espacios de datos

Open health data  is one of the most valuable assets of our society. Well managed and shared responsibly, they can save lives, drive medical discoveries, or even optimize hospital resources. However, for decades, this data has remained fragmented in institutional silos, with incompatible formats and technical and legal barriers that made it difficult to reuse. Now, the European Union is radically changing the landscape with an ambitious strategy that combines two complementary approaches:

• Facilitate open access to statistics and non-sensitive aggregated data.
• Create secure infrastructures to share personal health data under strict privacy guarantees.

In Spain, this transformation is already underway through the National Health Data Space or research groups that are at the forefront of  the innovative use of health data. Initiatives such as IMPACT-Data, which integrates medical data to drive precision medicine, demonstrate the potential of working with health data in a structured and secure way. And to make it easier for all this data to be easy to find and reuse, standards such as HealthDCAT-AP are implemented.

All this is perfectly aligned with the European strategy of the European Health Data Space Regulation (EHDS), officially published in March 2025, which is also integrated with the Open Data Directive (ODD), in force since 2019. Although the two regulatory frameworks have different scopes, their interaction offers extraordinary opportunities for innovation, research and the improvement of healthcare across Europe.

A recent report prepared by Capgemini Invent for data.europa.eu analyzes these synergies. In this post, we explore the main conclusions of this work and reflect on its relevance for the Spanish open data ecosystem.

1. Two complementary frameworks for a common goal

On the one hand, the European Health Data Space focuses specifically on health data and pursues three fundamental objectives:

• Facilitate international access to health data for patient care (primary use).
• Promote the reuse of this data for research, public policy, and innovation (secondary use).
• Technically standardize electronic health record (EHR) systems to improve cross-border interoperability.

For its part, the Open Data Directive has a broader scope: it encourages the public sector to make government data available to any user for free reuse. This includes  High-Value Datasets that must be published for free, in machine-readable formats, and via APIs in six categories that did not originally include "health." However, in the proposal to expand the new categories published by the EU, the health category does appear.

The complementarity between the two regulatory frameworks is evident: while the ODD facilitates open access to aggregated and non-sensitive health statistics, the EHDS regulates controlled access to individual health data under strict conditions of security, consent and governance. Together, they form a tiered data sharing system that maximizes its social value without compromising privacy, in full compliance with the General Data Protection Regulation (GDPR).

Main benefits computer by user groups

The report looks at four main user groups and examines both the potential benefits and challenges they face in combining EHDS data with open data.

1. Patients: Informed Empowerment with Practical Barriers

European patients will gain faster and more secure access to their own electronic health records, especially in cross-border contexts thanks to infrastructures such as MyHealth@EU. This project is particularly useful for European citizens who are displaced in another European country. . 

Another interesting project that informs the public is PatientsLikeMe,  which brings together more than 850,000 patients with rare or chronic diseases in an online community that shares information of interest about treatments and other issues. 

2. Potential health professionals subordinate to integration

On the other hand, healthcare professionals will be able to access clinical patient data earlier and more easily, even across borders, improving continuity of care and the quality of diagnosis and treatment.

The combination with open data could amplify these benefits if tools are developed that integrate both sources of information directly into electronic health record systems.

3. Policymakers: data for better decisions

Public officials are natural beneficiaries of the convergence between EHDS and open data. The possibility of combining detailed health data (upon request and authorisation through the Health Data Access Bodies that each Member State must establish) with open statistical and contextual information would allow  for much more robust evidence-based policies to be developed.

The report mentions use cases such as combining health data with environmental information to assess health impacts. A real example is the French Green Data for Health project, which crosses open data on noise pollution with information on prescriptions for sleep medications from more than 10 million inhabitants, investigating correlations between environmental noise and sleep disorders.

4. Researchers and reusers: the main immediate beneficiaries

Researchers, academics and innovators are the group that will most directly benefit from the EHDS-ODD synergy as they have the skills and tools to locate, access, combine and analyse data from multiple sources. In addition, their work already routinely involves the integration of various data sets.

A recent study published in PLOS Digital Health on the case of Andalusia demonstrates how open data in health can democratize research in health AI and improve equity in treatment.

The development of EHDS is being supported by European programmes such as EU4Health, Horizon Europe and specific projects such as TEHDAS2, which help to define technical standards and pilot real applications.

1. Recommendations to maximize impact

The report concludes with four key recommendations that are particularly relevant to the Spanish open data ecosystem:

1. Stimulate research at the EHDS-open data intersection through dedicated funding. It is essential to encourage researchers who combine these sources to translate their findings into practical applications: improved clinical protocols, decision tools, updated quality standards.
2. Evaluate and facilitate direct use by professionals and patients. Promoting data literacy and developing intuitive applications integrated into existing systems (such as electronic health records) could change this.
3. Strengthen governance through education and clear regulatory frameworks. As EHDS technical entities become operationalized, clear regulation defining common regulatory frameworks will be essential.
4. Monitor, evaluate and adapt. The period 2025-2031 will see the gradual entry into force of the various EHDS requirements. Regular evaluations are recommended to assess how EHDS is actually being used, which combinations with open data are generating the most value, and what adjustments are needed.

Moreover, for all this to work, the report suggests that portals such as data.europa.eu (and by extension, datos.gob.es) should highlight practical examples that demonstrate how open data complements protected data from sectoral spaces, thus inspiring new applications.

Overall, the role of open data portals will be fundamental in this emerging ecosystem: not only as providers of quality datasets, but also as facilitators of knowledge, meeting spaces between communities and catalysts for innovation. The future of European healthcare is now being written, and open data plays a leading role in that story.

Last September, the first edition of the European Data Spaces Awards was officially launched, an initiative promoted by the Data Spaces Support Centre (DSSC) in collaboration with the European Commission. These awards were created with the aim of promoting the best data exchange initiatives, recognizing their achievements and increasing their visibility. This seeks  to promote good practices that can serve as a guide for other actors in the European data ecosystem. The idea is that the awards will be awarded annually, which will help the community grow and improve.

Why are these awards important?

Data is one of Europe's most valuable economic assets, and its strategic harnessing is critical for the development of technologies such as artificial intelligence (AI). Therefore, the European strategy It involves establishing a single market for data that allows innovation to be promoted effectively. However, at present, the data is still widely distributed among many actors in the European ecosystem.

The European Data Spaces Awards are especially relevant because they recognise and promote initiatives that help to overcome this problem: data spaces. These are organisational and technical environments where multiple actors – public and private – share data in a secure, sovereign, controlled way and in accordance with common standards that promote their interoperability. This allows data to flow across sectors and borders, driving innovation.

In Spain, the development of data spaces is also being promoted through specific initiatives such as the Plan to Promote Sectoral Data Spaces.

Two award categories

In this context, two categories of awards have been created:

1. Excellence in end-user engagement and financial sustainability: Recognizes data spaces with a strong user focus and viable long-term financial models.
2. Most innovative emerging data space: rewards new initiatives that bring fresh and innovative ideas with high impact on the European ecosystem.

Who can participate?

The European Data Spaces Awards are open to any data space that meets these criteria:

• Its governance authority is registered in the European Union.
• It operates wholly or partially within European territory.
• It is being actively used for data exchange.
• It includes restricted data, beyond open data.

 Spaces in the implementation phase can also apply, as long as they share data in pilot or pre-operational environments. In these cases, the project coordinator can act on behalf of the project.

The assessment of eligibility will be based on the applicant's self-assessment, facilitating broad and representative participation of the European data ecosystem.

The same data space can apply for both categories, although you must make two different applications.

Schedule: registration open until November 7

The competition is structured in four key phases that set the pace of the participation and evaluation process:

• On 23 September 2025, the launch event  was held and the application period was officially opened.
• The application submission phase  will run for 7 weeks, until November 7, allowing data spaces to prepare and register their proposals.
• This will be followed by the evaluation phase, which will begin on December 17 and last 6 weeks. During this time, the Data Spaces Support Centre (DSSC) will conduct an internal eligibility review and the jury selects the winners.
• Finally, the awards will be announced and presented during the Data Space Symposium (DSS2026) event, on February 10 and 11, 2026 in Madrid. All nominees will be invited to take the stage during the ceremony, so they will get great visibility and recognition. The winners will not receive any monetary compensation.

How to participate?

To register, participants must access the online form  available on the official website of the awards. This page provides all the resources needed to prepare for your application, including reference documents, templates, and updates on the process.

The form includes three required elements:

• Basic questions about the requester and the data space.
• The eligibility self-assessment with four mandatory questions.
• A space to upload the Awards Application Document, a document in PDF format and whose template is available on the platform. (maximum 8 pages). The document, which follows a structure aligned with the Maturity Model v2.0, details the objectives and evaluation criteria by section.

In addition, participants have a space to provide, optionally, links to additional resources that help give context to their proposal.

For any questions that may arise during the process, a support platform has been set up.

The European Data Spaces Awards 2025 not only recognise excellence, but also highlight the impact of projects that are transforming the future of data in Europe. If you are interested in participating, we invite you to read the complete rules of the competition on their website.

To achieve its environmental sustainability goals, Europe needs accurate, accessible and up-to-date information that enables evidence-based decision-making. The Green Deal Data Space (GDDS) will facilitate this transformation by integrating diverse data sources into a common, interoperable and open digital infrastructure.

In Europe, work is being done on its development through various projects, which have made it possible to obtain recommendations and good practices for its implementation. Discover them in this article!

What is the Green Deal Data Space?

The Green Deal Data Space (GDDS) is an initiative of the European Commission to create a digital ecosystem that brings together data from multiple sectors. It aims to support and accelerate the objectives of the Green Deal: the European Union's roadmap for a sustainable, climate-neutral and fair economy. The pillars of the Green Deal include:

• An energy transition that reduces emissions and improves efficiency.
• The promotion of the circular economy, promoting the recycling, reuse and repair of products to minimise waste.
• The promotion of more sustainable agricultural practices.
• Restoring nature and biodiversity, protecting natural habitats and reducing air, water and soil pollution.
• The guarantee of social justice, through a transition that makes it easier for no country or community to be left behind.

Through this comprehensive strategy, the EU aims to become the world's first competitive and resource-efficient economy, achieving net-zero greenhouse gas emissions by 2050. The Green Deal Data Space is positioned as a key tool to achieve these objectives. Integrated into the European Data Strategy, data spaces are digital environments that enable the reliable exchange of data, while maintaining sovereignty and ensuring trust and security under a set of mutually agreed rules.

In this specific case, the GDDS will integrate valuable data on biodiversity, zero pollution, circular economy, climate change, forest services, smart mobility and environmental compliance. This data will be easy to locate, interoperable, accessible and reusable under the FAIR (Findability, Accessibility, Interoperability, Reusability) principles.

The GDDS will be implemented through the SAGE  (Dataspace for a Green and Sustainable Europe) project and will be based on the results of the GREAT (Governance of Responsible Innovation) initiative.

A report with recommendations for the GDDS

How we saw in a previous article, four pioneering projects are laying the foundations for this ecosystem: AD4GD, B-Cubed, FAIRiCUBE and USAGE.  These projects, funded under the HORIZON call, have analysed and documented for several years the requirements necessary to ensure that the GDDS follows the FAIR principles. As a result of this work, the report "Policy Brief: Unlocking The Full Potential Of The Green Deal Data Space”. It is a set of recommendations that seek to serve as a guide to the successful implementation of the Green Deal Data Space. 

The report highlights five major areas in which the challenges of GDDS construction are concentrated: 

1. Data harmonization 

Environmental data is heterogeneous, as it comes from different sources: satellites, sensors, weather stations, biodiversity registers, private companies, research institutes, etc. Each provider uses its own formats, scales, and methodologies. This causes incompatibilities that make it difficult to compare and combine data. To fix this, it is essential to:

• Adopt existing international standards and vocabularies, such as INSPIRE, that span multiple subject areas.
• Avoid proprietary formats, prioritizing those that are open and well documented.
• Invest in tools that allow data to be easily transformed from one format to another.

2. Semantic interoperability

Ensuring semantic interoperability is crucial so that data can be understood and reused across different contexts and disciplines, which is critical when sharing data between communities as diverse as those participating in the Green Deal objectives. In addition, the Data Act requires participants in data spaces to provide machine-readable descriptions of datasets, thus ensuring their location, access, and reuse. In addition, it requires that the vocabularies, taxonomies and lists of codes used be documented in a public and coherent manner. To achieve this, it is necessary to:

• Use  linked data and metadata that offer clear and shared concepts, through vocabularies, ontologies and standards such as those developed by the OGC or ISO standards.
• Use existing standards to organize and describe data and only create new extensions when really necessary.
• Improve the already accepted international vocabularies, giving them more precision and taking advantage of the fact that they are already widely used by scientific communities.

3. Metadata and data curation

Data only reaches its maximum value if it is accompanied by clear metadata explaining its origin, quality, restrictions on use and access conditions. However, poor metadata management remains a major barrier. In many cases, metadata is non-existent, incomplete, or poorly structured, and is often lost when translated between non-interoperable standards. To improve this situation, it is necessary to:

• Extend existing metadata standards to include critical elements such as observations, measurements, source traceability, etc.
• Foster interoperability between metadata standards in use, through mapping and transformation tools that respond to both commercial and open data needs.
• Recognize and finance the creation and maintenance of metadata in European projects, incorporating the obligation to generate a standardized catalogue from the outset in data management plans.

4. Data Exchange and Federated Provisioning

The GDDS does not only seek to centralize all the information in a single repository, but also to allow multiple actors to share data in a federated and secure way. Therefore, it is necessary to strike a balance between open access and the protection of rights and privacy. This requires:

• Adopt and promote open and easy-to-use technologies that allow the integration between open and protected data, complying with the General Data Protection Regulation (GDPR).
• Ensure the integration of various APIs used by data providers and user communities, accompanied by clear demonstrators and guidelines. However, the use of standardized APIs  needs to be promoted to facilitate a smoother implementation, such as OGC (Open Geospatial Consortium) APIs for geospatial assets.
• Offer clear specification and conversion tools to enable interoperability between APIs and data formats.

In parallel to the development of the Eclipse Dataspace Connectors  (an open-source technology to facilitate the creation of data spaces), it is proposed to explore alternatives such as blockchain catalogs  or digital certificates, following examples such as the FACTS (Federated Agile Collaborative Trusted System).

5. Inclusive and sustainable governance

The success of the GDDS will depend on establishing a robust governance framework that ensures transparency, participation, and long-term sustainability. It is not only about technical standards, but also about fair and representative rules. To make progress in this regard, it is key to:

• Use only European clouds to ensure data sovereignty, strengthen security and comply with EU regulations, something that is especially important in the face of today's global challenges.
• Integrating open platforms such as Copernicus, the European Data Portal and INSPIRE into the GDDS strengthens interoperability and facilitates access to public data. In this regard, it is necessary to design effective strategies to attract open data providers and prevent GDDS from becoming a commercial or restricted environment.
• Mandating data in publicly funded academic journals increases its visibility, and supporting standardization initiatives strengthens the visibility of data and ensures its long-term maintenance.
• Providing comprehensive training and promoting cross-use of harmonization tools prevents the creation of new data silos and improves cross-domain collaboration.

The following image summarizes the relationship between these blocks: 

Conclusion

All these recommendations have an impact on a central idea: building a Green Deal Data Space that complies with the FAIR principles is not only a technical issue, but also a strategic and ethical one. It requires cross-sector collaboration, political commitment, investment in capacities, and inclusive governance that ensures equity and sustainability. If Europe succeeds in consolidating this digital ecosystem, it will be better prepared to meet environmental challenges with informed, transparent and common good-oriented decisions.

The UNE 0087 standard defines for the first time in Spain the key principles and requirements for creating and operating in data spaces

On 17 July, the UNE 0087 Specification "Definition and Characterisation of Data Spaces" was officially published, the first Spanish standard to establish a common framework for these digital environments.

This milestone has been possible thanks to the collaboration of the Data Space Reference Centre (CRED) with the Spanish Association for Standardisation (UNE). This regulation, which was approved on June 20, 2025, defines three key pillars in adherence to data spaces: interoperability, governance and value creation, with the aim of offering legal certainty, trust and a common technical language in the data economy.

For its creation, three working groups have been formed with more than 50 participants from both public and private entities who have contributed their knowledge to define the principles and key characteristics of these collaborative systems. These working groups have been coordinated as follows:

• WG1: Definition of Data Spaces and Maturity Model.
• WG2: Technical and Semantic Interoperability.
• WG3: Legal and organisational interoperability.

The publication of this regulation is, therefore, a reference document for the creation of secure and reliable data spaces, applicable in all productive sectors and which serves as a basis for future guide documents.

In this way, to offer guidelines that facilitate the implementation and development of data spaces, the UNE 0087:2025 specification was created to create an inclusive framework of reference that guides organizations so that they can take advantage of all the information in an environment of regulatory compliance and digital sovereignty. The publication of this regulation has a number of benefits:

• Accelerate the deployment of data spaces across all sectors of the economy.
• Supporting sustainability and scaling/growth of data-sharing ecosystems.
• Promoting public/private collaboration, ensuring convergence with Europe.
• Move towards technological autonomy and data sovereignty in ecosystems.
• Promote the discovery of new innovative business opportunities by fostering collaboration and the creation of strategic alliances.

Within the specification, data spaces are defined, their key characteristics of interoperability, governance and value generation are established and the benefits of their adhesion are determined. The specification is published here and it is important to add that, although there is a download cost, it is free of charge, thanks to the sponsorship of the General Directorate of Data.

With this tool, Spain takes a firm step in the consolidation of cohesive, secure data spaces aligned with the European framework, facilitating the implementation of cross-cutting projects in different sectors.

Spain is taking a key step towards the data economy with the launch of the Data Spaces Kit, an aid programme that will subsidise the integration of public and private entities in sectoral data spaces.

Data spaces are secure ecosystems in which organizations, both public and private, share information in an  interoperable way, under common rules and with privacy guarantees. These allow new products to be developed, decision-making to be improved and operational efficiency to be increased, in sectors such as health, mobility or agri-food, among others.

Today, the Ministry for Digital Transformation and Public Function, through the Secretary of State for Digitalisation and Artificial Intelligence, has published in the Official State Gazette the rules governing the granting of aid to entities interested in effectively joining a data space.

This programme, which is called the "Data Spaces Kit", will be managed by Red.es and will subsidise the costs incurred by the beneficiary entities to achieve their incorporation into an eligible data space, i.e. one that meets the requirements set out in the bases, from the day of their publication.

Recipients and Funding

This aid plan is aimed at both public and private entities, as well as Public Administrations. Among the beneficiaries of these grants are the participants, which are those entities that seek to integrate into these ecosystems to share and take advantage of data and services.

For the execution of this plan, the Government has launched aid of up to 60 million euros that will be distributed, depending on the type of entity or the level of integration as follows:

• Private and public entities with economic activity will have an aid of up to €15,000 under the effective incorporation regime or up to €30,000 if they join as a supplier.
• On the other hand, Public Administrations will have funding of up to €25,000 if they are effectively incorporated, or up to €50,000 if they do so as a supplier.

The incorporation of companies from different sectors in the data spaces will generate benefits both at the business level and for the national economy, such as increasing the innovation capacity of the beneficiary companies, the creation of new products and services based on data analysis and the improvement of operational efficiency and decision-making.

The call is expected to be published during the fourth quarter of 2025. The subsidies will be applied for on a non-competitive basis, on a first-come, first-served basis and until the available funds are exhausted.

The publication of these regulatory bases in the Official State Gazette (BOE) aims to boost the data ecosystem in Spain, strengthen the competitiveness of the economy at the global level and consolidate the financial sustainability of innovative business models.

More information:

Regulatory bases in the BOE.

Data Space Reference Center LinkedIn page.

Just a few days ago, the Directorate General of Traffic published the new Framework Programme for the Testing of Automated Vehicles which, among other measures, contemplates "the mandatory delivery of reports, both periodic and final and in the event of incidents, which will allow the DGT to assess the safety of the tests and publish basic information [...] guaranteeing transparency and public trust."

The advancement of digital technology is making it easier for the transport sector to face an unprecedented revolution in autonomous vehicle driving, offering significant improvements in road safety, energy efficiency and mobility accessibility.

The final deployment of these vehicles depends to a large extent on the availability, quality and accessibility of large volumes of data, as well as on an appropriate legal framework that ensures the protection of the various legal assets involved (personal data, trade secrets, confidentiality, etc.), traffic security and transparency. In this context, open data and the reuse of public sector information are essential elements for the responsible development of autonomous mobility, in particular when it comes to ensuring adequate levels of traffic safety.

Data Dependency on Autonomous Vehicles

The technology that supports autonomous vehicles is based on the integration of a complex network of advanced sensors, artificial intelligence systems and real-time processing algorithms, which allows them to identify obstacles, interpret traffic signs, predict the behavior of other road users and, in a collaborative way, plan routes completely autonomously.

In the autonomous vehicle ecosystem, the availability of quality open data is strategic for:

• Improve road safety, so that real-time traffic data can be used to anticipate dangers, avoid accidents and optimise safe routes based on massive data analysis.
• Optimise operational efficiency, as access to up-to-date information on the state of roads, works, incidents and traffic conditions allows for more efficient planning of journeys.
• To promote sectoral innovation, facilitating the creation of new digital tools that facilitate mobility.

Specifically, ensuring the safe and efficient operation of this mobility model requires continuous access to two key categories of data:

• Variable or dynamic data, which offers constantly changing information such as the position, speed and behaviour of other vehicles, pedestrians, cyclists or weather conditions in real time.
• Static data, which includes relatively permanent information such as the exact location of traffic signs, traffic lights, lanes, speed limits or the main characteristics of the road infrastructure.

The prominence of the data provided by public entities

The sources from which such data come are certainly diverse. This is of great relevance as regards the conditions under which such data will be available. Specifically, some of the data are provided by public entities, while in other cases the origin comes from private companies (vehicle manufacturers, telecommunications service providers, developers of digital tools...) with their own interests or even from people who use public spaces, devices and digital applications.

This diversity requires a different approach to facilitating the availability of data under appropriate conditions, in particular because of the difficulties that may arise from a legal point of view. In relation to Public Administrations, Directive (EU) 2019/1024 on open data and the reuse of public sector information establishes clear obligations that would apply, for example, to the Directorate General of Traffic, the Administrations owning public roads or municipalities in the case of urban environments. Likewise, Regulation (EU) 2022/868 on European data governance reinforces this regulatory framework, in particular with regard to the guarantee of the rights of third parties and, in particular, the protection of personal data.

Moreover, some datasets should be provided under the conditions established for dynamic data, i.e. those "subject to frequent or real-time updates, due in particular to their volatility or rapid obsolescence", which should be available "for re-use immediately after collection, through appropriate APIs and,  where appropriate, in the form of a mass discharge."

One might even think that the high-value data category  is of particular interest in the context of autonomous vehicles given its potential to facilitate mobility, particularly considering its potential to:

• To promote technological innovation, as they would make it easier for manufacturers, developers and operators to access reliable and up-to-date information, essential for the development, validation and continuous improvement of autonomous driving systems.
• Facilitate monitoring and evaluation from a security perspective, as transparency and accessibility of such data are essential prerequisites from this perspective.
• To boost the development of advanced services, since data on road infrastructure, signage, traffic and even the results of tests carried out in the context of the aforementioned Framework Programme constitute the basis for new mobility applications and services that benefit society as a whole.

However, this condition is not expressly included for traffic-related data in the definition made at European level, so that, at least for the time being, public entities should not be required to disseminate the data that apply to autonomous vehicles under the unique conditions established for high-value data. However, at this time of transition for the deployment of autonomous vehicles, it is essential that public administrations publish and keep updated under appropriate conditions for their automated processing, some datasets, such as those relating to:

• Road signs and vertical signage elements.
• Traffic light states and traffic control systems.
• Lane configuration and characteristics.
• Information on works and temporary traffic alterations.
• Road infrastructure elements critical for autonomous navigation.

The recent update of the official catalogue of traffic signs, which comes into force on 1 July 2025, incorporates signs adapted to new realities, such as personal mobility. However, it requires greater specificity with regard to the availability of data relating to signals under these conditions. This will require the intervention of the authorities responsible for road signage.

The availability of data in the context of the European Mobility Area

Based on these conditions and the need to have mobility data generated by private companies and individuals, data spaces appear as the optimal legal and governance environment to facilitate their accessibility under appropriate conditions.

In this regard, the initiatives for the deployment of the European Mobility Data Space, created in 2023, constitute an opportunity to integrate into its design and configuration measures that support the need for access to data required by autonomous vehicles. Thus, within the framework of this initiative, it would be possible to unlock the potential of mobility data , and in particular:

• Facilitate the availability of data under conditions specific to the needs of autonomous vehicles.
• Promote the interconnection of various data sources linked to existing means of transport, but also emerging ones.
• Accelerate the digital transformation of autonomous vehicles.
• Strengthen the digital sovereignty of the European automotive industry, reducing dependence on large foreign technology corporations.

In short, autonomous vehicles can represent a fundamental transformation in mobility as it has been conceived until now, but their development depends, among other factors, on the availability, quality and accessibility of sufficient and adequate data. The Sustainable Mobility Bill currently being processed in Parliament is a great opportunity to strengthen the role of data in facilitating innovation in this area, which would undoubtedly favour the development of autonomous vehicles. To this end, it will be essential, on the one hand, to have a data sharing environment that makes access to data compatible with the appropriate guarantees for fundamental rights and information security; and, on the other hand, to design a governance model that, as emphasised in the Programme promoted by the Directorate-General for Traffic,  facilitates the collaborative participation of "manufacturers, developers, importers and fleet operators established in Spain or the European Union", which poses significant challenges in the availability of data.

Content prepared by Julián Valero, Professor at the University of Murcia and Coordinator of the Research Group "Innovation, Law and Technology" (iDerTec). The contents and points of view reflected in this publication are the sole responsibility of its author.

Data is a fundamental resource for improving our quality of life because it enables better decision-making processes to create personalised products and services, both in the public and private sectors. In contexts such as health, mobility, energy or education, the use of data facilitates more efficient solutions adapted to people's real needs. However, in working with data, privacy plays a key role. In this post, we will look at how data spaces, the federated computing paradigm and federated learning, one of its most powerful applications, provide a balanced solution for harnessing the potential of data without compromising privacy. In addition, we will highlight how federated learning can also be used with open data to enhance its reuse in a collaborative, incremental and efficient way.

Privacy, a key issue in data management

As mentioned above, the intensive use of data requires increasing attention to privacy. For example, in eHealth, secondary misuse of electronic health record data could violate patients' fundamental rights. One effective way to preserve privacy is through data ecosystems that prioritise data sovereignty, such as data spaces. A dataspace is a federated data management system that allows data to be exchanged reliably between providers and consumers. In addition, the data space ensures the interoperability of data to create products and services that create value. In a data space, each provider maintains its own governance rules, retaining control over its data (i.e. sovereignty over its data), while enabling its re-use by consumers. This implies that each provider should be able to decide what data it shares, with whom and under what conditions, ensuring compliance with its interests and legal obligations.

Federated computing and data spaces

Data spaces represent an evolution in data management, related to a paradigm called federated computing, where data is reused without the need for data flow from data providers to consumers. In federated computing, providers transform their data into privacy-preserving intermediate results so that they can be sent to data consumers. In addition, this enables other Data Privacy-Enhancing Technologies(Privacy-Enhancing Technologies)to be applied. Federated computing aligns perfectly with reference architectures such as Gaia-X and its Trust Framework, which sets out the principles and requirements to ensure secure, transparent and rule-compliant data exchange between data providers and data consumers.

Federated learning

One of the most powerful applications of federated computing is federated machine learning ( federated learning), an artificial intelligence technique that allows models to be trained without centralising data. That is, instead of sending the data to a central server for processing, what is sent are the models trained locally by each participant.

These models are then combined centrally to create a global model. As an example, imagine a consortium of hospitals that wants to develop a predictive model to detect a rare disease. Every hospital holds sensitive patient data, and open sharing of this data is not feasible due to privacy concerns (including other legal or ethical issues). With federated learning, each hospital trains the model locally with its own data, and only shares the model parameters (training results) centrally. Thus, the final model leverages the diversity of data from all hospitals without compromising the individual privacy and data governance rules of each hospital.

Training in federated learning usually follows an iterative cycle:

1. A central server starts a base model and sends it to each of the participating distributed nodes.
2. Each node trains the model locally with its data.
3. Nodes return only the parameters of the updated model, not the data (i.e. data shuttling is avoided).
4. The central server aggregates parameter updates, training results at each node and updates the global model.
5. The cycle is repeated until a sufficiently accurate model is achieved.

Figure 1. Visual representing the federated learning training process. Own elaboration

This approach is compatible with various machine learning algorithms, including deep neural networks, regression models, classifiers, etc.

Benefits and challenges of federated learning

Federated learning offers multiple benefits by avoiding data shuffling. Below are the most notable examples:

1. Privacy and compliance: by remaining at source, data exposure risks are significantly reduced and compliance with regulations such as the General Data Protection Regulation (GDPR) is facilitated.
2. Data sovereignty: Each entity retains full control over its data, which avoids competitive conflicts.
3. Efficiency: avoids the cost and complexity of exchanging large volumes of data, speeding up processing and development times.
4. Trust: facilitates frictionless collaboration between organisations.

There are several use cases in which federated learning is necessary, for example:

• Health: Hospitals and research centres can collaborate on predictive models without sharing patient data.
• Finance: banks and insurers can build fraud detection or risk-sharing analysis models, while respecting the confidentiality of their customers.
• Smart tourism: tourist destinations can analyse visitor flows or consumption patterns without the need to unify the databases of their stakeholders (both public and private).
• Industry: Companies in the same industry can train models for predictive maintenance or operational efficiency without revealing competitive data.

While its benefits are clear in a variety of use cases, federated learning also presents technical and organisational challenges:

• Data heterogeneity: Local data may have different formats or structures, making training difficult. In addition, the layout of this data may change over time, which presents an added difficulty.
• Unbalanced data: Some nodes may have more or higher quality data than others, which may skew the overall model.
• Local computational costs: Each node needs sufficient resources to train the model locally.
• Synchronisation: the training cycle requires good coordination between nodes to avoid latency or errors.

Beyond federated learning

Although the most prominent application of federated computing is federated learning, many additional applications in data management are emerging, such as federated data analytics (federated analytics). Federated data analysis allows statistical and descriptive analyses to be performed on distributed data without the need to move the data to the consumers; instead, each provider performs the required statistical calculations locally and only shares the aggregated results with the consumer according to their requirements and permissions. The following table shows the differences between federated learning and federated data analysis.

Figure 1. Comparative table. Source: own elaboration

Federated learning and open data: a symbiosis to be explored

In principle, open data resolves privacy issues prior to publication, so one would think that federated learning techniques would not be necessary. Nothing could be further from the truth. The use of federated learning techniques can bring significant advantages in the management and exploitation of open data. In fact, the first aspect to highlight is that open data portals such as datos.gob.es or data.europa.eu are federated environments. Therefore, in these portals, the application of federated learning on large datasets would allow models to be trained directly at source, avoiding transfer and processing costs. On the other hand, federated learning would facilitate the combination of open data with other sensitive data without compromising the privacy of the latter. Finally, the nature of a wide variety of open data types is very dynamic (such as traffic data), so federated learning would enable incremental training, automatically considering new updates to open datasets as they are published, without the need to restart costly training processes.

Federated learning, the basis for privacy-friendly AI

Federated machine learning represents a necessary evolution in the way we develop artificial intelligence services, especially in contexts where data is sensitive or distributed across multiple providers. Its natural alignment with the concept of the data space makes it a key technology to drive innovation based on data sharing, taking into account privacy and maintaining data sovereignty.

As regulation (such as the European Health Data Space Regulation) and data space infrastructures evolve, federated learning, and other types of federated computing, will play an increasingly important role in data sharing, maximising the value of data, but without compromising privacy. Finally, it is worth noting that, far from being unnecessary, federated learning can become a strategic ally to improve the efficiency, governance and impact of open data ecosystems.

Jose Norberto Mazón, Professor of Computer Languages and Systems at the University of Alicante. The contents and views reflected in this publication are the sole responsibility of the author.

Today's industry is facing one of the biggest challenges in its recent history. Market demands, pressure to meet climate targets, consumer demand for transparency and technological acceleration are converging in a profound transformation of the production model. This transformation is not only aimed at greater competitiveness, but also at more resilient, flexible, efficient and sustainable production.

In this context, industrial digitisation - driven by technologies such as the Internet of Things (IoT), artificial intelligence, edge computing, or cyber-physical systems - is generating massive amounts of operational, environmental and logistical data. However, the mere existence of this data does not in itself guarantee value. The key is to govern it properly, i.e. to establish principles, processes, roles and technologies that ensure that this data is reliable, accessible, useful and secure. In other words, that the data is fit to be harnessed to improve industrial processes.

This is why industrial data governance is positioned as a strategic factor. It is not just a matter of ‘having data’, but of turning it into a critical infrastructure for decision-making, resource optimisation, intelligent automation and ecological transition. Without data governance, there is no Industry 4.0. And without Industry 4.0, the challenges of sustainability, energy efficiency or full traceability are intractable.

In this article we explore why data governance is essential in industry, what concrete benefits it brings to production processes and how initiatives such as the National Industry Data Space can accelerate this transformation.
 We then analyse its impact at different levels, from the most relevant use cases to the collaborative frameworks that are emerging in Spain.

Why is data governance key in industry?

Industrial data comes from a multitude of distributed sources: IoT sensors, SCADA systems, automated assembly lines, maintenance platforms, ERP or Manufacturing Execution Systems (MES), among others. This heterogeneity, if not properly managed, can become a barrier to the integration and useful analysis of information.

Data governance overcomes these barriers by establishing the rules of the game for data capture, storage, quality, interoperability, use, protection and disposal. This enables not only operational efficiency but also long-term sustainability. How?

• Reducing operational inefficiencies: by having accurate, up-to-date and well-integrated data between systems, tasks can be automated, rework avoided, and unplanned downtime reduced. For example, a plant can adjust the speed of its production lines in real time based on the analysis of performance and demand data.
• Improving sustainability: robust data management can identify patterns of energy consumption, materials or emissions. With this information, processes can be redesigned to be more sustainable, eco-design can be applied and the environmental footprint can be reduced. Data, in this case, acts as a compass towards decarbonisation.
• Ensuring regulatory compliance and traceability: from ISO 9001 to the new circular economy regulations or the Digital Product Passport, industries must demonstrate compliance. This is only possible with reliable, traceable and auditable data.
• Facilitating interoperability between systems: data governance acts as the ‘glue’ that binds together the different technological silos of an organisation: quality, production, logistics, maintenance, purchasing, etc. The standardisation and semantic alignment of data allows for more agile flows and better informed decisions.
• Boosting the circular economy: thanks to the full traceability of a product's life cycle, from design to recycling, it is possible to identify opportunities for reuse, material recovery and sustainable design. This is supported by data that follows the product throughout its life.

What should data governance look like in the industrial environment?

A data governance model adapted to this context should include:

▸Specific roles: it is necessary to have a defined team, where everyone's responsibility and tasks are clear. Some of the roles that cannot be missing are:

• Data owners: responsible for the use of the data in their area (production, quality, maintenance...).
• Data stewards: ensure the consistency, completeness and accuracy of the information.
• Data governance team: coordinates the strategy, defines common policies and evaluates compliance.

▸Structured processes: Like the roles, it is necessary to define the various phases and operations to be carried out. These include the following:

• Classification and cataloguing of data assets (by type, criticality, use).
• Data quality control: definition of validation rules, cleaning of duplicates, exception management.
• Data life cycle: from its creation on the machine to its archiving or destruction.
• Access and security: privilege management, usage audits, traceability.

▸Organisational policies: to ensure interoperability and data quality it is necessary to have standards, norms and guidelines to guide users. Some examples are:

• Standards for nomenclature, formats, encoding and synchronisation.
• Standards for interoperability between systems (e.g. use of standards such as OPC UA or ISA-95).
• Guidelines for ethical and legally compliant use (such as Data Regulation, GDPR or environmental legislation).

This approach makes industrial data an asset managed with the same rigour as any physical infrastructure.

Industrial use cases enabled by data governance

The benefits of data governance in industry are realised in multiple practical applications. Some of the most representative use cases are:

1.Predictive maintenance

One of the great classics of Industry 4.0. By combining historical maintenance data with real-time sensors, organisations can anticipate machine failures and avoid unexpected downtime. But this is only possible if the data is governed: if its capture frequency, format, responsible parties, quality and availability have been defined.

2. Complete product traceability

From raw material to end customer, every event in the value chain is recorded and accessible. This is vital for sectors such as food, automotive or pharmaceuticals, where traceability is both an added value and a regulatory obligation. Data governance ensures that this traceability is not lost, is verifiable and meets the required interoperability standards.

3. Digital twins and process simulation

For a digital twin - a virtual replica of a physical process or system - to work, it needs to be fed with accurate, up-to-date and consistent data. Data governance ensures synchronisation between the physical and virtual worlds, and allows the generation of reliable simulation scenarios, from the design of a new production line to the optimisation of the factory layout, i.e. of the different elements within the plant.

4. Energy monitoring and emission control

Real-time monitoring of energy, water or gas consumption can reveal hidden inefficiencies and opportunities for savings. Through intelligent dashboards and KPIs defined on governed data, industrial plants can reduce their costs and advance their environmental sustainability goals.

5. Automation and intelligent quality control

Machine vision systems and machine learning algorithms trained with production data allow to detect defects in real time, adjust parameters automatically and improve final quality. Without good data quality (accuracy, completeness, consistency), these algorithms can fail or generate unreliable results.

The National Industry Data Space: key to collaboration and competitiveness

For industrial data governance to transcend the scope of each company and become a real lever for sectoral transformation, it is necessary to have infrastructures that facilitate the secure, reliable and efficient sharing of data between organisations. The National Data Space for Industry, framed within the Plan for the Promotion of Sectoral Data Spaces promoted by the Ministry for Digital Transformation and the Civil Service, is in this line.

This space aims to create an environment of trust where companies, associations, technology centres and administrations can share and reuse industrial data in an interoperable manner, in accordance with ethical, legal and technical principles. Through this framework, the aim is to enable new forms of collaboration, accelerate innovation and reinforce the strategic autonomy of the national productive fabric.

The industrial sector in Spain is enormously diverse, with an ecosystem made up of large corporations, SMEs, suppliers, subcontractors, clusters and R&D centres. This diversity can become a strength if it is articulated through a common data infrastructure that facilitates the integration and exchange of information in an orderly and secure manner. Moreover, these industrial data can be complemented with open data published by public bodies, such as those available in the National Catalogue of Open Data, thus extending the value and possibilities of reuse for the sector as a whole.

The strengths of this common infrastructure allow:

• Detect synergies along the value chain, such as industrial recycling opportunities between different sectors (e.g. plastic waste from one chemical industry as raw material in another).
• Reducing entry barriers to digitisation, especially for SMEs that do not have the resources to deploy advanced data analytics solutions, but could access shared services or data within the space.
• Encourage open innovation models where companies share data in a controlled way for the joint development of solutions based on artificial intelligence or predictive maintenance.
• Promote sectoral aggregate indicators, such as shared carbon footprints, energy efficiency levels or industrial circularity indices, which allow the country as a whole to make more coordinated progress towards sustainability and competitiveness objectives.

The creation of the National Industrial Data Space can be a true lever for modernization for the Spanish industrial fabric:

• Increased international competitiveness, by facilitating compliance with European market requirements, such as the Data Regulation, the Digital Product Passport, and sustainability standards.
• Regulatory agility and improved traceability, allowing industries to respond quickly to audits, certifications, or regulatory changes.
• Proactive capacity, thanks to the joint analysis of production, consumption, or market data that allows for the prediction of disruptions in supply chains or the demand for critical resources.
• Creation of new business models, based on the provision of products as a service, the reuse of materials, or the shared leasing of industrial capacities.

The deployment of this national data space not only seeks to improve the efficiency of industrial processes. It also aims to strengthen the country's technological and data sovereignty, enabling a model where the value generated by data remains within the companies, regions, and sectors themselves. In this sense, the National Industrial Data Space aligns with European initiatives such as GAIA-X and Manufacturing-X, but with an approach adapted to the context and needs of the Spanish industrial ecosystem.

Conclusions

Data governance is a fundamental pillar for the industry to move toward more efficient, sustainable, and resilient models. Having large volumes of information is not enough: it must be managed properly to generate real value.

The benefits are clear: operational optimization, improved traceability, a boost to the circular economy, and support for technologies such as artificial intelligence and digital twins. But the real leap forward comes when data is no longer managed in isolation and becomes part of a shared ecosystem.

The National Industrial Data Space offers this framework for collaboration and trust, facilitating innovation, competitiveness, and technological sovereignty. Investing in its development means investing in a more connected, intelligent industry that is prepared for the challenges of the future.

Content prepared by Dr. Fernando Gualo, Professor at UCLM and Data Governance and Quality Consultant. The content and point of view reflected in this publication are the sole responsibility of its author.

With 24 official languages and more than 60 regional and minority languages, the European Union is proud of its cultural and linguistic diversity. However, this richness also represents a significant challenge in the digital and technological sphere. Advances in artificial intelligence (AI) and natural language processing have been dominated by English, creating a noticeable imbalance in the availability of language resources for most European languages.

This imbalance has direct consequences, for example:

• Asymmetric technology development: Companies and researchers have difficulty creating AI solutions adapted to specific languages because resources are limited.
• Technological dependence: Europe risks becoming dependent on language solutions developed outside its cultural and normative context.

Addressing this gap is not only a matter of inclusion, but also represents a large-scale economic opportunity, capable of generating huge gains in both trade and technological innovation. To address these challenges, the European Commission has launched the European Language Data Space (LDS), a decentralised infrastructure that promotes the secure and controlled exchange of language data among multiple actors in the European ecosystem.

Unlike a simple centralised repository, the LDS functions as a language data marketplace that allows participants to share, sell or license their data under clearly defined conditions and with full control over the use of the data.

The European Language Data Space (LDS), with a beta version operational, represents a decisive step towards democratising language technologies across all languages of the European Union. We tell you the keys to this project and the next steps.

How does this platform work?

LDS is based on a decentralised peer-to-peer (P2P) architecture that allows users to interact directly with each other, without the need for a central server or single authority, where each participant maintains control of its own data. The key elements of LDS operation are:

1. Decentralised and sovereign architecture

Each participant (whether data provider or data consumer) can locally install the LDS Connector, a software that allows interacting directly with other participants without the need for a central server.. This approach ensures:

•  Data sovereignty: owners retain full control over who can access their data and under what conditions of use.

• Trust and security: Only eligible and authorised participants, legal entities registered in the EU, can be part of the ecosystem.

• Interoperability: is compatible with other European data spaces, following common standards.

2. Data exchange flow

The exchange process follows a structured flow between two main actors:

• The providers describe their linguistic datasets, establish access policies (licences, prices) and publish these offers in the catalogue.
• The consumers explore the catalogue, identify resources of interest and, through their connectors, initiate negotiations on the terms of use.

If both parties reach an agreement, a contract is established and the data transfer takes place securely between the connectors.

3 Supporting infrastructure

Although the exchange is decentralised, the LDS includes supporting elements such as:

• Participant registration: ensures that only verified entities participate in the ecosystem.

• Optional catalogue: facilitates the publication and discovery of available resources

• Hub of vocabularies: is a service that centralises controlled vocabularies, and allows maintaining lists of values, definitions, relationships between terms, mappers between lists, etc.

• Monitoring service: allows you to monitor the overall operation of the system.

Added value for the European data ecosystem

The LDS brings significant benefits to the European digital landscape:

• Boosting multilingual AI

By facilitating access to quality linguistic data in all European languages, the LDS contributes directly to the development of more inclusive AI models adapted to Europe's multilingual reality.  This is especially relevant at a time when large language models (LLMs) are transforming human-machine interaction.

• Strengthening the data economy

It is estimated that true digital language integration could generate enormous economic benefits in both trade and technological innovation. The LDS creates a marketplace where language data becomes valuable by incentivising its collection, processing and availability under fair and transparent conditions.

• Preservation of linguistic diversity

By promoting technological development in all European languages, the LDS contributes to preserving and revitalising the continent's linguistic heritage, ensuring that no language is left behind in the digital revolution.

• The crucial role of industry and public administrations

The success of the LDS depends crucially on the active participation of various actors:

• Fresh, quality data

The platform seeks to attract especially "fresh" data from the industry (media, publishing, customer services) and the public sector, necessary to train and improve current language models. They are particularly valued:

• Multimodal data (text, audio, video).

• Specific content from various professional domains.

• Up-to-date and relevant language resources.

• Participation open to all ecosystem actors

The LDS is designed to be inclusive, allowing both private organisations and public entities to participate, as long as they are legal entities registered in the EU. Both types of organisations can act as providers and/or consumers of data.

Participation is formalised through a validation process by the governance board, ensuring that all eligible organisations can benefit from this common language data marketplace.

How can you take part?

The beta version of the LDS is now operational and open to new participants. Organisations interested in participating in this initiative can:

1. Join the test and focus groups: to contribute to the development and improvement of the platform, here.
2. Testing the LDS connector: experimenting with the technology in controlled environments.
3. Provide technical feedback : helping to define key aspects such as metadata, licensing or exchange mechanisms.
4. Identify relevant data: assessing which language resources could be shared through the platform.

The future of the LDS

While LDS currently focuses on data exchange, its medium-term vision envisages the possibility of integrating language services and AI model hosting within the same ecosystem, thus reinforcing Europe's role in the development of language technologies . A pre-final version of LDS is expected to be available in July 2025 and the finalised version of LDS is expected in January 2026.

All these aspects were discussed at a free online seminar held by the European open data portal "Data spaces: experience from the European Language Data Space".  You can go back to watch the webinar here.

In a global context where technological sovereignty has become a strategic priority, the European Language Data Space represents a decisive step towards ensuring that the AI revolution does not leave Europe's linguistic richness behind.

Access to financial and banking data is revolutionising the sector, promoting transparency, financial inclusion and innovation in economic services. However, the management of this data faces regulatory challenges in balancing openness with security and privacy.

For this reason, there are different ways of accessing this type of data, as we will see below.

Open Banking and Open Finance versus Open Data.

These terms, although related, have important differences.

The term Open Banking refers to a system that allows banks and other financial institutions to securely and digitally share customer financial data with third parties. This requires the customers' express approval of the data sharing conditions . This consent can be cancelled at any time according to the customer's wishes.

Open Finance, on the other hand, is an evolution of Open Banking which embraces a broader range of financial products and services. When we talk about Open Finance, in addition to banking data, data on insurance, pensions, investments and other financial services are included.

In both Open Banking and Open Finance, the data is not open (Open Data), but can only be accessed by those previously authorised by the customer. The exchange of data is done through an application programming interface or API , which guarantees the agility and security of the process. All of this is regulated by the European directive on payment services in the internal market (known as PSD2), although the European Commission is working on updating the regulatory framework.

• Applications of Open Banking and Open Finance:

The purpose of these activities is to provide access to new services based on information sharing. For example, they facilitate the creation of apps that unify access to all the bank accounts of a customer, even if they are from different providers. This improves the management and control of income and expenditure by providing an overview in a single environment.

Another example of use is that they allow providers to cross-check information more quickly. For example, by allowing access to a customer's financial data, a dealer could provide information on financing options more quickly.

Open data platforms on banking

While private banking data, like all types of personal data, is strictly regulated and cannot be openly published due to privacy protection regulations, there are sets of financial data that can be freely shared. For example, aggregate information on interest rates, economic indicators, historical stock market data, investment trends and macroeconomic statistics, which are accessible through open sources.

This data, in addition to boosting transparency and confidence in markets, can be used to monitor economic trends, prevent fraud and improve risk management globally. In addition, fintechcompanies, developers and entrepreneurs can take advantage of them to create solutions such as financial analysis tools, digital payment systems or automated advice.

Let's look at some examples of places where open data on the banking and financial sector can be obtained.

International sources

Some of the most popular international sources are:

• European Central Bank: provides statistics and data on euro area financial markets, through various platforms. Among other information, users can download datasets on inflation, bank interest rates, balance of payments, public finances, etc.

• World Bank: provides access to global economic data on financial development, poverty and economic growth.

• International Monetary Fund: provides simplified access to macroeconomic and financial data, such as the outlook for the global or regional economy. It also provides open data from reports such as its Fiscal Monitor, which analyses the latest developments in public finances.

• Federal Reserve Economic Data (FRED): focuses on US economic data, including market indicators and interest rates. This repository is created and maintained by the Research Department of the Federal Reserve Bank of St. Louis.

National sources

Through the National Open Data Catalogue of datos.gob.es a large number of datasets related to the economy can be accessed. One of the most prominent publishers is the Instituto Nacional de Estadística (INE), which provides data on defaults by financial institution, mortgages, etc.

In addition, the Banco de España offers various products for those interested in the country's economic data:

• Statistics: the Banco de España collects, compiles and publishes a wide range of economic and financial statistics. It includes information on interest and exchange rates, financial accounts of the institutional sectors, balances of payments and even household financial surveys, among others.
• Dashboard: the Banco de España has also made available to the public an interactive viewer that allows quarterly and annual data on external statistics to be consumed in a more user-friendly way.

In addition, Banco de España has set up asecure room for researchers to access  data that is valuable but cannot be opened to the general public due to its nature. In this sense we find:

• BELab: the secure data laboratory managed by the Banco de España, offering on-site (Madrid) and remote access. These data have been used in various projects.
• ES_DataLab: restricted microdata laboratory for researchers developing projects for scientific and public interest purposes. In this case, it brings together micro-data from various organisations, including the Bank of Spain.

Data spaces: an opportunity for secure and controlled exchange of financial data

As we have just seen, there are also options to facilitate access to financial and banking data in a controlled and secure manner. This is where data spaces come into play, an ecosystem where different actors share data in a voluntary and secure manner, following common governance, regulatory and technical mechanisms.

In this respect, Europe is pushing for a European Financial Data Facility (EEDF), a key initiative within the European Data Strategy. The EEDF consists of three main pillars:

• Public reporting data ("public disclosures"): collects financial reporting data (balance sheets, revenues, income statements), which financial firms are required by law to disclose on a regular basis. In this area is the European Single Access Point (ESAP)initiative, a centralised platform for accessing data from over 200 public reports from more than 150,000 companies.

• Private customer data of financial service providers: encompasses those data held by financial service providers such as banks. In this area is the framework for access to financial data, which covers data such as investments, insurance, pensions, loans and savings.

• Data from supervisory reports: for this type of data, the supervisory strategy, which covers data from different sectors (banks, insurance, pension funds...) has to be taken into account in order to promote digital transformation in the financial sector.

In conclusion, access to financial and banking data is evolving significantly thanks to various initiatives that have enabled greater transparency and that will encourage the development of new services, while ensuring the security and privacy of shared data. The future of the financial sector will be shaped by the ability of institutions and regulators to foster data ecosystems that drive innovation and trust in the market.