Current Volume 9
AI-Powered Data Governance Fabrics: Unifying Master Data Management, Cloud Data Warehousing, Data Mesh, and GenAI Analytics for Trusted Enterprise Intelligence
Abstract
Modern enterprises require trusted analytics capable of supporting strategic decisions across increasingly distributed digital ecosystems. Traditional Business Intelligence platforms often suffer from inconsistent master records, fragmented governance policies, poor metadata synchronization, and disconnected analytical pipelines. This paper presents an advanced enterprise framework known as Intelligent Data Governance Fabrics that combines AI-powered governance, Master Data Management, cloud-native data warehousing, semantic metadata intelligence, Data Mesh principles, and Generative AI analytics governance into a unified analytical architecture. The research introduces a scalable governance-driven enterprise model designed to improve analytical trustworthiness, strengthen compliance, enhance metadata observability, and accelerate real-time decision intelligence. The paper further explores governance-aware GenAI systems, zero-trust analytical architectures, predictive metadata management, autonomous stewardship automation, and hybrid multi-cloud governance ecosystems.
Citations
IRE Journals:
Rajesh Chavan "AI-Powered Data Governance Fabrics: Unifying Master Data Management, Cloud Data Warehousing, Data Mesh, and GenAI Analytics for Trusted Enterprise Intelligence" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 3277-3280 https://doi.org/10.64388/IREV9I11-1717707
IEEE:
Rajesh Chavan
"AI-Powered Data Governance Fabrics: Unifying Master Data Management, Cloud Data Warehousing, Data Mesh, and GenAI Analytics for Trusted Enterprise Intelligence" Iconic Research And Engineering Journals, 9(11) https://doi.org/10.64388/IREV9I11-1717707
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