Intelligent Enterprise Data Platforms Using Explainable AI for Scalable Cloud-Native Architecture Frameworks

Dr.V.R. Vimal; Dr.V.R. Vimal

doi:10.15662/ijetemr.2025.1005006

Intelligent Enterprise Data Platforms Using Explainable AI for Scalable Cloud-Native Architecture Frameworks

Dr.V.R. Vimal

Professor, Department of Computer Science & Engineering, SIMATS Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, India

Abstract

Enterprise data platforms are increasingly required to process heterogeneous, high-volume, and continuously generated data while supporting artificial intelligence, analytics, and real-time decision-making. However, conventional centralized architectures often encounter limitations involving scalability, interoperability, governance, operational complexity, and the interpretability of AI-driven decisions. This paper proposes an intelligent enterprise data platform framework that integrates Explainable Artificial Intelligence (XAI) with scalable cloud-native architecture principles. The proposed framework combines cloud-native microservices, containerized workloads, distributed data processing, data-product principles, automated governance, metadata management, and explainable machine-learning services. XAI mechanisms such as feature attribution, local explanations, model transparency, confidence assessment, and explanation auditing are incorporated into the platform lifecycle to improve trust and accountability. The methodology follows a design-oriented research approach involving architecture definition, requirement analysis, data-platform modeling, XAI integration, prototype-oriented evaluation, and comparative assessment. The framework is designed to support elastic resource allocation, independent service deployment, continuous monitoring, model governance, and cross-domain data sharing. By integrating explainability into the platform rather than treating it as an external reporting function, enterprises can obtain more transparent and auditable AI-supported decisions. The proposed framework provides a foundation for scalable, trustworthy, and adaptable enterprise data ecosystems suitable for hybrid and multi-cloud environments.

Article Information

Journal	International Journal of Emerging Trends in Engineering and Management Research
Volume (Issue)	Vol. 10 No. 5 (2025): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR)
DOI	https://doi.org/10.15662/ijetemr.2025.1005006
Pages	18682-18693
Published	October 9, 2025
Copyright	All rights reserved
Open Access	This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite	Dr.V.R. Vimal (%2025). Intelligent Enterprise Data Platforms Using Explainable AI for Scalable Cloud-Native Architecture Frameworks. International Journal of Emerging Trends in Engineering and Management Research , Vol. 10 No. 5 (2025): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 18682-18693. https://doi.org/10.15662/ijetemr.2025.1005006

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