A Marketplace for Data: An Algorithmic Solution
We aim to create a data marketplace – a robust matching mechanism to efficiently buy and sell data while optimizing social welfare and maximizing revenue. Monetization of data is an essential focus by many industries but there doesn’t exist a market mechanism that can price data and match buyers to vendors while addressing the (computational and other) complexity associated with creating a market platform. The challenge in creating such a marketplace stems from the nature of data as an asset: (i) it is replicated at zero marginal cost; (ii) its value is inherently combinatorial (i.e. value depends on other (potentially correlated) datasets are available); (iii) value to a firm is dependent on which other firms get access to the same data; (iv) prediction tasks and value of an increase in prediction accuracy vary widely between different firms – not obvious how to set prices for a collection of datasets with correlated signals; (v) authenticity and truthfulness of data is difficult to verify a priori without first applying it to a prediction task. Our proposed marketplace provides an algorithmic solution combining concepts from machine learning, algorithmic market design, and mathematical optimization under uncertainty. We discuss some examples motivating this work.
Authors: Anish Agarwal, Tuhin Sarkar
