Authors: Alice M. Young, Fiona L. Marshall, Gregory W. White, Chaitanya Srinivas, Aneesha Raj
Abstract: Enterprise data governance has become a strategic capability for organizations operating in regu-lated industries, where ensuring data quality, security, privacy, transparency, and regulatory com-pliance is essential for effective business operations and risk management. The rapid adoption of cloud computing, big data platforms, artificial intelligence, and distributed enterprise systems has significantly increased the complexity of managing data assets across heterogeneous environ-ments, creating a growing need for comprehensive governance programs. This study presents a systematic evidence mapping of enterprise data governance programs in regulated industries to synthesize existing research, identify prevailing governance frameworks, implementation strate-gies, technological enablers, and research gaps. The evidence mapping categorizes published studies according to governance objectives, regulatory compliance, metadata management, data stewardship, master data management, data quality, risk management, audit readiness, governance maturity, and supporting technologies such as data lineage, cloud platforms, automation, and artificial intelligence. The analysis reveals that effective enterprise data governance programs are characterized by strong executive sponsorship, clearly defined governance structures, standard-ized policies, automated metadata management, continuous data quality monitoring, robust com-pliance mechanisms, and cross-functional collaboration among business and technical stakehold-ers. Furthermore, the study highlights the increasing adoption of AI-enabled governance, intelli-gent metadata discovery, policy automation, and real-time compliance monitoring to improve operational efficiency and regulatory responsiveness. Despite these advancements, several chal-lenges persist, including fragmented governance practices, inconsistent metadata standards, in-teroperability issues, organizational resistance, scalability limitations, and the absence of univer-sally accepted governance maturity models. The evidence mapping also identifies promising future research directions involving autonomous governance frameworks, explainable artificial intelligence, knowledge graph–based governance, intelligent compliance automation, and continu-ous audit readiness. Overall, this study provides researchers, practitioners, and policymakers with a comprehensive overview of the current landscape of enterprise data governance programs and offers valuable insights for developing resilient, scalable, and technology-driven governance frameworks that strengthen regulatory compliance, improve organizational accountability, en-hance audit readiness, and support sustainable enterprise data management.
DOI: http://doi.org/10.5281/zenodo.21944376
