Tandon, Yaman (2025) AI-powered data products: The key to unlocking business value from enterprise data. World Journal of Advanced Research and Reviews, 26 (2). pp. 4244-4252. ISSN 2581-9615
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WJARR-2025-2083.pdf - Accepted Version
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Abstract
The digital transformation landscape continues evolving rapidly as enterprises shift toward cloud infrastructure and AI-driven solutions, necessitating fundamental changes in data management approaches. Traditional methods characterized by centralized warehouses, static reporting, and periodic analytics no longer meet the demands for real-time insights and automated decision-making capabilities essential in contemporary business environments. AI-powered data products represent a transformative evolution in enterprise data strategy, encapsulating analytics and intelligence within self-contained, reusable assets that directly address specific business needs. These products incorporate domain-specific data, embedded machine learning, automated pipelines, interactive interfaces, standardized APIs, and governance controls—functioning as cohesive solutions designed to deliver specific outcomes while maintaining enterprise interoperability. The architectural characteristics of these solutions enable organizations to balance decentralized innovation with enterprise-wide governance through domain orientation, self-contained structures, service-oriented interfaces, formal lifecycle management, observability frameworks, and federated governance models. Implementation requires careful attention to organizational alignment, governance frameworks, and emerging technological trends. Real-world applications across cloud infrastructure optimization, cybersecurity threat intelligence, and AI governance demonstrate substantial business value through improved cost efficiency, operational performance, and regulatory compliance. This transition toward product-oriented data assets represents a strategic imperative for organizations seeking to maximize value from data investments in an increasingly AI-driven landscape.
Item Type: | Article |
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Official URL: | https://doi.org/10.30574/wjarr.2025.26.2.2083 |
Uncontrolled Keywords: | Data products; AI integration; Enterprise architecture; Decision intelligence; Data governance |
Depositing User: | Editor WJARR |
Date Deposited: | 20 Aug 2025 11:54 |
Related URLs: | |
URI: | https://eprint.scholarsrepository.com/id/eprint/3697 |