How can a data governance quality manager ensure data policies are followed and quality is maintained?

1 Establish data quality metrics and goals

A data governance quality manager needs to establish data quality metrics and goals that align with the business objectives and data policies. Data quality metrics are measurable indicators of data quality, such as error rate, completeness rate, validity rate, and timeliness rate. Data quality goals are the desired levels of performance for each metric, such as 95% error-free, 100% complete, 100% valid, and 24-hour update. Data quality metrics and goals should be SMART: specific, measurable, achievable, relevant, and time-bound.

2 Monitor and report data quality issues

A data governance quality manager needs to monitor and report data quality issues on a regular basis. Data quality issues are deviations from the data quality metrics and goals, such as missing values, incorrect values, duplicate records, and outdated information. Data quality issues can affect the reliability, usability, and value of data. A data governance quality manager should use data quality tools and dashboards to track and visualize data quality issues, identify the root causes, and communicate the impact and urgency to the relevant stakeholders.

3 Implement data quality improvement initiatives

A data governance quality manager needs to implement data quality improvement initiatives to resolve data quality issues and prevent them from recurring. Data quality improvement initiatives are actions or projects that aim to enhance data quality, such as data cleansing, data validation, data standardization, and data enrichment. A data governance quality manager should prioritize data quality improvement initiatives based on the business value and risk of data quality issues, and coordinate with the data owners, stewards, and users to execute them.

4 Educate and train data stakeholders

A data governance quality manager needs to educate and train data stakeholders on the data policies and quality standards. Data stakeholders are the people who have an interest or role in data, such as data owners, stewards, users, and consumers. Data owners are the people who have the authority and accountability for data assets. Data stewards are the people who have the responsibility and expertise for data quality and metadata management. Data users are the people who have the access and permission to use data for analysis and decision making. Data consumers are the people who have the need and expectation for data products and services. A data governance quality manager should provide data stakeholders with the knowledge, skills, and tools to follow the data policies and quality standards, and to report and resolve data quality issues.

5 Collaborate and coordinate with data governance team

A data governance quality manager needs to collaborate and coordinate with the data governance team to ensure data policies are followed and quality is maintained. The data governance team is the group of people who oversee and guide the data governance program and framework. The data governance team may include a data governance leader, a data governance council, a data governance office, and data governance working groups. A data governance quality manager should work closely with the data governance team to align data quality metrics and goals, to report data quality issues and improvement initiatives, and to support data governance activities and projects.

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?