  We model real-world data markets, where sellers post fixed prices and buyers
are free to purchase from any set of sellers, as a simultaneous game. A key
component here is the negative externality buyers induce on one another due to
data purchases. Starting with a simple setting where buyers know their
valuations a priori, we characterize both the existence and welfare properties
of the pure Nash equilibrium in the presence of such externality. While the
outcomes are bleak without any intervention, mirroring the limitations of
current data markets, we prove that for a standard class of externality
functions, platforms intervening through a transaction cost can lead to a pure
equilibrium with strong welfare guarantees. We next consider a more realistic
setting where buyers learn their valuations over time through market
interactions. Our intervention is feasible here as well, and we consider
learning algorithms to achieve low regret concerning both individual and
cumulative utility metrics. Lastly, we analyze the promises of this
intervention under a much richer externality model.
