Data governance is critical for businesses aiming to make data-driven decisions. A strong data governance program ensures data quality, accuracy, security, and usability.

Evaluating the performance of data governance strategies requires measurable metrics and key performance indicators (KPIs). Here, we will explore some of the most significant metrics and KPIs businesses can use to gauge their data governance initiatives.

What are data governance metrics and KPIs?

Data governance metrics and key performance indicators (KPIs) measure the effectiveness of an organization’s data governance program. 

They can help:

  • Prove the business benefits of data governance to key stakeholders
  • Get or maintain executive buy-in 
  • Track the progress of an ongoing data governance initiative
  • Identify areas for improvement in data governance

Data governance metrics and KPIs

The metrics and KPIs below can help measure data quality, data literacy, data ownership and accountability, and business value at your organization. 

1. Data quality score: A score or rating that represents the accuracy, consistency, timeliness, completeness, and reliability of your data. 

How to measure:

  • Completeness: Percentage of missing data in a dataset.
  • Accuracy: Match rate of a sample of data against a trusted source.
  • Timeliness: Age of the data relative to its intended update frequency.
  • Consistency: Rate of contradictions in data between sources.
  • Reliability: Percentage of data that remains stable over time.

2. Data availability percentage: The rate at which critical data assets are available for use. Keeping an eye on this metric can help ensure that data is accessible when needed, promoting business continuity and reducing operational disruptions.

How to measure:

  • Total uptime of a data source or system divided by the total time (typically measured over a month or a year).

3. Rate of data incidents: The number of data breaches, data loss, data inaccuracies incidents, and/or whatever else your organization may determine as an incident. A lower rate indicates better data security and accuracy, minimizing business risks associated with data mishandling.

How to measure:

  • Number of data incidents reported over a specified period.

4. Data usage and adoption rate: The rate at which business units or teams adopt and use data assets. This metric highlights the effectiveness of your data governance strategies in promoting a data-driven culture.

How to measure:

  • Track the number of data assets accessed or queries made by departments against total available data assets.

5. Data stewardship activity: Monitoring key activities for data stewards, such as data validation, correction, annotation, etc. Meaningful stewardship activity can be an indicator of active data management and the effectiveness of data stewards.

How to measure:

  • Number of meaningful actions taken by data stewards over a given period.

6. Data governance training and awareness level: The rate at which employees are trained and aware of data governance policies and practices. Regular, effective training ensures that staff are equipped with the right knowledge around data governance policies and best practices.

How to measure:

  • Survey employees, monitor the percentage of staff who have completed data governance training, or track quiz scores.

7. Compliance with data standards: The rate at which data conforms to defined standards or formats. Uniform data promotes ease of use across the organization.

How to measure:

  • Perform audits or automated checks on datasets to identify non-conforming data.

Monitoring these data governance KPIs allows businesses to assess and refine their data governance strategies over time. By paying attention to these metrics, organizations can promote a strong data culture and drive value from their data assets.

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