Data is the central commodity of the digital economy. Unlike physical goods, data exhibits properties that defy the standard theory of supply and demand: it is non-rival (the same dataset can be sold to multiple buyers without degradation), it is replicable at near-zero cost, and it is traded under heterogeneous licensing rules that restrict lawful use. Determining a new pricing principle to attach a fair price tag to datasets is therefore a difficult but central problem. We propose a game-theoretic framework in which the value of a data string emerges from strategic competition among $N$ players betting on a stochastic process with asymmetric information about past outcomes. A better-informed player may either exploit her advantage or sell part of her dataset to less informed competitors. By analytically deriving the Nash equilibrium, we identify the price range for a mutually beneficial trade. The model reveals market dynamics that depart from textbook intuition: informed players may compete or jointly exploit the least informed; data can be shared even at zero price without reducing the seller`s utility; rivalry among well-informed players can benefit uninformed ones; and trades infeasible in small markets can be viable in larger ones. These findings establish a theoretical foundation for the pricing of intangible goods in interacting digital markets, which are in need of robust valuation principles.

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