How to Adapt a Data Space to Different Industrial Domains

Interoperability, semantics, and data cataloging as keys to building scalable industrial ecosystems

Industrial digitalization has evolved rapidly in recent years. Sectors such as manufacturing, transportation, energy, and logistics generate increasingly more information from sensors, IoT platforms, SCADA systems, ERPs, or business applications. However, having large volumes of data does not by itself guarantee value generation. The real challenge lies in ensuring that this information can be shared, understood, and used efficiently across different organizations, platforms, and industrial domains.

In this context, Data Spaces have become a key piece for building interoperable digital ecosystems, where different entities can exchange information while maintaining control over their data and ensuring its traceability and governance. Nevertheless, one of the main challenges of these architectures is their ability to adapt to multiple domains, each with its own models, processes, and operational needs.

An architecture valid for mobility does not always fit directly into an industrial or energy environment. Each sector uses a different language to represent assets, events, or processes. Therefore, concepts such as semantic interoperability, data cataloging, and the use of open standards are fundamental to ensuring the scalability and evolution of modern Data Spaces.

Each industrial domain has its own language

One of the greatest challenges in building Data Spaces is the heterogeneity existing between different industrial domains. Although all sectors work with digital information, each one uses different data models, terminologies, and structures to represent its processes and assets.

For example, in the railway domain, entities related to stations, traffic, track occupancy, or asset maintenance may be managed. In contrast, in an industrial environment, concepts associated with production lines, machinery sensors, manufacturing orders, or efficiency indicators predominate. Similarly, sectors such as energy or logistics handle completely different metrics, events, and operational models.

This diversity hinders interoperability between organizations and platforms. Sharing data is not only about enabling connections between systems, but about ensuring that information maintains the same meaning regardless of the environment where it is consumed. The same concept can be represented in different ways depending on the domain or even the organization that manages it.

Therefore, modern Data Spaces must be designed with multi-domain adaptation capability. Rigid architectures, built around closed models or dependent on specific technologies, limit scalability and make it difficult to incorporate new use cases.

Flexibility thus becomes a fundamental requirement. Organizations need platforms capable of integrating different information models, evolving over time, and facilitating interoperability between heterogeneous ecosystems without losing coherence or traceability over shared data.

Interoperability goes beyond connecting systems

Traditionally, many integration projects have focused solely on connecting applications through APIs or point-to-point information exchanges. However, in a Data Space, interoperability implies a much deeper level. It is not just about moving data between platforms, but about ensuring that different organizations can interpret and use that information coherently.

To achieve this, it is essential to work on common information models and mechanisms that allow data to be contextualized. At this point, open standards play a key role. Initiatives promoted by organizations such as the FIWARE Foundation or Gaia-X promote interoperable architectures based on shared models and open technologies that facilitate integration between different actors.

One of the most widely used approaches today is the use of semantic models, capable of representing entities, relationships, and context in a homogeneous manner. This allows different platforms to understand not only the value of a data point, but also its meaning within an operational or business process.

For example, an industrial temperature sensor can share information along with the asset it belongs to, its location, the capture instant, or the associated process. In this way, data acquires context and can be reused much more efficiently by different applications and organizations.

Semantic interoperability also facilitates the evolution of digital ecosystems. When systems share open and extensible models, it becomes much easier to incorporate new domains, integrate third parties, or develop advanced services based on analytics and artificial intelligence.

Data cataloging as a key element

As Data Spaces grow and incorporate new participants, the ability to locate, understand, and govern information becomes a critical factor. In this context, data cataloging ceases to be a secondary element to become an essential piece within the architecture.

A data catalog allows knowing what information exists within the ecosystem, who its owner is, how it can be used, and under what conditions it is available. In addition, it facilitates fundamental aspects such as traceability, data quality, or control over sharing processes between organizations.

Without adequate cataloging mechanisms, data ends up fragmented across different platforms, hindering its discovery and reuse. Data that cannot be located or correctly interpreted can hardly generate value within an industrial ecosystem.

Metadata plays a particularly important role here. Thanks to them, it is possible to describe information related to the origin of the data, its update frequency, the models used, or the associated access policies. This allows building more transparent and governed environments, where each organization maintains control over its digital assets.

In addition, cataloging facilitates the incorporation of new participants within a Data Space. When models and datasets are properly documented, integration and consumption tasks are considerably simplified.

In recent years, different solutions oriented toward data governance and discovery have emerged, allowing the construction of federated and scalable catalogs prepared for multi-domain environments. These capabilities are fundamental to ensuring the sustainability and evolution of industrial digital ecosystems.

Architectures prepared to evolve

Industrial Data Spaces must be designed thinking about the constant evolution of digital ecosystems. Integration needs change rapidly and organizations require platforms capable of adapting to new domains, participants, and use cases without the need to completely redesign the architecture.

In this scenario, flexibility and scalability become fundamental requirements. Modern architectures must allow the integration of OT and IT systems, combine information from multiple sources, and facilitate interoperability between organizations with different levels of technological maturity.

To this end, many current initiatives bet on federated models and open standards that reduce technological dependencies and favor collaboration between ecosystems. Technologies promoted by organizations such as the International Data Spaces Association or the FIWARE Foundation seek precisely to facilitate this secure and interoperable exchange of information between different actors.

Another key aspect is the ability to extend data models without compromising system compatibility. An architecture prepared to evolve must allow incorporating new industrial domains, adapting semantic models, and deploying new services while maintaining ecosystem coherence.

In addition, the growth of technologies associated with artificial intelligence, advanced analytics, or digital twins will further increase the need for interoperable and governed platforms. These capabilities depend directly on the quality, traceability, and contextualization of shared data.

In this context, Geprom has worked on the design and implementation of Data Space architectures oriented to multi-domain environments, capable of integrating different information models and ensuring interoperability between organizations. An example of this is the development of the Telefónica Tech Data Space, where challenges related to the integration of heterogeneous data, information cataloging, traceability, and the use of open standards to facilitate interoperability between platforms and participants have been addressed.

This type of initiative demonstrates that Data Spaces should not be understood solely as a technological platform, but as living architectures prepared to evolve alongside the real needs of industry, facilitating collaboration between organizations and enabling the construction of scalable and sustainable digital ecosystems in the long term.