The mobility sector plays a critical role in modern societies, encompassing diverse types of movement for both tourism and work-related purposes. Moreover, this sector is linked with the current ability to widely generate real-time data, providing valuable information that can be leveraged for analytics and informed decision-making. However, extracting actionable insights remains challenging due to the pronounced heterogeneity of data sources and the often unreliable nature of traditional data exchange environments. Data spaces have emerged as a solution to these challenges, providing secure and governed environments for inter-organizational data sharing while ensuring that data owners retain full sovereignty over what they provide. Specifically, data spaces enable secure collaborations between consumers, who need to exploit their data, and providers, who offer value-added services to support decision-making processes. Nonetheless, despite their potential, research on data spaces is still in its early stages, and there is no clear consensus on the core concepts that underpin their practical implementation. Therefore, we first conduct a comprehensive literature review to design an architecture for data spaces and then we present the implementation of this architecture in a mobility data space. Our case study demonstrates the practical applicability of data spaces in the mobility domain, illustrating how they can be used not only to activate datasets but also to enable value-added services built upon those data, such as interactive dashboards. The results show that data spaces offer a viable approach for effectively leveraging mobility data, supporting both operational optimization and strategic decision-making in transportation services.

Dashboards are popular tools for presenting key insights to decision-makers by translating large volumes of data into clear information. However, while individual visualizations may effectively answer specific questions, they often fail to connect in a way that conveys the overall narrative , leaving decision-makers without a cohesive understanding of the area under analysis. This paper presents a novel methodology for the systematic design of holistic dashboards, moving from analytical requirements to storytelling dashboards. Our approach ensures that all visualizations are aligned with the analytical goals of decision-makers. It includes several key steps: capturing analytical requirements through the i* framework; structuring and refining these requirements into a tree model to reflect the decision-maker’s mental analysis; identifying and preparing relevant data; capturing the key concepts and relationships for the composition of the cohesive storytelling dashboard through a novel storytelling conceptual model; finally, implementing and integrating the visualizations into the dashboard, ensuring coherence and alignment with the decision-maker’s needs. Our methodology has been applied in real-world industrial environments. We evaluated its impact through a controlled experiment. The findings show that storytelling dashboards significantly improve data interpretation , reduce misinterpretations, and enhance the overall user experience compared to traditional dashboards.