AI-Augmented Data Governance Framework for Data Management Offices

Abstract

The exponential growth of organizational data assets has rendered traditional manual data governance approaches inadequate for modern Data Management Offices (DMOs). Existing frameworks lack the scalability, adaptability, and intelligence required to address the complexities of heterogeneous, high-velocity data environments. This paper proposes the AI-Augmented Intelligent Data Management Office (AI-iDMO) framework, a comprehensive six-layer architecture that integrates cutting-edge artificial intelligence including natural language processing (NLP), deep learning, federated learning, knowledge graphs, and reinforcement learning to automate and optimize core data governance functions. The AI-iDMO framework encompasses automated metadata management, real-time data quality monitoring, policy compliance enforcement, intelligent data lineage tracking, and adaptive access control, all orchestrated through a unified AI governance engine. We formally define each framework component using mathematical notations and present a reference implementation evaluated on benchmark enterprise datasets. Experimental results demonstrate that AI-iDMO achieves a classification accuracy of 96.8%, an F1-score of 0.967, a precision of 0.971, and a recall of 0.963, outperforming five state-of-the-art baselines. The framework offers a scalable, explainable, and privacy-preserving approach to intelligent data governance, providing practical guidance for DMOs navigating increasingly stringent regulatory requirements such as GDPR, CCPA, and HIPAA.