We propose modeling real-world data markets, where sellers post fixed prices and buyers are free to purchase from any set of sellers, as a simultaneous-move game between the buyers. A key component of this model is the negative externality buyers induce on one another due to purchasing data with a competitive advantage, a phenomenon exacerbated by data's easy replicability. We consider two settings. In the simpler complete-information setting, where all buyers know their valuations, we characterize both the existence and welfare properties of the pure-strategy Nash equilibrium in the presence of buyer externality. While this picture is bleak without any market intervention, reinforcing the limitations of current data markets, we prove that for a standard class of externality functions, market intervention in the form of a transaction cost can lead to a pure-strategy equilibrium with strong welfare guarantees. We next consider a more general setting where buyers start with unknown valuations and learn them over time through repeated data purchases. Our intervention is feasible in this regime as well, and we provide a learning algorithm for buyers in this online scenario that under some natural assumptions, achieves low regret with respect to both individual and cumulative utility metrics. Lastly, we analyze the promise and shortfalls of this intervention under a much richer model of externality. Our work paves the way for investigating simple interventions for existing data markets to address their shortcoming and the unique challenges put forth by data products.

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