Dataspace-enabled Analytics - Scope, Opportunities and Challenges

Dataspaces enable secure, decentralized data exchange between organizations, ensuring trust and data privacy while enhancing collaboration and operational efficiency. They support interoperability and compliance through standardized protocols, fostering innovation and informed decision-making across various industries. However, implementing dataspaces is complex and costly, requiring significant investment in technology and compliance. Achieving seamless interoperability and maintaining data integrity across diverse systems remains a challenge, making this an ongoing research area. Fraunhofer FIT is a leading institution in dataspace technologies participating in a diverse range of projects and initiatives. Through this work, we have identified dataspaces‘ potential to accelerate and enrich analytical tools development by bringing together numerous partners willing to share data. We define "dataspace-enabled analytics" to include all analytical tools connected to dataspaces, from visualization to AI applications. While these tools benefit from dataspace architectures, they must be adapted to the unique settings of dataspaces, which can hinder their use.