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Organizations that would mutually benefit from pooling their data are otherwise wary of sharing. This is because sharing data is costly—in time and effort—and, at the same time, the benefits of sharing are not clear. Without a clear cost-benefit analysis, participants default in not sharing. As a consequence, many opportunities to create valuable data-sharing consortia never materialize and the value of data remains locked. We introduce a new sharing model, market protocol, and algorithms to incentivize the creation of data-sharing markets. The combined contributions of this paper, which we call DSC, incentivize the creation of data-sharing markets that unleash the value of data for its participants. The sharing model introduces two incentives; one that guarantees that participating is better than not doing so, and another that compensates participants according to how valuable is their data. Because operating the consortia is costly, we are also concerned with ensuring its operation is sustainable: we design a protocol that ensures that valuable data-sharing consortia form when it is sustainable. We introduce algorithms to elicit the value of data from the participants, which is used to: first, cover the costs of operating the consortia, and second compensate data contributions. For the latter, we challenge the use of the Shapley value to allocate revenue. We offer analytical and empirical evidence for this and introduce an alternative method that compensates participants better and leads to the formation of more data-sharing consortia. 

The growing adoption of data analytics platforms and machine learning-based solutions for decision-makers creates a significant demand for datasets, which explains the appearance of data markets. In a well-functioning data market, sellers share data in exchange for money, and buyers pay for datasets that help them solve problems. The market raises sufficient money to compensate sellers and incentivize them to keep sharing datasets. This low-friction matching of sellers and buyers distributes the value of data among participants. But designing online data markets is challenging because they must account for the strategic behavior of participants. In this paper, we introduce techniques to protect data markets from strategic participants, even when the asset traded is data. We combine those techniques into a pricing algorithm specifically designed to trade data. The evaluation includes a user study and extensive simulations. Together, the evaluation demonstrates how participants strategize and the effectiveness of our techniques.