Infrastructural Sovereignty over Agreement and Transaction Data (‘Metadata’) in an Open Network-Model for Multilateral Sharing of Sensitive Data

Organizations are becoming ever more aware that their data is a valuable asset requiring protection against mis-use. Therefore, being in control over the usage conditions (i.e. data sovereignty) is a prerequisite for sharing sensitive data in (increasingly complex) supply chains. Maintaining sovereignty applies to both the primary shared data and to the ‘metadata’ stemming from the data sharing support processes. However, maintaining sovereignty over this metadata creates an area of tension. Data providers must balance operational efficiency through outsourcing the data sharing support processes and the associated metadata to external, trusted, organizations against the added risk of transferring control over the metadata. At the same time, lock-in by community providers and major integration efforts due to multiple data sharing relationships need to be avoided. To address these issues, this paper elaborates an open network-model approach for maintaining sovereignty over metadata

Trust me, I’m an Intermediary! Exploring Data Intermediation Services

Data ecosystems receive considerable attention in academia and practice, as indicated by a steadily growing body of research and large-scale (industry-driven) research projects. They can leverage so-called data intermediaries, which are mediating parties that facilitate data sharing between a data provider and a data consumer. Research has uncovered many types of data intermediaries, such as data marketplaces or data trusts. However, what is missing is a ‘big picture’ of data intermediaries and the functions they fulfill. We tackle this issue by extracting data intermediation services decoupled from specific instances to give a comprehensive overview of how they work. To achieve this, we report on a systematic literature review, contributing data intermediation services

Is Your Organization Ready to Share? A Framework of Beneficial Conditions for Data Sharing

In a constantly evolving digital sphere, surmounting organizational boundaries and sharing data offers the opportunity to realize a multitude of mutual benefits, such as advanced analytics and innovative services. Organizations aspire to share data. However, they struggle to identify and establish beneficial conditions for data sharing, and research still offers little support to exploit the potential of data sharing. We apply an exploratory research approach to develop a framework of beneficial conditions for data sharing. By combining ten expert interviews and a systematic literature review, we aggregate 23 characteristics that constitute beneficial conditions into eight categories and apply and validate the framework in a real-world case. Thus, we contribute to research by providing a fundamental understanding of beneficial conditions for data sharing and a compact target picture. Additionally, we enable practitioners to systematically assess an organization’s current condition to set the course toward exploiting the full potential of data sharing

Unleashing The Potential of Data Ecosystems: Establishing Digital Trust through Trust-Enhancing Technologies

Companies increasingly innovate data-driven business models, enabling them to create new products and services. Emerging data ecosystems provide these companies access to complementary data, offering them additional potential. This, however remains untapped, as a lack of digital trust prevents companies from sharing data within these ecosystems. By using trust-enhancing technologies, companies can establish trust; this can be explained through the theoretical lens of system trust. Using a design research approach helped us to unlock the knowledge of 21 experts and identify five technologies with the potential to solve the trust challenge: self-sovereign identities, differential privacy, fully homomorphic encryption, trusted execution environments and secure multiparty computation. We integrated these technologies into the data sharing process in data ecosystems and elaborated on their limitations and maturity. Ultimately, we derived two principles that allow for adapting our results to future technological developments: complementarity and customization

Data Sharing Fundamentals: Definition and Characteristics

The importance of data as a key resource is a universal theme dominating social and business life. In this regard, inter-organizational data sharing shines in a new light prompting businesses to leverage their potential. However, it is still unclear what data sharing actually entails, i.e., what it means, what its potentials are, and what barriers one must overcome. In short, it lacks conceptual clarity and a clear description of its characteristics. The conceptual ambiguity and the synonymous use with data exchange in the literature are particularly problematic, which prevents a targeted conceptualization and use. The paper starts precisely at this point as it proposes a unifying definition and characteristics of data sharing. We report on a systematic literature review characterizing data sharing and delineating it from data exchange