Intellectual Property Theories as Applied to Big Data

Abstract

Emerging data-driven technologies and exponential information growth are catalyzing new conceptualizations of data governance. This article undertakes comparative analysis of intellectual property regimes and rights theories as applied to big data. It evaluates tensions between public good knowledge sharing and private interests in data control. Doctrinal, ethical, economic and policy perspectives inform examination of varied models including IP analogues, effort-based rights, unfair competition, metadata protections, technological controls and context-specific data rights. The study assesses merits and critiques of existing and proposed regulatory approaches across jurisdictions. It concludes by proposing balanced frameworks recognizing collective oversight, differentiated data protections and public interest mandates as pathways to stimulate socially beneficial data innovation.