Supporting the Tourism (data) Strategy

To understand what a data space is, it helps to begin with what it is not and to trace how data sharing has evolved to reach this concept. Most people are familiar with APIs. An API transfers data from one point to another. This represents the starting point: a one-to-one relationship. APIs are still valid in the data space world, however over time, the need to share data with more actors emerges. Instead of one-to-one exchanges, data sharing shifts toward one-to-several situations, which leads to the rise of … platforms. In this configuration, a single endpoint can share data with multiple actors. Rather than a single recipient, the data may be accessed by two, three, four, or even hundreds or thousands of participants.

This is typically enabled through what is known as data lakes, data warehouses and what is now in fashion data lake-houses : data is copied from its original source and placed into a centralised repository where actors within the same ecosystem can access it and consume it.

This approach works, but only to a certain extent. Access is limited to participants within that specific silo (platform), and it mostly relies on creating replicas of the data from where it was created. Moreover, not all data can realistically be duplicated and placed into such centralised environments.

Data spaces represent the next step in this evolution. They move beyond the one-to-several model towards a one-to-N approach, where an unlimited number of participants can potentially interact. The model can be compared to the internet. When connected to the internet, any participant is effectively connected to a global network of others. The exact number of participants may be unknown, but communication is possible because everyone relies on a shared set of standards. This shared language, standardisation, is precisely what removes the silo effect and makes data spaces possible.

A data space can be pictured as a solar system: a structured environment where actors gravitate around a central body that orchestrates the whole. Zoom out further, and the Common European Data Spaces become the galaxy:  an organised space where multiple data space ecosystems coexist and interact according to shared rules. Vast, governed, and open.

What makes this possible is standardisation. Just as gravity defines the rules within a solar system, agreed-upon standards define how actors behave and interact within a data space. It is not the far west,  it is a governed environment. And within that governance, interoperability becomes possible.

It is worth noting that this interoperability does not fully exist yet. Data spaces are still being built. A few years ago, the concept was largely theoretical. Today, driven by the maturity of available technologies, the growing need for large-scale data access, and the rise of AI, adoption is accelerating rapidly.

So what is a data space, concretely? It is an interoperable ecosystem that brings actors together within a decentralised model, where data stays at its source. What circulates is not the data itself, but mostly metadata (the description of the data) along with associated catalogs, policies, and conditions under which exchange is possible. This structure ensures sovereignty, governance, and trust. It builds on what platforms have enabled, but goes a step further: breaking down their limitations and opening the door to a wider, more collaborative, and more resilient model of data sharing.

The first thing to understand about data spaces is that they are decentralised. Unlike traditional platforms, there is no large central infrastructure where all data is hosted and stored. Instead, the architecture is built around what are called connectors. A connector is hosted directly within each participant’s own system (or sometimes as a service) and acts as their endpoint within the ecosystem. It is the core technical element of a data space. Inside each connector sit multiple modules, each handling a specific function: identity management, contracts, and all the common building blocks needed to interact with other participants.

That said, decentralization does not mean the absence of any shared infrastructure. Some federated services do exist at the center of the ecosystem. A registry, for instance, keeps track of who the participants are and which connectors belong to the ecosystem. A federated catalog facilitates discoverability, making it possible to find out what data is available and under what conditions. These shared services sit on the governance and orchestration side of the data space, providing the common standards that make the whole system function.

The connector is what each participant needs to be part of the ecosystem — and it is what keeps the architecture truly distributed.

Trust is central to data spaces, which is why they cannot be seen as purely technical infrastructures. They are ecosystems built to serve real use cases—both public and business—and require clear governance and economic models. Without trust, such environments simply cannot function.

One key element is the decentralised architecture. Data is not copied and stored in a central system managed by a third party. Instead, participants keep control of their own data, which already strengthens trust by design.

On top of this, Data spaces use mechanisms such as smart contracts within a “control plane.” This layer allows participants to discover data and negotiate access conditions through machine-readable contracts that are also legally valid (often based on standards like ODRL). Only participants that meet specific rules can access the data.

Trust is further reinforced through verifiable credentials—digital proofs that confirm identity and permissions. These ensure that a data provider can trust that a consumer is who they claim to be and will respect the defined usage conditions.

In a way, the rules are embedded into the system, making it impossible to access data without complying with them. However, once the data is used outside the data space, full control cannot be guaranteed. What remains is traceability: participants have agreed to the rules, and these commitments are legally enforceable if needed.

This model enables data sharing even between unknown participants, across sectors or countries, based on trust by design rather than prior relationships.

From a physical perspective, Europe has already organised its economy to enable free movement across countries, for example, through the Schengen area. However, in the digital world, this level of fluidity does not yet exist. The ability to seamlessly exchange data, collaborate, and “move freely” across digital ecosystems remains limited.

Today, there are still significant challenges: concerns about dominant players concentrating too much power, difficulties in discovering available data, and the fact that a large share of data (especially industrial data) remains unused. In fact, around 80% of data is not shared and simply stored, representing a major untapped source of value.

As an economic space, Europe aims to unlock this potential by building infrastructures that improve data flow and enable data reuse. The more data circulates, the more value it can generate.

Beyond the economic dimension, there is also a strong political rationale. European initiatives, such as data strategies and AI development plans, seek to ensure sovereignty over data and strengthen resilience in a complex geopolitical context.

Not necessarily. Participating in a data space requires a certain level of maturity in terms of data management and digital capabilities. Many tourism actors are still in the early stages of digitalisation, sometimes even struggling to maintain a basic online presence. In that sense, data spaces are not immediately accessible to everyone.

However, for organisations that are already active in the digital world, data should no longer be seen as a by-product of their activity, but as a real strategic asset. While data has traditionally been managed by IT departments, it is increasingly becoming a core business topic, with roles like Chief Data Officer gaining importance.

There is also a shift in mindset: data can create value beyond internal use. It can be shared, exchanged, or even monetised, enabling better insights, stronger collaboration, and more informed decision-making. This is why the focus is moving from individual sectors to interconnected ecosystems.

In a context marked by global challenges such as COVID-19 or environmental transitions, isolated approaches are no longer sufficient. Collaboration becomes essential. Being part of a data ecosystem offers more opportunities, more flexibility, and greater resilience in an environment that is constantly evolving.