Discovering data spaces: A classification of design options
Year: 2024
Type: article
Abstract: Technical coordination between organizations and security concerns are among the major barriers to data sharing. Data spaces are an emerging digital infrastructure that helps address these challenges by sovereignly sharing data across institutional boundaries. The data space concept is at the core of many high-profile research initiatives in the European Union and receives great adoption in practice. Despite the great interest, there is, however, a demand for more conceptual clarity and approaches to describe and design them purposefully. We propose a taxonomy of data space design options grounded in a literature review, an analysis of real-world objects, and over nine hours of expert interviews with data space initiatives. The taxonomy advances our understanding of data space designs and gives a framework to practice making informed design decisions. Our work provides a comprehensive solution space for data space designers to (a) (re-)design data spaces more efficiently and (b) acquire a ‘big picture’ of what needs to be considered. • Taxonomy of key characteristics to describe, classify, and redesign data spaces. • Grounded in literature review, analysis of real-world objects, and expert interviews. • Advances understanding of data space designs by providing a framework for informed design decisions. • Addresses the need for enhanced conceptual clarity and effective design tools for data spaces. • Illustrates the wide application of data spaces in various industries.
Source: Computers in Industry
Authors: Anna Gieß, Thorsten Schoormann, Frederik Möller, Inan Gür
Language: English
Subfield: Strategy and Management
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $4,170
Award: G5585440825
OpenAlex ID: W4404402871