A formalized set of policies, standards, and procedures that serves as a rulebook for data stewards, analysts, and other stakeholders to ensure data is consistently arranged into homogeneous (similar) groups according to their common characteristics in alignment with business objectives, regulatory requirements, and risk management practices. It includes clear definitions of data categories so that all users understand the scope and implications of each metadata label, while outlining standardized procedures and precise criteria for examining, evaluating, and categorizing data. By incorporating role assignments and continuous improvement protocols, the Data Classification Framework provides a structured approach essential for effective data governance, ensuring data is classified objectively and consistently to support compliance and mitigate risk. Example: When classifying data, the following criteria may be used: * Data Domain: Categorize data by the business area or subject matter it represents—such as finance, marketing, operations, or customer relations—ensuring that data from different domains is consistently grouped. * Data Ownership: Assign classifications based on data stewardship or ownership, which helps in tracking accountability and ensuring that the right stakeholders manage and maintain the data. * Data Sensitivity: Although more security-focused, sensitivity classification can also be integrated to distinguish between public, unflassified, restricted, confidential, secret and top secret data, which supports proper protection and handling. * Data Lifecycle Stage: Classify data by its stage in the lifecycle—raw, processed, archived, or purged—to facilitate efficient management and storage strategies. * Data Quality: Assess and classify data based on quality dimensions like accuracy, completeness, consistency, timeliness, and validity. This helps in identifying datasets that meet required standards for analysis and reporting. * Data Format and Structure: Distinguish between structured, semi-structured, and unstructured data, or by file types and formats (e.g., relational data, text, images, etc.) to determine the best storage and processing techniques. * Usage Frequency and Criticality: Group data based on how frequently it is accessed and its importance to business operations, which supports prioritization in both maintenance and performance optimization. * Geographical or Regional Origin: In cases where location matters (e.g., for regulatory reasons or market analysis), data can be classified by geographic region or the originating business unit.