International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Building AI-Native Data Platforms: From Data Lakes to Intelligent Decision Platforms

Author(s) Ms. Neetu Uthaman, Mr. Jose Prabhu Michael Singarayan, Mr. Jayakumar Ramalingam
Country United States
Abstract This is an original research paper that investigates how to create Artificial Intelligence (AI)-native data platforms that transform data lakes into intelligent data decision platforms. The research relies on a self-made data set, which includes 215 enterprise data platform instances for ingestion quality, storage readiness, visibility of metadata, coverage of automation, decisions delivery, trust score, user adoption, access speed, and control of risk. The dataset was staged to simulate real enterprise data platform stage behavior for data lake, processed data, intelligent service and decision platform. The tools used in the study are spreadsheet-based tabulation, graphical analysis, architecture modelling and structured performance comparison. The paper describes how modern data platforms go beyond just the data store, adding governance, metadata, automation, learning-enabled services, monitoring and decision applications. The proposed architecture illustrates the route to decision interfaces for business teams from raw enterprise sources. The results point to the fact that intelligent decision platforms have even more value when integrated with quality control, cataloging, model enablement and delivery services in a governed environment. The study also provides two result tables, a mixed bar-line graph and a waterfall graph to demonstrate an increase in readiness, accuracy, performance and risk reduction. Finally, the paper highlights the importance of AI-native platforms for enterprises to transform siloed data into decisions that are timely, trusted and actionable.
Keywords AI-Native Platform, Data Lake, Intelligent Decision Platform, Enterprise Data, Data Governance
Field Computer > Data / Information
Published In Volume 7, Issue 3, May-June 2025
Published On 2025-05-05
DOI https://doi.org/10.36948/ijfmr.2025.v07i03.84897
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E-ISSN 2582-2160
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CrossRef DOI prefix of IJFMR is 10.36948/ijfmr

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