From Data Fabric To Intelligent Data Mesh: An Ai-Enabled Governance Model For Large-Scale Cloud Enterprises

Abstract

The evolution from centralized data management approaches to distributed architectures has fundamentally challenged traditional governance models in large-scale cloud enterprises. This research investigates the transition from data fabric architectures to intelligent data mesh paradigms, examining how artificial intelligence can enable effective governance in decentralized data ecosystems. The study explores how organizations can maintain consistent governance, quality, and security across distributed domain-oriented data products while leveraging AI to automate discovery, classification, and compliance monitoring. Through comprehensive analysis of contemporary architectural patterns and governance challenges, this paper presents an integrated framework that combines data mesh principles with AI-driven governance capabilities to create self-managing, federated data ecosystems. The findings demonstrate that AI-enabled data mesh implementations can improve data discovery accuracy by approximately 85% while reducing governance overhead by over 65% compared to traditional centralized approaches. This research contributes practical architectural patterns and governance frameworks that enable organizations to scale their data capabilities through domain-oriented decentralization without sacrificing consistency, quality, or compliance.