Understanding the Price of Data in Commercial Data Marketplaces

Abstract

A large number of Data Marketplaces (DMs) have appeared in the last few years to help owners monetize their data, and data buyers optimize their marketing campaigns, train their ML models, and facilitate other data-driven decision processes. In this paper, we present a first of its kind measurement study of the growing DM ecosystem, focused on understanding which features of data are actually driving their prices in the market. We show that data products listed in commercial DMs may cost from few to hundreds of thousands of US dollars. We analyze the prices of different categories of data and show that products about telecommunications, manufacturing, automotive, and gaming command the highest prices. We also develop classifiers for comparing data products across different DMs, as well as a regression analysis for revealing features that correlate with data product prices of specific categories, such as update rate or history for financial data, and volume and geographical scope for marketing data.