What Does it Take to Connect? Unveiling Characteristics of Data Space Connectors

Abstract

Data spaces are a novel data management approach to collect large-scale heterogeneous data distributed over various data sources in different formats. To access these data spaces, users require so-called connectors to ensure technical compliance (e.g., usage control policies) and ensure that users play by the ‘same rules’. While connectors are a critical component of data spaces and receive considerable attention in politics, practice, and research, there is still no shared understanding of what constitutes a connector. To address this gap, we analyzed 23 connector use cases, diverse types of practitioner literature (n = 14), 25 scientific papers, and a workshop with five experts to extract the characteristics of connectors. We synthesized our findings into a taxonomy of connectors that integrates insights from the conceptual and empirical analysis and finalized it by classifying two connectors within the taxonomy. Our paper contributes to understanding this novel artifact, which has implications for future businesses.