Intelligent Cloud Data Governance Through AI-Driven Metadata Management and Real-Time Analytics in SAP Datasphere

The increasing adoption of cloud-based data platforms has created significant challenges for enterprises seeking to maintain data quality, security, consistency, and regulatory compliance across distributed information environments. Artificial Intelligence (AI)-enabled data governance offers opportunities to address these challenges through automated metadata management, intelligent data classification, anomaly detection, and real-time analytical monitoring. This research examines an integrated approach to cloud data governance using SAP Datasphere as a centralized environment for data integration, semantic modeling, and enterprise-wide data accessibility. The proposed framework combines metadata management capabilities with AI-driven analytics to improve data discovery, lineage tracking, quality assessment, access control, and governance decision-making. Metadata describing data ownership, business definitions, transformations, sensitivity, and usage patterns provides the contextual foundation for intelligent governance processes. Real-time analytics further enables organizations to identify emerging data quality issues, detect unusual access patterns, and monitor compliance-related indicators. The research methodology adopts a quantitative, design-oriented approach involving framework development, experimental implementation, and comparative evaluation against conventional governance practices. Evaluation criteria include metadata completeness, data quality, anomaly detection accuracy, lineage transparency, compliance monitoring efficiency, and analytical response time. The study aims to establish how AI and metadata-driven governance can improve enterprise data reliability, operational transparency, and responsible data utilization. The proposed approach contributes to the development of scalable, adaptive, and trustworthy cloud data governance practices within modern data-driven organizations.