How do you enforce data quality and governance policies?

1 Define data quality and governance goals

The first step to enforce data quality and governance policies is to define what they mean for your business and your BI project. Data quality and governance goals should align with your business objectives, your data strategy, and your stakeholder expectations. For example, you may want to set goals for data completeness, accuracy, timeliness, consistency, validity, and security. You should also define how you will measure and monitor your data quality and governance performance, using metrics, indicators, and reports.

2 Establish data quality and governance roles and responsibilities

The second step to enforce data quality and governance policies is to establish clear roles and responsibilities for the data owners, data stewards, data consumers, and data analysts involved in your BI project. Data owners are responsible for the source and quality of the data they provide. Data stewards are responsible for the definition, documentation, and maintenance of the data standards, rules, and processes. Data consumers are responsible for the use and interpretation of the data they access. Data analysts are responsible for the transformation, integration, and analysis of the data they work with. You should also assign a data quality and governance lead or team to oversee and coordinate the data quality and governance activities and issues.

3 Implement data quality and governance processes and tools

The third step to enforce data quality and governance policies is to implement the processes and tools that will support your data quality and governance goals and roles. Data quality and governance processes include data profiling, data cleansing, data validation, data mapping, data lineage, data cataloging, data auditing, and data reporting. Data quality and governance tools include data quality software, data integration software, data catalog software, data lineage software, data governance software, and data dashboard software. You should choose the processes and tools that best fit your data quality and governance requirements, budget, and resources.

4 Communicate data quality and governance policies and expectations

The fourth step to enforce data quality and governance policies is to communicate them clearly and consistently to all the data stakeholders in your BI project. Communication is key to ensure that everyone understands the data quality and governance goals, roles, responsibilities, processes, tools, and metrics. Communication can take various forms, such as data quality and governance documents, data dictionaries, data glossaries, data catalogs, data lineage diagrams, data quality reports, data governance dashboards, and data quality and governance training sessions.

5 Monitor and improve data quality and governance performance

The fifth step to enforce data quality and governance policies is to monitor and improve your data quality and governance performance over time. Monitoring involves collecting, analyzing, and reporting on your data quality and governance metrics, indicators, and issues. Improving involves identifying, prioritizing, and resolving your data quality and governance problems, as well as implementing preventive and corrective actions. You should also review and update your data quality and governance policies, processes, and tools regularly, to ensure they are still relevant, effective, and efficient.

6 Foster a data quality and governance culture

The sixth and final step to enforce data quality and governance policies is to foster a data quality and governance culture in your organization. A data quality and governance culture is a mindset and a behavior that values, respects, and promotes data quality and governance as a strategic asset and a competitive advantage. To foster a data quality and governance culture, you should involve and empower your data stakeholders, celebrate and reward your data quality and governance achievements, learn and share your data quality and governance best practices, and continuously improve your data quality and governance capabilities.

7 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?