Category: B – DATA GOVERNANCE

Best practices for ensuring broad participation in the practice and senior oversight of the effectiveness of data management.
The Data Governance category encompasses process areas designed to help an organization achieve strong participation across the organization for critical decisions a ecting the data assets. It provides best practices for implementing a data governance program and structure capable of functioning consistently across the broad scope of shared responsibilities; expanding and managing the organization-wide collection of approved business terms em- ployed in the target data architecture, taxonomies, and ontologies; and the development and implementation of metadata to fully describe the organization’s data assets.
In addition to building data management functions, governance may include the manage- ment of external or regulatory requirements, and external shareholders with an interes in data management activities and outcomes. More generally, governance also includes monitoring data management results to ensure that the organization receives the desired outcomes and business value from data management activities.
Governance Management addresses the processes that facilitate collaborative decision making and e ectively implement the building, sustaining, and compliance functions of governance bodies. Business Glossary practices help an organization to achieve a common understanding and representation of an expanding compendium of approved business terms, prioritize and sequence its development, and manage term creation and changes over time. Metadata Management provides a top-down approach to architecting, planning, populating, and managing the metadata repository to fully describe the organization’s data assets.
Maturity of the practices within this category will create a corporate culture of shared responsibility for the data assets. Maturity enables improved data quality, supports data integration, facilitates the design and implementation of the target data architecture, and helps the organization to develop a thorough and detailed knowledge of the as-is data layer and the lineage of data through business processes, data stores, and systems.