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This research aims to develop a novel AI-driven framework for valuing data assets using advanced predictive techniques.

July 13, 2026Scientific ReportsOpen Access

An artificial intelligence-driven holistic multi-criteria framework for data asset valuation

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This research aims to develop a novel AI-driven framework for valuing data assets using advanced predictive techniques.
Proposed a hybrid framework combining knowledge graph, preference learning, and Support Vector Regression (SVR).
Developed a three-tier indicator system featuring 18 metrics using knowledge graph technology.
Validated the framework on Bitcoin and stock index datasets against AHP and standalone SVR.
Achieved superior predictive performance by reducing RMSE by 1.446 (Bitcoin) and 1.0673 (stock index).
Reduced MAE by 2.4004 (Bitcoin) and 0.803 (stock index).
Findings highlight practical tools for enterprise data asset management and optimization.

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Randomized trial evaluates AI framework for data asset valuation, suggesting enhanced predictive methods.

Gong et al. (2026) studied this question.

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