Building Trusted and Intelligent Enterprise Stewardship Systems through Cognitive MDM Architectures with Explainable Large Language Models, Autonomous Governance Automation, and Semantic Workflow Intelligence

Nagender Yamasani

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2026, vol. 12, issue 2, 841-864

Abstract: Enterprise master data environments continue to face critical challenges related to data inconsistency, governance complexity, fragmented stewardship operations, and the limited adaptability of conventional rule driven management systems in dynamic digital enterprises. This study proposes a cognitive Master Data Management architecture that integrates Explainable Large Language Models, autonomous governance automation, and semantic workflow intelligence to establish a trusted and intelligent enterprise stewardship ecosystem. The research addresses the growing need for adaptive, context aware, and transparent data governance frameworks capable of supporting enterprise scale decision intelligence while maintaining regulatory compliance and operational reliability. A mixed methodological approach combining architectural modeling, workflow simulation, semantic reasoning analysis, and enterprise governance evaluation was employed to validate the proposed framework across distributed data stewardship scenarios. Findings demonstrate significant improvements in governance transparency, workflow efficiency, semantic interoperability, automated policy enforcement, and contextual decision support when compared with traditional MDM architectures. The framework introduces an innovative explainable reasoning layer that enhances trust, accountability, and human centered oversight in autonomous enterprise environments. Strategic contributions include the advancement of intelligent governance automation, cognitive data stewardship, and scalable semantic orchestration for enterprise ecosystems. Academic contributions extend existing research in cognitive enterprise systems, explainable artificial intelligence, and intelligent data governance. The study concludes that cognitive MDM architectures represent a transformative foundation for future enterprise stewardship systems requiring scalable intelligence, operational resilience, and trustworthy governance automation.

Keywords: Cognitive Master Data Management; Explainable Artificial Intelligence; Large Language Models; Enterprise Data Stewardship; Autonomous Governance Automation; Semantic Workflow Intelligence; Intelligent Data Governance; AI Driven MDM Architectures; Trusted Enterprise Systems; Context Aware Decision Intelligence; Enterprise Knowledge Graphs; Data Quality Automation; Semantic Reasoning Frameworks; Human Centered AI Governance; Adaptive Workflow Orchestration; Enterprise Metadata Intelligence; Autonomous Data Stewardship; AI Enabled Governance Frameworks; Intelligent Enterprise Architecture; Responsible Artificial Intelligence; Distributed Enterprise Data Management (search for similar items in EconPapers)
Date: 2026
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT26123316
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v12:y2026:i2:id:2018

DOI: 10.32628/CSEIT26123316

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