This information guide presents a data governance maturity model that can help organizations to evaluate and design data governance programs. Each of the following categories of data governance can be characterized in programs by five levels of maturity: initial, managing, defined, quantitatively managed, and optimizing.

• Organizational Structures & Awareness – Describes the level of mutual responsibility between business and IT, and recognition of the fiduciary responsibility to govern data at different levels of management.
• Stewardship – Stewardship is a quality control discipline designed to ensure custodial care of data for asset enhancement, risk mitigation, and organizational control.
• Policy – Policy is the written articulation of desired organizational behavior.
• Value Creation – The process by which data assets are qualified and quantified to enable the business to maximize the value created by data assets.
• Data Risk Management & Compliance – The methodology by which risks are identified, qualified, quantified, avoided, accepted, mitigated, or transferred out.
• Information Security & Privacy – Describes the policies, practices and controls used by an organization to mitigate risk and protect data assets.
• Data Architecture – The architectural design of structured and unstructured data systems and applications that enable data availability and distribution to appropriate users.
• Data Quality Management – Methods to measure, improve, and certify the quality and integrity of production, test, and archival data.
• Classification and Metadata – The methods and tools used to create common semantic definitions for business and IT terms, data models, types, and repositories. Metadata that bridge human and computer understanding.
• Information Lifecycle Management – A systematic policy-based approach to information collection, use, retention, and deletion.
• Audit Information, Logging, and Reporting – The organizational processes for monitoring and measuring the data value, risks, and efficacy of governance.

Organizations can evaluate the status of data governance within each category using the five maturity levels to identify areas for improvement and develop a larger, more integrated data governance plan. Two examples are provided to show how agencies could use the maturity model to evaluate programs.