Towards Insight-Centric Data Spaces: Integrating Visual Analytics and Decentralized Data Ecosystems

Abstract

Visual analytics and dataspaces address complementary challenges in modern data ecosystems: visual analytics enables interactive exploration and human-centered reasoning over complex data, dataspaces provide decentralized infrastructures for sovereign and collaborative data sharing. Both fields have largely evolved independently, resulting in a gap between data management and data understanding in distributed environments.We explore their intersection and argue that integration is essential for next-generation data ecosystems. Visual analytics acts as a cognitive layer for dataspaces by making data availability, quality, provenance, and exchange processes observable and interpretable, while dataspaces reshape visual analytics by introducing decentralized, heterogeneous, and governed data environments. We illustrate this interplay through a semi-synthetic cultural dataspace scenario analyzing exchange patterns and metadata quality across a federated ecosystem. Based on these insights, we identify key open challenges and outline a vision of insight-centric data spaces, in which data management and analysis are tightly integrated into a unified, human-centered environment.