Authors: Steven Walker, Andrew Allen, Kenneth Young, Chaitanya Srinivas, Niharika
Abstract: The rapid adoption of artificial intelligence, cloud computing, big data analytics, and digital platforms has significantly increased the complexity of enterprise data management and governance. Organizations increasingly require mature data governance capabilities to ensure that enterprise data is accurate, secure, accessible, compliant, traceable, and suitable for intelligent decision-making. However, conventional data governance maturity models frequently depend on manually assessed capabilities, static evaluation criteria, and periodic assessments, limiting their ability to respond to rapidly changing data environments and emerging AI-driven requirements. This research proposes an AI-Enabled Data Governance Maturity Framework for Intelligent Enterprise Data Management that integrates artificial intelligence, machine learning, predictive analytics, data quality management, metadata management, data lineage, security, privacy, compliance, and governance automation into a unified maturity assessment approach. The proposed framework evaluates enterprise governance capabilities across multiple maturity dimensions, including data strategy, organizational governance, data quality, metadata management, lineage and traceability, security and privacy, regulatory compliance, technology enablement, AI governance, and automation. AI-driven analytical mechanisms are incorporated to assess governance performance, identify capability gaps, predict governance risks, and recommend prioritized improvement actions. The framework further introduces continuous monitoring and feedback mechanisms to enable organizations to dynamically update their maturity assessments as data environments, business requirements, technologies, and regulatory conditions evolve. A maturity scoring mechanism can classify organizations into progressive levels ranging from initial and developing governance practices to managed, intelligent, predictive, and optimized governance capabilities. The proposed approach aims to reduce manual assessment effort, improve governance transparency, strengthen data quality and compliance, and support continuous improvement of enterprise data management practices. The framework can be evaluated using metrics such as maturity assessment accuracy, governance-risk prediction accuracy, data-quality improvement, compliance effectiveness, automation rate, assessment efficiency, and reduction in governance-related incidents. The proposed framework provides a foundation for organizations seeking to transition from traditional governance practices toward intelligent, adaptive, and AI-enabled enterprise data governance.
DOI: http://doi.org/10.5281/zenodo.23075782
