Cooperative data-analytics: a market for machine-data

Organised by

• Department of Law

Abstract

Recent technological developments in ICT and Artificial Intelligence allow extracting valuable information from data generated by machines in production, i.e. machine data (MD). Although very valuable, a market for MD and analytics may suffer from some daunting issues. MD are fragmented into many (possibly small) data producers, they feature externalities typical for non-rival information, and property rights of MD are currently fuzzy. Combining these elements with some key properties of the Machine Learning used for data analytics, we show how and to what extent a data aggregator can overcome these issues, contracting with different data producers to analyse and share cooperative analytics. Using concepts and tools in the toolbox of management and economics, we discuss conditions that affect the outcomes of data analytics, such as the heterogeneity of data producers, their request for anonymity and the intensity of competition of the markets in which they operate.

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