Data standardization can create synergies by reducing the three main obstacles to data interoperability: metadata uncertainties, data transfer obstacles, and missing data. This article analyzes the justifications for and the limitations of data standardization in light of data’s special characteristics. The article also explains how data standardization can remove obstacles to data use and integration. Standardization of data collection can lead to smoother data flows, better machine learning, and easier policing in cases where rights are infringed or unjustified harms are created by data-fed algorithms. Furthermore, data standardization has the potential to support a competitive and distributed data collection ecosystem. However, increasing the scale and scope of data analysis can create negative externalities in the form of better profiling, increased harms to privacy, and/or cybersecurity harms. Standardization also has implications for investment and innovation. The article asks whether data standardization should be regulated, and whether market-led standardization initiatives can be relied upon to increase welfare.
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