A data governance framework is a set of policies and practices that help an organization manage its data.
It’s a strategic program that ensures data is accurate, reliable, and accessible.
Key elements of a data governance framework:
– Data quality: Guidelines for data quality, including how to label and categorize data
– Security and privacy: Policies and processes to protect data from unauthorized access and breaches
– Compliance: Guidelines to ensure data complies with regulatory requirements
– Data management: Processes for managing data throughout its lifecycle
– Monitoring and auditing: Regular processes to ensure data is accurate and secure;
– Training and awareness: Building knowledge and skills about data governance
Benefits of a data governance framework minimizes risk, reduces costs associated with fragmented data, streamlines workflows, and supports data-driven insights.
Implementing a data governance framework:
– Identify and prioritize existing data
– Implement ownership and responsibility for data
– Use a data governance maturity model to measure your company’s current state and build a roadmap
Automate for greater efficiency
Automating certain parts of the data governance process can help improve efficiency and reduce errors. Data governance and management tools can help automate routine tasks such as:
– Constructing data lineages to help visualize the flow of data through systems without the need for complex hand-coded solutions.
– Propagating policies to assign metadata tags to specific data elements—such as columns or tables—to identify them as sensitive information.
– Generating audit logs to record data interactions to understand how employees use data.
– Data classification is based on predefined categories, such as PII, financial data, intellectual property or confidential information.
