In the data economy, data sovereignty is often conceptualized as data providers’ ability to control their shared data. While control is essential, the current literature overlooks how this facet interrelates with other sovereignty facets and contextual conditions. Drawing from social contract theory and insights from 31 expert interviews, we propose a data sovereignty conceptual framework encompassing protection, participation, and provision facets. The protection facets establish data sharing foundations by emphasizing baseline rights, such as data ownership. Building on this foundation, the participation facet, through responsibility divisions, steers the provision facets. Provision comprises facets such as control, security, and compliance mechanisms, thus ensuring that foundational rights are preserved during and after data sharing. Contextual conditions (data type, organizational size, and business data sharing setting) determine the level of difficulty in realizing sovereignty facets. For instance, if personal data is shared, privacy becomes a relevant protection facet, leading to challenges of ownership between data providers and data subjects, compliance demands, and control enforcement. Our novel conceptualization paves the way for coherent and comprehensive theory development concerning data sovereignty as a complex, multi-faceted construct. © The Author(s) 2024.

Data platforms enable actors to exchange personal and business data. While data is relevant for any digital platform, data platforms exclusively revolve around data artifacts. This paper argues that the specific characteristics of data artifacts challenge the authors' understanding of platform openness. Specifically, it is argued that data artifacts are editable, interactive and distributable, which means that the consequences of opening up a data platform extend far beyond the focal platform and its context. From this, the study infers that the scope of platform openness extends beyond the data platform on which data artifacts originate. At the same time, the very nature of data artifacts afford new mechanisms to realize and reduce the risks of openness. New avenues are suggested to study platform openness in the realm of data platforms. These avenues include (1) exploring and incorporating novel consequences of platform openness in a data platform setting, (2) examining new arenas for defining openness beyond a focal platform's confines, and (3) theorizing the implications of new mechanisms for realizing openness while maintaining apparent control over data artifacts. © The Author(s) 2024.

Managers increasingly seek ways to explore insights from data for business improvements and innovation. Data Analytics (DA) platforms hold promise for businesses, especially small businesses that cannot afford tailor-made proprietary analytics services. DA platforms offer generic analytics features to a pool of businesses, saving costs and enabling benchmarking. This paper explores value propositions and adoption challenges for small businesses regarding DA. The paper offers practical insights from designing and launching a DA platform targeting small businesses in the hospitality sector. The findings of our paper show that data analytics is potentially valuable for small businesses through insights into market and customer trends. Small businesses can leverage such insights to refine their offerings. Trust and privacy concerns in sharing data are key challenges holding back adoption. We proposed measures, especially privacy-preserving technologies, to mitigate the risk of tracing a specific enterprise's data shared on the DA platform. These measures assure businesses that data shared or analyzed through the DA platform is not used to harm their competitive advantage.