What are the best data governance metrics to prioritize issues and improvements?

1 Data Quality Metrics

Data quality metrics are indicators of the accuracy, consistency, completeness, and timeliness of your data. They can help you detect and address any errors, anomalies, or gaps that may impact your data analysis, reporting, and decision making. For instance, data accuracy is the percentage of data records that match the expected or reference values. Data completeness is the percentage of data records that have all the required fields populated. Additionally, data consistency is the percentage of data records that follow the same format, rules, and standards across different sources and systems. Finally, data timeliness is the average time between data creation and its availability for consumption.

2 Data Security Metrics

Data security metrics are indicators of how well your data is protected from unauthorized access, modification, or loss. They help in monitoring and enforcing data security policies, controls, and practices. For example, data breach incidents can be monitored to assess the number and severity of data breaches that occurred in a given period. Additionally, data access requests from internal and external users or parties can be tracked to determine their number and status. Furthermore, the percentage of data encrypted at rest and in transit can be measured as a metric. Lastly, the frequency and success rate of data backup and recovery operations should be regularly monitored. All these metrics can help you ensure that your data is secure.

3 Data Availability Metrics

Data availability metrics provide insight into how accessible and reliable your data is for your data consumers. These metrics can help ensure that your data is always ready and available for data needs and demands. Data downtime, data latency, data freshness, and data utilization are all examples of data availability metrics. Data downtime is the total time that your data was unavailable or inaccessible due to system failures, maintenance, or other issues. Data latency is the average time between a data request and its delivery. Data freshness measures the average age of your data at the point of consumption. And finally, data utilization indicates the percentage of data that is actually used or consumed by your data consumers.

4 Data Usability Metrics

Data usability metrics are indicators of how easy and effective your data is for your data consumers. They can help you enhance user experience, satisfaction, and adoption. For example, tracking the number and frequency of data catalog visits, searches, and views by consumers can give insight into data catalog usage. Additionally, measuring the percentage of data assets that have sufficient and accurate metadata, descriptions, and lineage information can provide insight into documentation quality. Furthermore, tracking the number and type of data feedback received from users such as ratings, reviews, comments, and suggestions can give insight into user feedback. Lastly, estimating or measuring the business value or impact of your data assets such as revenue, cost savings, customer satisfaction, or innovation can give insight into data value.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?