Machine Data: Market and Analytics

Machine data (MD), that is, data generated by machines, are increasingly gain-ing importance, potentially surpassing the value of the extensively discussed personaldata. We present a theoretical analysis of the MD market, addressing challenges such asdata fragmentation, ambiguous property rights, and the public-good nature of MD. Weconsider machine users producing data and data aggregators providing MD analyticsservices (e.g., with digital twins for real-time simulation and optimization). By analyzingmachine learning algorithms, we identify critical properties for the value of MD analytics,Scale, Scope, and Synergy. We leverage these properties to explore market scenarios,including anonymous and secret contracting, competition among MD producers, and mul-tiple competing aggregators. We identify significant inefficiencies and market failures,highlighting the need for nuanced policy interventions