Autonomous Data Products: Enabling AI-Driven Data Interoperability in Cloud Architectures

  • Authors

    • Arjun Malhotra Assistant Professor, Department of Computer Science and Engineering, NIT, India. Author

    Published 2026-01-07

  • Autonomous Data Products, Data Interoperability, AI-Driven Data Governance, Cloud Data, Architecture, Data Mesh, Data Fabric, Metadata Management, Semantic Interoperability, DataOps, Self-Describing Data Units

    Issue

    Vol. 1 No. 1 (2026)

    Section

    Articles

    How to Cite

    [1]
    A. Malhotra, “Autonomous Data Products: Enabling AI-Driven Data Interoperability in Cloud Architectures”, IJDEIC, vol. 1, no. 1, pp. 22–30, Jan. 2026, Accessed: Mar. 02, 2026. [Online]. Available: https://worldcometresearchgroup.com/index.php/ijdeic/article/view/45

  • Abstract

    As enterprises transition toward increasingly distributed and cloud-native architectures, the need for seamless data interoperability has become paramount. Traditional data integration and governance approaches often fall short in dynamic, multi-cloud environments. Autonomous Data Products (ADPs) emerge as a transformative paradigm—self-contained, self-describing, and AI-enabled units that encapsulate data, metadata, policies, and processing logic. This paper explores the architecture, capabilities, and implementation strategies of ADPs to enhance data interoperability across cloud ecosystems. We discuss how AI enables adaptive schema evolution, smart data discovery, and automated quality checks within ADPs, and how they align with principles of data mesh and data fabric. Through technical frameworks and real-world use cases, we demonstrate how autonomous data products can drive scalability, agility, and intelligence in modern data architectures.

  • References

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