Data valuation model for non-monetary exchanges

2026-06-04

AI Summary

Traditional economic valuation models struggle to apply to internal enterprise data exchange scenarios lacking market price mechanisms. This work proposes a normative data valuation approach grounded in user choice behavior, formalizing the value of data products as a cooperative game. By modeling user attention and preferences, the method derives a closed-form Shapley value that enables fair valuation without requiring information on costs, demand, or competitive prices. It overcomes the limitations of conventional popularity-based metrics by emphasizing the uniqueness and discriminative consumption of data, thereby effectively incentivizing the creation of high-value long-tail data products. The framework also provides a theoretical foundation for assessing the total value of internal data outputs, designing bundling strategies, and studying complementarities among data assets.

Abstract

In the evolving landscape of data product exchange platforms, traditional economic valuation models fall short due to the non-rival nature of data and the prevalence of non-monetary data product exchanges. This paper introduces a normative, choice-based metric for valuing data products within intracompany exchanges, where conventional pricing mechanisms are absent. By modeling consumer attention and preferences, the proposed metric quantifies the value of data offerings based solely on user selection behavior, without relying on cost, demand, or competitive pricing data. We show that this metric can be formally cast as a cooperative game with a closed-form Shapley value, providing a principled and fairness-based allocation of value across offerings. The model rewards uniqueness and discriminative consumption, effectively addressing the limitations of popularity-based metrics and incentivizing the creation of high-value, long-tail data products. Through theoretical analysis and illustrative examples, the metric is shown to align with economic principles, support equitable valuation, and contribute to a robust framework for measuring gross data product value. Future research directions include exploring bundling strategies and quantifying product complementarity.

Problem

• data valuation
• non-monetary exchange
• data product
• Shapley value
• cooperative game

Innovation

• data valuation
• non-monetary exchange
• Shapley value
• choice-based metric
• cooperative game theory