Data sovereignty, the possibility to keep control over data, is gaining increasing attention in both research and industry. Due to complex supply chains and a strong trend toward digitization, digital assets are essential to be fast and competitive. As a result, companies need to share data while retaining control over it to prevent unwanted leaks of sensitive data. However, implementing effective data governance, access, and usage control mechanisms can be challenging, especially in cross-company data sharing networks and ecosystems like dataspaces. In this paper, we examine the industrial landscape and interview eleven experts from software providers and producing organizations to identify their requirements and challenges of existing data sovereign solutions. Based on Grounded Theory and semi-structured interviews, we explore the motivations and issues behind data sharing from an Information Systems and Software Engineering point of view. The findings include current industrial contexts, use cases, and solutions with data sovereignty’s technical and non-technical implementations. Seven requirements and thirteen challenges were observed throughout a qualitative analysis. Clustered by organizational, technical, personal, and emotional viewpoints, they are discussed with initial approaches for mitigation. The results identify current practical needs and will enable the design of future data sovereignty solutions in theory and different practical domains.

The establishment of collaborative AI pipelines, in which multiple organizations share their data and models, is often complicated by lengthy data governance processes and legal clarifications. Data sovereignty solutions, which ensure data is being used under agreed terms and conditions, are promising to overcome these problems. However, there is limited research on their applicability in AI pipelines. In this study, we extended an existing AI pipeline at Mondragon Corporation, in which sensor data is collected and subsequently forwarded to a data quality service provider with a data sovereignty component. By systematically reflecting and generalizing our experiences during the twelve-month action research project, we formulated ten lessons learned, four benefits, and three barriers to data-sovereign AI pipelines that can inform further research and custom implementations. Our results show that a data sovereignty component can help reduce existing barriers and increase the success of collaborative data science initiatives.

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