Data Governance Transformation: Now is the time to revisit Data Governance Policy - Pasner, Jacob (International Journal of Population Data Science, 2026)
This paper presents MDI's Data Governance Transformation (DGT) project, an already-in-use data governance policy & infrastructure framework developed to support robust implementation of Privacy Enhancing Technologies (PETs), govern rapid AI development, and confront precedent shattering data use in the United States. As government agencies increasingly rely on complex and distributed data ecosystems, traditional data management approaches have proven insufficient to ensure data quality, accessibility, privacy, and interoperability. This framework establishes a cross-functional data governance structure that includes clearly defined roles, a RACI (Responsible, Accountable, Consulted, Informed) matrix, a standardized change control process, and an architecture rooted in medallion-style data layering. It also includes practical guidance on the following: conducting a data inventory, aligning with cloud and privacy requirements, and coordinating with contractors to ensure data portability and reproducibility. By embedding PETs and clear accountability mechanisms, this framework not only supports compliance with regulatory mandates but also enables data-driven decision-making and responsible use of AI. The framework serves as a replicable model for other government and non-government entities seeking to implement or refresh their data governance strategies to meet the demands of modern public service delivery.