A few weeks ago, the strategy for the Integrated Mobility Data Space (EDIM) was presented: the roadmap promoted by the Ministry of Transport and Sustainable Mobility to build a public infrastructure that enables mobility data to be shared and used interoperably in Spain.
The strategy sets a clear direction, but it also raises a new question.
How do we move from the roadmap to real results?
For years, the sector has talked about the importance of data spaces: what they are, why they are needed and what opportunities they can create for administrations, operators and companies. Now the conversation is beginning to change.
It is no longer only about explaining what a data space is. It is about demonstrating that it can help solve specific mobility challenges.
Sharing data is not enough
Digital transformation has encouraged the publication and opening of data. Administrations, operators and public bodies have made progress in creating open data portals, making information available to third parties that was previously much harder to access.
But publishing data is not enough. Data may be available and still be difficult to use. They may be open and still work like a silo. They may be downloadable, but not properly understood. They may seem equivalent to data published by another administration and yet have been calculated using a different methodology.
Take, for example, a seemingly simple dataset: the average daily traffic volume on a road. Broadly speaking, we can all understand what it means. But does it include motorcycles? Does it distinguish between light and heavy vehicles? Was it captured with the same type of sensor? Is it calculated in the same way on a state road, a regional road or an urban street?
If those questions are not answered, comparing data across territories becomes complex. And if each organisation uses its own criteria, its own format and its own “dictionary”, the effort required to harmonise the information is multiplied every time someone wants to build an analysis or a model.
That is why data spaces are the next step. It is not only about making data available. It is about making sure they can be understood, combined and used under common rules. This requires metadata, standards, governance, quality criteria and mechanisms to know who accesses which data, for what purpose and under what conditions.
A well-built data space does not remove data sovereignty. On the contrary: it strengthens it. Each actor retains control over its data and decides how, when and with whom it shares them.
That is the difference between having open data and building a true data ecosystem.
From discourse to reality
Sharing data is not simply about connecting systems or creating a platform.
A data space needs technology, but it also needs:
- Governance: to define clear rules.
- Trust: so that actors can collaborate without losing control over their data.
- Interoperability: so that information can be understood and used across different systems.
- Real use cases: to demonstrate that the model creates value.
- Public-private coordination: to connect administrations, operators, companies and knowledge centres.
The challenge is not only to share data. It is to make sure that those data help support better decisions.
Learning before scaling
Before deploying a model at scale, it needs to be tested. That is why pilots are a key piece of the process.
They make it possible to check, in real scenarios:
- Which data are actually needed.
- How they should be shared.
- Which technical, legal or organisational barriers appear.
- What conditions allow effective collaboration.
- Which models can be scaled in the future.
Pilots are not just a preliminary phase. They are the space where theory begins to become practice.
The role of DS4MM
DS4MM works precisely at that intermediate point between strategy and real-world application.
The project seeks to validate how different organisations can share, connect and use data to feed useful mobility models.
It is about understanding what happens when those data start working together: when they make it possible to anticipate traffic problems before they become incidents, plan a transport network more effectively, optimise infrastructure or make decisions with a more complete view of the territory.
That is where the real value lies: in turning scattered data into models capable of helping reduce emissions, improve coordination between administrations and design more efficient mobility services.
In short, DS4MM helps make data generate better mobility decisions.
The importance of demonstrating value
Mobility is at a decisive moment. Europe is promoting a new generation of data spaces and Spain is moving forward with the EDIM strategy. More and more organisations want to be part of this new ecosystem.
Success will depend on demonstrating that data spaces are not just infrastructure, but a tool capable of generating useful services, improving decision-making and creating trust between very different actors.
It will also depend on proving that collaboration is possible without losing control over one’s own data, and that solutions developed in specific pilots can grow into models that can be applied on a larger scale.
That learning will not come only from strategic documents. It will come from experience, from pilots and from use cases that make it possible to understand what works, what needs to be improved and what conditions make progress possible.
