Understanding the Structural Characteristics of Data Platforms Using Metadata and a Network Approach

With the emergence of global platforms for trading and buying/selling data, data have become a profitable commodity. The growth of such platforms has necessitated the further expansion of the scope of data in digital economies. To this end, understanding the nature of available data and their relationships between them has become an important challenge for expanding their use. Thus, in this study, we assumed data on the platforms as a population and metadata as the samples. Thus, we quantitatively investigated the structural characteristics of data platforms, while avoiding the risk of lost business opportunities and privacy issues by not sharing the data themselves. By observing the characteristics of data and variables, we found that the data network had a structure that was locally dense and globally sparse, which is quite similar to networks of human relationships. Moreover, we found that data play different roles on the platforms when divided into sharing conditions, namely, shareable data and sensitive data. Finally, we discussed the potential tactics for individuals who create/use data platforms based on our findings. The contributions of this study include a new framework for data platform observation and a method that uses metadata and a network approach to analyze structural characteristics of data.