stewards in your organization.

Define Clear Objectives and KPIs

Once you’re ready to start defining the specific objectives for your Data Governance Program, ask yourself what are you trying to achieve? Common objectives may include improving data quality, decreasing data risk, and enabling better decision-making. Then, establish Key Performance Indicators (KPIs) that align with these objectives.

So let’s take a look at different categories of data governance metrics for inspiration on how you can measure success through a combination of qualitative and quantitative metrics.

Data Governance Metrics & KPI's?

Qualitative Metrics?

1. Data Quality KPIs:

Ensuring high data quality is a cornerstone of your Data Governance Program. Focusing on metrics, such as accuracy rate, completeness, consistency, and recency of your data can help you assess how trustworthy and useful the data is in decision-making, and where you have gaps that need to be closed through data quality improvements. By tracking and improving these factors, you can lay a foundation for reliable and actionable data.

Here are examples of data quality metrics that could help you start improving your data quality:

1. Data Accuracy Rate: The percentage of data records that are error-free or accurate.
2. Data Completeness: The percentage of required data fields that are filled out.
3. Data Consistency: The degree to which data values are uniform and consistent across your data set(s).
4. Data Timeliness / velocity: The percentage of data that is updated within specified timeframes as well as the ability to identify data that has become stale and needs to be updated.

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1. Data Catalog, Data Dictionaries and Metadata KPIs:

A well-organized data catalog and comprehensive metadata management can be essential tools for efficient data management. There are now many data catalog and data dictionary offerings available that meet the diverse needs and scopes of data governance programs and organizations. Many of these tools integrate with a variety of existing data sources and can give you a head start in building a data governance KPI dashboard.

Once implemented, you should closely monitor the adoption and usage of your data catalog and data dictionary, because the more the data is used, rated and accessed, the more efficient your data can drive solid decisions across the organization.

Starting with the following metrics can help you track how data is used across your organization, and what data is valuable for end-users: ??

1. Number of Registered Data Assets: The quantity of data assets cataloged and how data assets volumes are changing over time.
2. User Engagement: The level of interaction with the data catalog and metadata, and how engagement might change over time.
3. User Ratings: How valuable users find the data, and how trustworthy or accurate they think it is.
4. Metadata Completeness: The percentage of metadata fields completed and accessible for each data asset, and where work might be needed to improve.

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Engagement Metrics

1. Data Usage and Access KPIs:

Monitoring data usage and access is crucial for understanding how effectively data is being leveraged within your organization. Measuring data usage growth, the number of data access or reporting requests, and access approval time can provide insights into the accessibility and relevance of your data assets to end-users.

Showing an increase in the utilization of high-quality data, and faster access for stakeholders can increase efficiencies and faster decision-making across all levels of your organization. The below metrics can help your track this progress:

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1. Data Usage Growth: The increase in the number of data queries run or data access requests.
2. Data Access Approval Time: The time it takes to grant data access requests.
3. Data Access Compliance: The percentage of data accesses that comply with security and privacy policies.

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1. Data Stewardship KPIs:

The role of data stewards is pivotal in ensuring the day-to-day management of your data assets. Therefore you should track data steward productivity, training completion, and adherence to data governance policies to assess the overall effectiveness of stewardship within your organization?

Metrics to track data stewardship success could include the completion of data quality tasks, the number of data stewardship training sessions, and the accuracy of their work, including:

1. Data Steward Productivity: The number of data quality tasks completed by data stewards.
2. Data Steward Training Completion: The percentage of data stewards who have completed required training.

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1. Data Literacy & Training KPIs:

But it's not just data stewards that need to be considered. Improving data literacy across the organization for ALL data users is an ongoing process. Assess data literacy through scores from surveys/assessments, and track training attendance to ensure employees are equipped with the knowledge to leverage your data effectively.

Measure improvements in data literacy across the organization through metrics like these:

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1. Data Literacy Assessment Scores: Scores from assessments testing employees' data knowledge.
2. Data Training Attendance: The number of employees attending and completing data literacy training.

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Financial Metrics

1. Cost Savings and ROI KPIs:

Demonstrating the tangible benefits of your Data Governance Program is crucial for securing ongoing support. Measure cost savings resulting from improved data usage and calculate the return on investment to showcase the program's financial impact on your organization.

For example, calculate the reduction in data-related incidents or the elimination of redundant data management processes by measuring the following:

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1. Cost Savings: The amount of money saved through data governance improvements (e.g., reduced data incidents, optimized data storage, better forecasting).
2. Return on Investment (ROI): The return on investment resulting from data governance initiatives, e.g. more reliable lead scoring and quicker billing processes.

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1. Data Governance Maturity KPIs:

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As your Data Governance Program matures, it's essential to measure its overall performance. Use your maturity model’s capability areas to assess the program's maturity level and track enhancements over time to show ongoing progress.

The use of a matrix-type is common when scoring framework capabilities and can help identify which pillars in your program need improvement and which have matured nicely through KPI's like:

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1. Maturity Level: Using a maturity model, track the level of maturity of the data governance program over time.
2. Capability Enhancement: Measure the growth in data governance capabilities and practices and compare to previous periods to track progress.

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User Experience Metrics

1. User Satisfaction KPIs & Surveys:

Ultimately, the success of your Data Governance Program is reflected in user satisfaction. Collect feedback across your data users through satisfaction surveys and track user-reported issues to identify where to make targeted improvements.

Ask about their satisfaction with data quality, accessibility, and the overall Data Governance Program. High satisfaction scores can indicate success, and some data catalog or data lineage tools now offer ratings as a default feature to track metrics like the below:

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1. User Satisfaction Survey Scores: Scores from surveys measuring satisfaction with data quality and accessibility.
2. Data Usability: Rating different data sets on how accurate and useable the data set is.
3. User Feedback: The number of user-reported issues or suggestions related to data governance.

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Compliance Metrics

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1. Data Incident and Issue Resolution KPIs:

Responding to and resolving data incidents is integral to maintaining data integrity. Metrics like incident rate, issue resolution time, and incident reduction can gauge the effectiveness of your response mechanisms and highlight areas for improvement.

Track the time it takes to resolve data-related issues, such as data quality problems or access requests. Faster resolution times indicate efficient data governance processes. Consider tracking these metrics:

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1. Data Incident Rate: The number of data-related incidents (e.g., breaches, errors) over a period of time.
2. Issue Resolution Time: The average time taken to resolve data-related issues in your dev ops ticketing system.
3. Incident Reduction: The percentage reduction in data incidents over time as you implement better incident controls, like employee training or security programs

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1. Compliance and Risk KPI’s:

As data regulations become more stringent, organizations must prioritize privacy, security & compliance. You can use metrics like compliance scores and the number of audit findings related to data governance to provide a comprehensive view of your organization's adherence to data-related regulations.

This may include tracking the number of data breaches or incidents, compliance scores, and audits as outlined below:

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1. Compliance Metrics: A score measuring the organization's compliance with data-related regulations, e.g. the percentage of employees having completed the mandatory security training
2. Audit Findings: The number of data governance-related audit findings or issues, e.g. the number of incidents reported or security scan failures.

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Benchmarking

Once you have established your KPIs, define their benchmarks, either by creating a baseline from history, or by referencing industry or standard benchmarks. As your data governance program matures, start comparing your Data Governance Program's performance to the benchmarks you set to determine how you stack up against peers and how your data strategy performs.

Executive Dashboards

Don’t’ forget to share your metrics with leadership to prove the value your Data Governance Program. Create executive dashboards that provide a high-level view of key metrics to keep senior leadership, or your board, informed about the program's progress but also point out gaps or risks that might justify funding for program enhancements.

Too often, Data Governance is seen as a cost center or blocker to innovation. Sharing metrics that track the positive impact can be critical in driving support for maturing your program. Highlighting operational efficiencies, better data use or reduced risk and cost can increase buy-in and support from all data-users and leaders at your organization.

Continuous Improvement

But don't just stop here. Ensure that your Data Governance Program includes mechanisms and measurements for continuous improvement. Periodically review and update your KPIs and metrics to reflect changing business needs and priorities.

Measuring the success of a collaborative Data Governance Program is an ongoing process.

Conclusion

To continuously mature your Data Governance Program, it is imperative you strategically implement and monitor relevant KPIs. Regular assessments using these metrics will not only highlight areas for improvement but also provide you with a roadmap for achieving higher levels of data governance maturity. By fostering a data-centric culture and leveraging these KPIs, your organizations can unlock the full potential of its data assets while ensuring compliance, accuracy, and user satisfaction.

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