Europe’s ambition to create a single market for data is often discussed at a high level, but its real impact depends on whether organizations can share data in practice. Despite strong policy support, many initiatives still struggle with fragmented systems, unclear governance, and a lack of trust between stakeholders. Data spaces are intended to solve these issues but designing them remains a complex task.
The approach presented here builds on findings from deployEMDS partner imec published in the scientific journal Data in Brief, in the article “From use case to data space, a bottom-up data space design framework leveraging data products” by Casper Van Gheluwe, Gabriele Bozzi, Eridona Selita, Nele Daels, Tanguy Coenen, and Laure De Cock. While grounded in research, these insights are directly applicable to real-world deployment, particularly in the context of the deployEMDS project use cases.
A data space isn’t just a piece of technology. It’s a setup where organisations share and reuse data under agreed rules, with trust around security and control. The problem is that while the idea is clear in theory, many organisations lack know-how on how to turn it into something that works.
The deployEMDS approach tackles this differently. Instead of starting with systems and architecture, it starts with real use cases. That matters because data sharing only has value when it supports a concrete goal, like better traffic flow or improved public services.
At the core is the idea of a data product. This goes beyond just data, as it includes how it’s used, who can access it, and under what conditions. Focusing on data products keeps things practical and value-driven, making it easier for both technical and non-technical stakeholders to contribute.
Bridging use cases and technical design
To make this approach practical, deployEMDS introduces a structured design framework that connects use cases to technical implementation. It works in two steps. First, requirements are systematically captured from each use case through a guided process. Stakeholders describe what data is needed, who is involved, what constraints apply, and how the data should be used. This ensures that both technical and non-technical perspectives are taken into account. In a second step, these requirements are translated into a set of technical capabilities that the data space must provide. These capabilities can include identity management, access control, data catalogues, interoperability mechanisms, and policy enforcement tools.
What makes this approach particularly useful is that it creates a clear link between non-technical requirements and technical design decisions. Instead of relying on predefined architectures, the resulting system reflects the specific needs of the use cases it is meant to support. This also means that different use cases can lead to different configurations, even within the same domain. For example, one implementation may prioritise strict access control and interoperability standards, while another may focus on open data and transparency.
At the same time, the framework highlights an important limitation of relying solely on a bottom-up approach. If design is driven only by immediate use case needs, certain foundational capabilities may be overlooked. These include elements that are essential for a minimum viable data space, such as consistent governance mechanisms or baseline security features. To address this, the framework recommends combining bottom-up insights with top-down guidance. This ensures that the system is both practical and aligned with broader standards and long-term objectives.
Wider impact
For policymakers, this has several implications. It demonstrates that effective data sharing cannot be achieved through regulation or infrastructure alone. It requires tools and methods that help organisations translate policy goals into concrete implementations. By making this translation more systematic, the framework reduces uncertainty and lowers the barrier for participation. It also supports better decision-making by clarifying which capabilities are actually needed and why.
For citizens, the benefits are less visible but equally important. When data can be shared securely and efficiently, it enables better services, more responsive governance, and more informed decision-making. Whether it is optimising urban mobility, improving environmental monitoring, or supporting innovation, the ability to combine and reuse data plays a critical role.
The work carried out in deployEMDS shows that designing data spaces is not only a technical exercise but also an organizational and strategic one. It requires balancing flexibility with standardization, and immediate needs with long-term interoperability. By starting from use cases and systematically translating them into capabilities, the framework offers a practical way to navigate this complexity.
As data spaces continue to evolve across Europe, such approaches will be essential to ensure that the vision of a data-driven society can be implemented in a way that is both effective and trustworthy.
You can read the full paper here.
