You’ve just implemented a data governance program. How do you know it’s working?

1 Data Quality Metrics

One of the main goals of data governance is to improve the quality of your data, which means ensuring that it is accurate, complete, consistent, valid, and timely. Data quality metrics are quantitative measures that assess the condition and reliability of your data. For example, you can track the number and percentage of errors, duplicates, missing values, outliers, and anomalies in your data sets. You can also monitor the frequency and duration of data quality checks, audits, and remediation actions. Data quality metrics can help you identify and prioritize data quality issues, evaluate the performance of your data quality tools and processes, and demonstrate the value of your data governance program.

2 Data Security Metrics

Another key objective of data governance is to protect your data from unauthorized access, use, modification, or disclosure. Data security metrics are quantitative measures that evaluate the level and effectiveness of your data security controls. For example, you can measure the number and severity of data breaches, incidents, and vulnerabilities, as well as the response time and resolution rate of data security incidents. You can also track the compliance rate and coverage of your data security policies, standards, and regulations. Data security metrics can help you assess and mitigate data security risks, optimize your data security resources and investments, and ensure the trustworthiness of your data.

3 Data Availability Metrics

Data governance also aims to ensure that your data is available and accessible to the right people, at the right time, and for the right purpose. Data availability metrics are quantitative measures that indicate the degree and ease of access to your data. For example, you can measure the uptime and downtime of your data sources, systems, and platforms, as well as the latency and throughput of your data flows and pipelines. You can also monitor the usage rate and satisfaction level of your data consumers, such as business users, analysts, and data scientists. Data availability metrics can help you optimize your data infrastructure and architecture, enhance your data service delivery and support, and increase your data value and utility.

5 Data Governance Maturity

Data governance maturity is a qualitative measure that reflects the overall state and progress of your data governance program. It involves assessing the alignment, integration, and optimization of your data governance strategy, organization, processes, and technologies. For example, you can use a data governance maturity model, such as the one proposed by the Data Governance Institute, to evaluate your data governance capabilities and performance across six dimensions: vision, people, structure, process, communication, and metrics. You can also benchmark your data governance maturity against best practices, industry standards, and peer organizations. Data governance maturity can help you identify your data governance strengths and weaknesses, set your data governance goals and priorities, and plan your data governance roadmap and actions.

6 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?