Machine Data: Market and Analytics

• 成果类型: Article; Early Access
• 署名作者: Calzolari, Giacomo; Cheysson, Anatole; Rovatti, Riccardo
• 署名单位: European University Institute; Centre for Economic Policy Research - UK; University of Bologna
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2023.00674
• 发表日期: 2025
• 关键词: digital twins; IoT; 5G; ICT; enabling technology market organization; Externality; anonymity; property rights; COMPETITION; collusion; machine data; industrial data; nonpersonal data; data analytics services; Machine Learning; Artificial intelligence

摘要

Machine data (MD), that is, data generated by machines, are increasingly gaining importance, potentially surpassing the value of the extensively discussed personal data. We present a theoretical analysis of the MD market, addressing challenges such as data fragmentation, ambiguous property rights, and the public-good nature of MD. We consider machine users producing data and data aggregators providing MD analytics services (e.g., with digital twins for real-time simulation and optimization). By analyzing machine 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 multiple competing aggregators. We identify significant inefficiencies and market failures, highlighting the need for nuanced policy interventions.