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 On 19 November, the European Commission presented the Data Union Strategy, a roadmap that seeks to consolidate a robust, secure and competitive European data ecosystem. This strategy is built around three key pillars: expanding access to quality data for artificial intelligence and innovation, simplifying the existing regulatory framework, and protecting European digital sovereignty. In this post, we will explain each of these pillars in detail, as well as the implementation timeline of the plan planned for the next two years.

Pillar 1: Expanding access to quality data for AI and innovation

The first pillar of the strategy focuses on ensuring that companies, researchers and public administrations have access to high-quality data that allows the development of innovative applications, especially in the field of artificial intelligence. To this end, the Commission proposes a number of interconnected initiatives ranging from the creation of infrastructure to the development of standards and technical enablers. A series of actions are established as part of this pillar: the expansion of common European data spaces, the development of data labs, the promotion of the Cloud and AI Development Act, the expansion of strategic data assets and the development of facilitators to implement these measures.

1.1 Extension of the Common European Data Spaces (ECSs)

Common European Data Spaces are one of the central elements of this strategy:

• Planned investment: 100 million euros for its deployment.

• Priority sectors: health, mobility, energy, (legal) public administration and environment.

• Interoperability: SIMPL is committed  to interoperability between data spaces with the support of the European Data Spaces Support Center (DSSC).

• Key Applications:

  • European Health Data Space (EHDS): Special mention for its role as a bridge between health data systems and the development of AI.

  • New Defence Data Space: for the development of state-of-the-art systems, coordinated by the European Defence Agency.

1.2 Data Labs: the new ecosystem for connecting data and AI development

The strategy proposes to use Data Labs as points of connection between the development of artificial intelligence and European data.

These labs employ data pooling, a process of combining and sharing public and restricted data from multiple sources in a centralized repository or shared environment. All this facilitates access and use of information. Specifically, the services offered by Data Labs are:

• Makes it easy to access data.

• Technical infrastructure and tools.

• Data pooling.

• Data filtering and labeling 

• Regulatory guidance and training.

• Bridging the gap between data spaces and AI ecosystems.

Implementation plan:

• First phase: the first Data Labs will be established within the framework of AI Factories (AI gigafactories), offering data services to connect AI development with European data spaces.

• Sectoral Data Labs: will be established independently in other areas to cover specific needs, for example, in the energy sector.

• Self-sustaining model: It is envisaged that the Data Labs model  can be deployed commercially, making it a self-sustaining ecosystem that connects data and AI.

1.3 Cloud and AI Development Act: boosting the sovereign cloud

To promote cloud technology, the Commission will propose this new regulation in the first quarter of 2026. There is currently an open public consultation in which you can participate here.

1.4 Strategic data assets: public sector, scientific, cultural and linguistic resources

On the one hand, in 2026 it will be proposed to expand the list of high-value data  in English or HVDS to include legal, judicial and administrative data, among others. And on the other hand, the Commission will map existing bases and finance new digital infrastructure.

1.5 Horizontal enablers: synthetic data, data pooling, and standards

The European Commission will develop guidelines and standards on synthetic data and advanced R+D in techniques for its generation will be funded through Horizon Europe.

Another issue that the EU wants to promote is data pooling, as we explained above. Sharing data from early stages of the production cycle can generate collective benefits, but barriers persist due to legal uncertainty and fear of violating competition rules. Its purpose? Make data pooling a reliable and legally secure option to accelerate progress in critical sectors.

Finally, in terms of standardisation, the European standardisation organisations (CEN/CENELEC) will be asked to develop new technical standards in two key areas: data quality and labelling. These standards will make it possible to establish common criteria on how data should be to ensure its reliability and how it should be labelled to facilitate its identification and use in different contexts.

Pillar 2: Regulatory simplification

The second pillar addresses one of the challenges most highlighted by companies and organisations: the complexity of the European regulatory framework on data. The strategy proposes a series of measures aimed at simplifying and consolidating existing legislation.

2.1 Derogations and regulatory consolidation: towards a more coherent framework

The aim is to eliminate regulations whose functions are already covered by more recent legislation, thus avoiding duplication and contradictions. Firstly, the Free Flow of Non-Personal Data Regulation (FFoNPD) will be repealed, as its functions are now covered by the Data Act. However, the prohibition of unjustified data localisation, a fundamental principle for the Digital Single Market, will be explicitly preserved.

Similarly, the Data Governance Act  (European Data Governance Regulation or DGA) will be eliminated as a stand-alone rule, migrating its essential provisions to the Data Act. This move simplifies the regulatory framework and also eases the administrative burden: obligations for data intermediaries will become lighter and more voluntary.

As for the public sector, the strategy proposes an important consolidation. The rules on public data sharing, currently dispersed between the DGA and the Open Data Directive, will be merged into a single chapter within the Data Act. This unification will facilitate both the application and the understanding of the legal framework by public administrations.

2.2 Cookie reform: balancing protection and usability

Another relevant detail is the regulation of cookies, which will undergo a significant modernization, being integrated into the framework of the General Data Protection Regulation (GDPR). The reform seeks a balance: on the one hand, low-risk uses that currently generate legal uncertainty will be legalized; on the other,  consent banners will be simplified  through "one-click" systems. The goal is clear: to reduce the so-called "user fatigue" in the face of the repetitive requests for consent that we all know when browsing the Internet.

2.3 Adjustments to the GDPR to facilitate AI development

The General Data Protection Regulation will also be subject to a targeted reform, specifically designed to release data responsibly for the benefit of the development of artificial intelligence. This surgical intervention addresses three specific aspects:

1. It clarifies when legitimate interest for AI model training may apply.

2. It defines more precisely the distinction between anonymised and pseudonymised data, especially in relation to the risk of re-identification.

3. It harmonises data protection impact assessments, facilitating their consistent application across the Union.

2. 4 Implementation and Support for the Data Act

The recently approved Data Act will be subject to adjustments to improve its application. On the one hand, the scope of business-to-government ( B2G) data sharing is refined, strictly limiting it to emergency situations. On the other hand, the umbrella of protection is extended: the favourable conditions currently enjoyed by small and medium-sized enterprises (SMEs) will also be extended to medium-sized companies or small mid-caps, those with between 250 and 749 employees.

To facilitate the practical implementation of the standard, a model contractual clause for data exchange has already been published , thus providing a template that organizations can use directly. In addition, two additional guides will be published during the first quarter of 2026: one on the concept of "reasonable compensation" in data exchanges, and another aimed at clarifying the key definitions of the Data Act that may generate interpretative doubts.

Aware that SMEs may struggle to navigate this new legal framework, a Legal Helpdesk  will be set up in the fourth quarter of 2025. This helpdesk will provide direct advice on the implementation of the Data Act, giving priority precisely to small and medium-sized enterprises that lack specialised legal departments.

2.5 Evolving governance: towards a more coordinated ecosystem

The governance architecture of the European data ecosystem is also undergoing significant changes. The European Data Innovation Board (EDIB) evolves from a primarily advisory body to a forum for more technical and strategic discussions, bringing together both Member States and industry representatives. To this end, its articles will be modified with two objectives: to allow the inclusion of the competent authorities in the debates on Data Act, and to provide greater flexibility to the European Commission in the composition and operation of the body.

In addition, two additional mechanisms of feedback and anticipation are articulated. The Apply AI Alliance will channel  sectoral feedback, collecting the specific experiences and needs of each industry. For its part, the AI Observatory will act as a trend radar, identifying emerging developments in the field of artificial intelligence and translating them into public policy recommendations. In this way, a virtuous circle is closed where politics is constantly nourished by the reality of the field.

Pillar 3: Protecting European data sovereignty

The third pillar focuses on ensuring that European data is treated fairly and securely, both inside and outside the Union's borders. The intention is that data will only be shared with countries with the same regulatory vision.

3.1 Specific measures to protect European data

• Publication of guides to assess the fair treatment of EU data abroad (Q2 2026):

• Publication of the Unfair Practices Toolbox  (Q2 2026):

  • Unjustified location.

  • Exclusion.

  • Weak safeguards.

  • The data leak.

• Taking measures to protect sensitive non-personal data.

All these measures are planned to be implemented from the last quarter of 2025 and throughout 2026 in a progressive deployment that will allow a gradual and coordinated adoption of the different measures, as established in the Data Union Strategy.

In short, the Data Union Strategy represents a comprehensive effort to consolidate European leadership in the data economy. To this end, data pooling and data spaces in the Member States will  be promoted, Data Labs and AI gigafactories will be committed to and regulatory simplification will be encouraged.

The European open data portal has published the third volume of its Use Case Observatory, a report that compiles the evolution of data reuse projects across Europe. This initiative highlights the progress made in four areas: economic, governmental, social and environmental impact.

The closure of a three-year investigation

Between 2022 and 2025, the European Open Data Portal has systematically monitored the evolution of various European projects. The research began with an initial selection of 30 representative initiatives, which were analyzed in depth to identify their potential for impact.

After two years, 13 projects continued in the study, including three Spanish ones: Planttes, Tangible Data and UniversiDATA-Lab. Its development over time was studied to understand how the reuse of open data can generate real and sustainable benefits.

The publication of volume III in October 2025 marks the closure of this series of reports, following volume I (2022) and volume II (2024). This last document offers a longitudinal view, showing how the projects have matured in three years of observation and what concrete impacts they have generated in their respective contexts.

Common conclusions

This third and final report compiles a number of key findings:

Economic impact

Open data drives growth and efficiency across industries. They contribute to job creation, both directly and indirectly, facilitate smarter recruitment processes and stimulate innovation in areas such as urban planning and digital services.

The report shows the example of:

•  Naar Jobs (Belgium): an application for job search close to users' homes and focused on the available transport options.

This application demonstrates how open data can become a driver for regional employment and business development.

Government impact

The opening of data strengthens transparency, accountability and citizen participation.

Two use cases analysed belong to this field:

• Waar is mijn stemlokaal? (Netherlands): platform for the search for polling stations.
• Statsregnskapet.no (Norway): website to visualize government revenues and expenditures.

Both examples show how access to public information empowers citizens, enriches the work of the media, and supports evidence-based policymaking. All of this helps to strengthen democratic processes and trust in institutions.

Social impact

Open data promotes inclusion, collaboration, and well-being.

The following initiatives analysed belong to this field:

• UniversiDATA-Lab (Spain): university data repository that facilitates analytical applications.
• VisImE-360 (Italy): a tool to map visual impairment and guide health resources.
• Tangible Data (Spain): a company focused on making physical sculptures that turn data into accessible experiences.
• EU Twinnings (Netherlands): platform that compares European regions to find "twin cities"
• Open Food Facts (France): collaborative database on food products.
• Integreat (Germany): application that centralizes public information to support the integration of migrants.

All of them show how data-driven solutions can amplify the voice of vulnerable groups, improve health outcomes and open up new educational opportunities. Even the smallest effects, such as improvement in a single person's life, can prove significant and long-lasting.

Environmental impact

Open data acts as a powerful enabler of sustainability.

As with environmental impact, in this area we find a large number of use cases:

• Digital Forest Dryads (Estonia): a project that uses data to monitor forests and promote their conservation.
• Air Quality in Cyprus (Cyprus): platform that reports on air quality and supports environmental policies.
• Planttes (Spain): citizen science app that helps people with pollen allergies by tracking plant phenology.
• Environ-Mate (Ireland): a tool that promotes sustainable habits and ecological awareness.

These initiatives highlight how data reuse contributes to raising awareness, driving behavioural change and enabling targeted interventions to protect ecosystems and strengthen climate resilience.

Volume III also points to common challenges: the need for sustainable financing, the importance of combining institutional data with citizen-generated data, and the desirability of involving end-users throughout the project lifecycle. In addition, it underlines the importance of European collaboration and transnational interoperability to scale impact.

Overall, the report reinforces the relevance of continuing to invest in open data ecosystems as a key tool to address societal challenges and promote inclusive transformation.

The impact of Spanish projects on the reuse of open data

As we have mentioned, three of the use cases analysed in the Use Case Observatory have a Spanish stamp. These initiatives stand out for their ability to combine technological innovation with social and environmental impact, and highlight Spain 's relevance within the European open data ecosystem. His career demonstrates how our country actively contributes to transforming data into solutions that improve people's lives and reinforce sustainability and inclusion. Below, we zoom in on what the report says about them.

Planks

This citizen science initiative helps people with pollen allergies through real-time information about allergenic plants in bloom. Since its appearance in Volume I of the Use Case Observatory,  it has evolved as a participatory platform in which users contribute photos and phenological data to create a personalized risk map. This participatory model has made it possible to maintain a constant flow of information validated by researchers and to offer increasingly complete maps. With more than 1,000 initial downloads and about 65,000 annual visitors to its website, it is a useful tool for people with allergies, educators and researchers.

The project has strengthened its digital presence, with increasing visibility thanks to the support of institutions such as the Autonomous University of Barcelona and the University of Granada, in addition to the promotion carried out by the company Thigis.

Its challenges include expanding geographical coverage beyond Catalonia and Granada and sustaining data participation and validation. Therefore, looking to the future, it seeks to extend its territorial reach, strengthen collaboration with schools and communities, integrate more data in real time and improve its predictive capabilities.

Throughout this time, Planttes has established herself as an example of how citizen-driven science can improve public health and environmental awareness, demonstrating the value of citizen science in environmental education, allergy management, and climate change monitoring.

Tangible data

The project transforms datasets into physical sculptures that represent global challenges such as climate change or poverty, integrating QR codes and NFC to contextualize the information. Recognized at the EU Open Data Days 2025, Tangible Data has inaugurated its installation Tangible climate at the National Museum of Natural Sciences in Madrid.

Tangible Data has evolved in three years from a prototype project based on 3D sculptures to visualize sustainability data to become an educational and cultural platform that connects open data with society. Volume III of the Use Case Observatory reflects its expansion into schools and museums, the creation of an educational program for 15-year-old students, and the development of interactive experiences with artificial intelligence, consolidating its commitment to accessibility and social impact.

Its challenges include funding and scaling up the education programme, while its future goals include scaling up school activities, displaying large-format sculptures in public spaces,  and strengthening collaboration with artists and museums. Overall, it remains true to its mission of making data tangible, inclusive, and actionable.

UniversiDATA-Lab

UniversiDATA-Lab is a dynamic repository of analytical applications based on open data from Spanish universities, created in 2020 as a public-private collaboration and currently made up of six institutions. Its unified infrastructure facilitates the publication and reuse of data in standardized formats, reducing barriers and allowing students, researchers, companies and citizens to access useful information for education, research and decision-making.

Over the past three years, the project has grown from a prototype to a consolidated platform, with active applications such as the budget and retirement viewer, and a hiring viewer in beta. In addition, it organizes a periodic datathon that promotes innovation and projects with social impact.

Its challenges include internal resistance at some universities and the complex anonymization of sensitive data, although it has responded with robust protocols and a focus on transparency. Looking to the future, it seeks to expand its catalogue, add new universities and launch applications on emerging issues such as school dropouts, teacher diversity or sustainability, aspiring to become a European benchmark in the reuse of open data in higher education.

Conclusion

In conclusion, the third volume of the Use Case Observatory confirms that open data has established itself as a key tool to boost innovation, transparency and sustainability in Europe. The projects analysed – and in particular the Spanish initiatives Planttes, Tangible Data and UniversiDATA-Lab – demonstrate that the reuse of public information can translate into concrete benefits for citizens, education, research and the environment.

The European data portal, data.europa.eu, has published the Open Data Maturity Index 2024, an annual report that assesses the level of open data maturity of European countries.

The 34 participating countries, including the 27 EU Member States, four candidate countries (Bosnia and Herzegovina, Albania, Serbia and Ukraine) and three European Free Trade Association countries (Iceland, Norway and Switzerland) were surveyed.

In this year's edition, Spain obtains an overall rating of 95% out of 100%. This places it in sixth place overall. As reflected in the following image, for yet another year, Spain is in the group of so-called trendsetter countries (trendsetter) , which are those with the best scores in the ranking, and which also include France, Poland, Ukraine, Slovakia, Ireland, Lithuania, Czech Republic, Italy, Estonia and Cyprus.

Figure 1: Groups of participating countries according to their overall open data maturity score.

Above the EU27 average in all four dimensions analysed

The Policy Dimension, focusing on open data policies in different countries, analyses the existence of national governance models for open data management and the measures that have been put in place to implement existing strategies.  In these aspects, Spain scored 96% compared to the European average of 91%. The most positive aspects identified are:

• Alignment with European policies: The report highlights that Spain is fully aligned with the European Open Data Directive, among other recent data-related regulations that have come into force.
• Well-defined action plans: It highlights the strategies deployed in different public administrations focused on incentivising the publication and re-use of data generated in real time and data from citizens.
• Strengthening competences: It focuses on how Spain has developed training programmes to improve the skills of civil servants in managing and publishing open data, ensuring quality standards and fostering a data culture in public administration.

The Impact Dimension analyses the activities carried out to monitor and measure both the re-use of open data, and the impact created as a result of this re-use.  Year after year, this has been the least mature dimension in Europe. Thus, compared to an EU average of 80%, Spain obtains a score of 100% for the development of numerous actions, among which the following stand out:

• Multi-sectoral collaboration: The report highlights how our country is presented as an example of interaction between public administrations, private companies and civil society, materialized in examples such as the close ties between the public sector and the Multisectoral Association of Information (ASEDIE), which produces year after year the ASEDIE report on the reuse of public sector information.
• Examples of re-use in key sectors: It shows how Spain has promoted numerous cases of open data reuse in strategic areas such as the environment, mobility and energy.
• Innovation in communication: The document highlights the effort invested in innovative communication strategies to raise public awareness of the value of data, and especially young audiences. Also noteworthy is the production of podcasts featuring interviews with open data experts, accompanied by short promotional videos.

The Portal Dimension focuses on analysing the functionalities of the national platform to enableusers to access open data and interact within the community. With 96% compared to 82% in the EU27, Spain is positioned as one of the European benchmarks in improving user experience and optimising national portals. The highlights of the report are:  

• Sustainability and continuous improvement: According to the report, Spain has demonstrated a strong commitment to the sustainability of the national open data platform (datos.gob.es) and its adaptation to new technological demands.

• Interaction with users: One of the great strengths is the active promotion from the platform of the datasets available and of the channels through which users can request data that are not available in the National Catalogue.

Finally, the Quality Dimension examines the mechanisms for ensuring the quality of (meta)data. Here Spain scores 88% compared to 79% in the EU. Spain continues to stand out with initiatives that ensure the reliability, accessibility and standardisation of open data. Some of the strengths highlighted in the report are:  

• Metadata automation: It highlights the use of advanced techniques for automatic metadata collection, reducing reliance on manual processes and improving accuracy and real-time updating. 
• Guidelines for data and metadata quality: Spain provides many practical guidelines to improve the publication and quality of open data, including anonymisation techniques, publication in tabular formats (CSV) and the use of APIs.

Continuing to innovate to maintain Spain's advanced position in open data maturity

While Spain continues to stand out in the EU thanks to its open data ecosystem, efforts must continue. To this end, the same report identifies lines of work for countries, such as Spain, that seek to maintain their advanced position in open data maturity and to continue innovating. Among others, the following recommendations are made:

1. Consolidating open data ecosystems: Strengthen thematic communities of providers and re-users by prioritising High Value Datasets (HVDs) in their development and promotion.
2. Promoting coordination: Align the national strategy with the needs of agencies and local authorities.
3. Develop country-specific impact metrics: Collaborate with universities, research institutions and others to develop impact assessment frameworks.
4. Measure and disseminate the impact of open data: Conduct regular (annual or biannual) assessments of the economic, environmental and social impact of open data, promoting the results to generate political support.
5. Facilitate the participation of the open data community: Ensure that providers improve the publication of data based on user feedback and ratings.
6. Increase the quality of data and metadata: Use automated tools and validations to improve publication standards, including adopting artificial intelligence technology to optimise metadata quality.
7. Promote successful reuse cases: Publish and promote success stories in the use of open data, interact with providers and users to identify innovative needs and applications.

Overall, the report shows good progress on open data across Europe. Although there are areas for improvement, the European open data landscape is consolidating, with Spain at the top of the table. Read here the complete Open Data Maturity Index 2024.

The transformative potential of open data initiatives is now widely recognised as they offer opportunities for fostering innovation, greater transparency and improved efficiency in many processes. However, reliable measurement of the real impact of these initiatives is difficult to obtain.

From this same space we have also raised on more than one occasion the recurring question of what would be the best way to measure the impact of open data, we have reviewed different methods and best practices to try to quantify it, as well as to analyse it through detailed use cases or the specific impact on specific topics and sectors such as employment, geographic data, transport or sustainable development objectives. Now, thanks to the report "Indicators for Open Data Impact Assessment" by the data.europa.eu team, we have a new resource to not only understand but also be able to amplify the impact of open data initiatives by designing the right indicators. This publication will provide a quick analysis of the importance of these indicators and also briefly explain how they can be used to maximise the potential of open data.

Understanding open data and its value chain

Open data refers to the practice of making data available to the public in a way that makes it freely accessible and usable. Beyond ensuring simple availability, the real value of open data lies in its use in various domains, fostering economic growth, improving public sector transparency and driving social innovation. However, quantifying the real impact of data openness poses significant challenges due to the multiple ways in which data is used and the wide-ranging implications it can have for society. 

To understand the impact of open data, we must first understand its value chain, which will provide us with a structured and appropriate framework for transforming raw data into actionable insights. This chain includes four main stages that form a continuous process from the initial production to the final use of the data: 

Figure 1: Open Data Value Chain

1. Collection: this consists of identifying existing data and establishing the necessary procedures for their cataloguing.
2. Publication: making data available in an accessible form and easy to locate.
3. Uptake:will come sooner when data is easy to use and accompanied by the right incentives to use it.
4. Application:either through direct consumption of the data or through some transformation that adds new value to the initial data.

Each of these steps will play a critical role in contributing to the overall effectiveness and value derived from open data. The indicators developed to assess the impact of open data will also be closely linked to each of these stages, providing a holistic view of how data is transformed from simple information into a powerful tool for development.

Indicators for impact assessment

The report introduces a set of robust indicators that are specifically designed to monitor open data initiatives through their outputs, outcomes and impact as a result of their value chain. These indicators should not be seen as simple metrics, but as tools to help us understand the effectiveness of open data initiatives and make strategic improvements. Let us look at these indicators in a little more detail:

• Output indicators: are those that focus on measuring the immediate results that come from making open data available. Examples would be the number of datasets released, the frequency of dataset updates , the number of visitors to the data catalogue, the accessibility of the data across various platforms, or even the efforts made to promote the data and increase its visibility. Output indicators help us to assess the efficiency of data publication and dissemination processes and are generally easy to measure, although they will only give a fairly superficial measure of impact.

• Indicators of effect: Outcome indicators measure the short- and medium-term consequences of open data. These indicators are crucial to understand how open data influences decision-makingprocesses, leads to the development of new applications or improves government transparency. Thus, improved public transport planning based on usage data, increased citizen participation in the development of public policies to tackle climate change brought about by the increased availability of data and information, or improved productivity of public services through the use of data can be considered as significant examples of outcome indicators.

• Impact indicators: This is the deepest level of measurement, as impact indicators assess the broader, long-term effects of open data. These indicators may include economic benefits such as job creation or GDP growth, social impacts such as improved trust in public entities, or even environmental impacts such as the effective reduction of greenhouse gases. At this level, indicators are much more complex and specific, and should therefore be defined for each specific domain to be analysed and also according to the objectives set within that domain.

Figure 2: Indicators for impact assessmen

Implementing these indicators in practice will require the implementation of a robust methodological framework that can capture and analyse data from a variety of sources. It is recommended to combine automated and survey-based data collection methods to collect more comprehensive data. This type of dual approach allows for capturing quantitative data through automated systems while also incorporating qualitative insights through user feedback and more in-depth use case analysis.

Looking to the future

The future of open data impact assessment looks towards refining the indicators used for measurement and consolidating them through the use of interactive monitoring tools. Such tools would enable the possibility of a more continuous assessment that would provide real-time information on how open data is being used and its effects in different sectors. In addition, the development of standardised metrics for these indicators would facilitate comparative analysis across regions and over time, further improving the overall understanding of the impact of open data.

Another important factor to take into account are possible privacy and ethical considerations applicable to the selected indicators. As in any other data-centric initiative, privacy and data protection considerations will be paramount and mandatory for the indicators developed. However, once we get into its use by users, it could lead to more delicate situations. Generally, this should not be a particularly problematic issue when monitoring data. However, once we get into its use by users, it could lead to more delicate situations. Ensuring anonymity in indicators and secure practices in their management is also crucial to maintaining trust and integrity in open data processes.

In conclusion, the development and implementation of specific detailed indicators following the recommendations of the report"Indicators for an Open Data Impact Assessment" would be a significant step forward in terms of how we measure and understand the impact of open data.Continuous refinement and adaptation of these indicators will also be crucial as they evolve in tandem with the open data strategies they accompany and their growing sphere of influence. In the medium term the European Commission will further develop its analysis in this area of work through the data.europa.eu project with the ultimate goal of being able to formulate a common methodology for the assessment of the impact of the re-use of public data and to develop an interactive monitoring tool for its implementation.

Content prepared by Carlos Iglesias, Open data Researcher and consultant, World Wide Web Foundation. The contents and views expressed in this publication are the sole responsibility of the author.

Geographic data allow us to learn about the world around us. From locating optimal travel routes to monitoring natural ecosystems, from urban planning and development to emergency management, geographic data has great potential to drive development and efficiency in multiple economic and social areas. They are therefore considered high-value datasets by the European Commission, and have a specific obligations to make their publication accessible and interoperable.

In order to understand the real impact of this type of data, several reports and studies have been carried out. The following are several of them, which address the challenge of measuring the impact of geographic information.

Geospatial data in the Ministry of Transport and Sustainable Mobility. Impact of information co-produced by IGN and CNIG (2024)  

• You can read the full report here.  

This report, produced by ASEDIE and CNIG, aims to draw conclusions about the use and perception of the services co-produced by National Geographic Institute (IGN) and National Centre for Geographic Information (CNIG) in order to understand the benefit they bring to the daily activity of the companies that use them and to society. For this purpose, a survey was carried out among companies using geospatial data, thanks to which a classification of companies reusing geographic data could be drawn up.

Of the 170 companies considered, 70.0% are self-employed and micro-enterprises (less than 10 employees). These companies are mainly located in the Community of Madrid (25.6%), Catalonia (16.3%), Andalusia (14%), Valencia (11.6%) and Castilla y León (11.6%).  53.3% claim to reuse data from Spatial Data Infrastructures (SDI) and 51% open data from INE, among others. The most used products are orthophotos and satellite images (74%), followed by vector maps and cartographic and topographic databases (63%), and LiDAR (58%).

In terms of economic impact, the report estimates an average impact of 35.7% on the sales of the companies surveyed. Specifically, open geographic information from the IGN and CNIG account for an impact of 12.4% of sales.

The report also includes the analysis of collected use cases, as well as in-depth interviews with companies in the sector as examples of best practices and, on the other hand, updates the information from Asedie's annual report on the Data Economy in its 2023 infomediary scope with respect to the economic data of the geographic subsector.

Economic benefits of the SDI central node by CNIG and University of Leuven (2021) 

This document develops and tests a methodology for estimating the economic benefits generated by the Spanish Spatial Data Infrastructure (IDEE), which establishes the publication and accessibility of spatial data through free geographic web services for viewing and downloading produced by cartographic, environmental, cadastral and land observation organisations at national, regional and local level since 2004, in accordance with norms, standards and recommendations that guarantee their interoperability.

The study was to answer the question of what would happen if the NSDI were to disappear. For the study, only the central node of the NSDIE was considered, understanding as such the geographic services and data co-produced among the partners of the National Cartographic System, and focusing on web map services (WMS) and map tiles (WMTS). The nodes of ministries, autonomous communities and local entities were not part of its scope.

Two investigation paths were used to carry out the calculation:

The European Data Portal, data.europa.eu, has just published its Data Maturity Index, an index that assesses the level of maturity of European countries in terms of open data. For its elaboration, an evaluation survey has been carried out and has been completed by 35 countries, including the 27 Member States of the European Union, three countries of the European Free Trade Association (Iceland, Norway and Switzerland) and five candidate countries (Bosnia and Herzegovina, Montenegro, Albania, Serbia and Serbia and Ukraine).  

In this year's edition, Spain obtained a score of 95% out of 100%. This places it in fifth place overall and in fourth place if only European Union (EU27) member countries are taken into account. This figure represents an improvement of three percentage points over last year's score and places Spain 12 points above the EU27 average (83%).  

The top positions in the ranking are occupied by France, Poland, Ukraine and Estonia. 

Above the EU27 average in all dimensions analyzed 

The index is accompanied by a report containing the analysis carried out and an overview of the good practices applied in Europe. In the case of Spain, it is above the EU-27 average in the four indicators analyzed:  

• Policy, focused on the open data policies of the different countries. It analyzes the existence of national governance models for open data management and the measures that have been applied to implement existing strategies. This is the indicator in which Spain obtains a higher score, with 99% compared to 89% in the EU27. The report highlights how the country's national open data strategy helps promote the openness of public information through innovative and structured actions in collaboration with public and private partners. Among other issues, the strategy includes the objective of identifying business models and business success stories to share successful practices. The report also highlights the existence of various digital strategies that complement specific policies on open data, such as the national artificial intelligence strategy, which includes provisions related to the availability of open data for the operation and training of artificial intelligence systems. 

• Impact, which analyzes the activities undertaken to monitor and measure both the reuse of open data and the impact created by such reuse. Traditionally, this has been the least mature dimension across Europe. Nevertheless, Spain scores 96% compared to 77% in the EU27. The best results are achieved in measuring the impact of open data use cases in the environmental, economic and political sectors.  

• Portal, focused on evaluating the functionalities of the national platform that allow users to access open data and help drive interaction within the community. With 96% compared to 85% in the EU27, Spain stands out in the use of analytical tools to understand user behavior, and in the implementation of strategies to ensure the sustainability of the portal and increase its visibility, including presence in social networks. It also highlights the existence of a private area that allows editors to act according to the feedback received. 

• Quality, which examines the mechanisms for ensuring the quality of (meta)data. Here Spain scores 88% compared to 82% in the EU27. Spain's score is driven by compliance with the DCAT-AP standard (providing educational materials for publishers), the existence of a systematic approach to ensure that metadata is up to date, and the wide range of data offered, both historical and current. 

The report also measures how EU27 countries are progressing in the implementation of the implementing regulation on high-value datasets. In this section, Spain ranks ninth, with 68% implementation. In general, Member States are making more progress on geospatial and statistical datasets. Progress is also being made on the underlying technical and legal requirements.  

Overall recommendations 

The report includes a number of recommendations for Spain, among other countries, including encouraging the development of initiatives at the local and regional level, fostering better coordination between teams, and activating the network of open data officers to implement monitoring activities within their organizations. Emphasis is also placed on the need to promote existing open data courses and promote new training materials, paying special attention to developing strategic awareness of the reuse and impact of open data.

Overall, the report shows good progress in open data across Europe. Although there are areas for improvement, the European open data landscape is consolidating, with Spain at the top of the table.  

In 2024, new waves of implementation of the European data strategy will present national teams with new challenges. On the one hand, they will have to redouble their efforts to inform citizens of the new data sources arising from initiatives such as the Data Governance Act and the data spaces. In this sense, coordination will be necessary between the new figures arising from these legislative developments and the traditional world of open data, enhancing the obvious synergies between the two to boost the data economy and collectivize the value generated. 

There is a recurring question that has been around since the beginning of the open data movement, and as efforts and investments in data collection and publication have increased, it has resonated more and more strongly: What is the value of a dataset?

This is an extremely difficult question to answer given not only the inherent complexity of the data itself, which grows exponentially as we begin to combine it, but also the different points of view from which the question of value can be approached.

• If we know that the value will not be immediate, how can we foresee and quantify the potential benefits at some point in the future?
•  Could the value of data become negative in some cases, if we can also cause some kind of 'harm' with it?
• Can the value of data degrade over time?

In this space we have recurrently analysed the value of open data for the administration from different approaches: high-value data and its identification, the perspective of suppliers, the keys to the value of data, how to generate value through data or what is the value of data in real time. However, analysis and research work in this area continues to grow unstoppably. In this regard, we would like to highlight a recently published paper from the University of Cambridge. It is a study in which some of the most common methods for data valuation are described.

Based on their previous analysis of the characteristics of the data and its associated value, a review of the methods that currently exist has been carried out. They concluded that these methods can be divided into several categories, the characteristics of which are detailed below.

Methods based on cost análisis

This approach is based on the traditional statistical principle of "sum of costs". It takes into account the costs of generating, collecting, storing and replacing datasets, as well as the costs to the organisation in case the data results in some kind of loss. These methods have the advantage that they are relatively easy to calculate, but on the other hand, they have the difficulty of having to differentiate between costs directly attributable to the data and other indirect costs related to, for example, the variety of professional work involved or the different software elements used.

An example of the application of this method is the case of Statistics Canada with its analysis of the valuation of the costs associated with investment in data, databases and data science in Canada.

Methods based on revenue análisis

In this case, revenue stream expectations are used, taking as a reference the existing potential market for the exploitation of the data. This may take into account, for example, usage fees, trademarks or patents. The main limitations of these methods are generally that they require the application of somewhat more subjective criteria and the complexity of estimating the value when the data are not exploited directly but indirectly, e.g. through analytics.

These methods are used in the OECD study on the prospects for the value of data. It calculates the reported revenues related to the collection and sale of data through the US enterprise survey.

Methods based on market análisis

Generally, these are the preferred methods to use when all the elements necessary to make the calculations are available. However, today there is still a large amount of data in organisations for internal use only, which makes it difficult to use these methods, as their behaviour is not visible to the market. Furthermore, these methods cannot fully incorporate the social value of the data.

An example of this method is the analysis made in the study carried out by the Economic Commission for Latin America and the Caribbean (ECLAC) on the data marts launched by the European Union and the Government of Colombia, respectively.

Experiments and surveys

This approach to the value of data consists of assessing market sentiment in relation to the data by directly asking about the willingness to pay for certain data or to do without it. It is generally used when the public market value is not known or in cases where social value is important, for example in the environmental area. A limitation of these methods is that, when respondents are not specialists, it can be quite difficult for them to assess the possible uses of the data and thus its full value.

The study carried out by the UK Office for National Statistics is a clear example of such valuation methods.

Impact-based methods

In this case the assessment is carried out through experiments or case studies that analyse the causal effect on certain outcomes attributable to the data. This option is particularly useful for evidence-led policy makers, as it allows a cause-effect relationship to be established, making it easier to understand the benefits and to develop a narrative in favour of the use of the data. However, if the experiments are not well designed or are not well adjusted to the specific context we want to analyse, we run the risk of obtaining an excessively subjective assessment.

The decision-based evaluation framework proposed by the Internet of Water Coalition is a good example of how to apply impact-focused methods to a particular case.

Actor-chain methods

The aim of these methods is to use a more comprehensive view to assess the data from different points of view. This means that evaluations can also be more complex by involving different definitions of what constitutes the value of the data. However, it also makes it the most appropriate method when one wants to assess a data ecosystem as a whole. Moreover, it is a growing method for organisations considering socially responsible investment.

An example of how these methods can be applied in practice is the case study carried out with Highways England.

Methods based on real options analysis

The main advantage of these methods is that they can be applied even when not all possible use cases for the data are yet defined. Their aim is to get an estimate of the value of the data in certain possible future scenarios - usually through computer simulation - so that if such a scenario is reached, exploitation of the data could be justified. Thus, certain data-related decisions and investments could be postponed until the ideal scenario that maximises the value of the data is reached, thereby minimising the associated costs and risks until that point.

The UK case study on the transport sector provides an example of how these methods could be applied using financial models.

And what is the method I should use in my particular case?

Unfortunately, there is no golden rule for selecting a particular method. However, there are a number of questions that the authors of the study suggest we ask ourselves in order to find the most appropriate method for each case:

• What exactly we are assessing: Data goes through several stages in its life cycle - from raw data to processed data, analysis or generated knowledge. Depending on which phase we want to focus our analysis on, some methods may be more appropriate than others.
• From which point of view the valuation is carried out: value can have different definitions depending on the point of view of who is carrying out or commissioning a valuation. In some cases, for example, cost containment due to budgetary constraints may be the priority, while in others one might choose to try to maximise social value.
• When the assessment process takes place: basically it should be considered whether the assessment will be carried out in a predictive way before all the elements assessed are available or whether it will be carried out a posteriori, once all the variables are already known.
• What is the purpose of the assessment: several of the available methods omit or minimise certain aspects of the data by focusing on other features of the assessment process. Therefore, it will be necessary to be clear about the priorities of our evaluation when selecting the most appropriate method: are we interested in social impact, improving productivity or maximising the cost-effectiveness of data?

We should therefore first analyse our needs and our own definition of value, asking ourselves what exactly we want to evaluate and how best to carry out that evaluation, and then develop our own valuation framework using the most appropriate methods from the wide variety available.

Content prepared by Carlos Iglesias, Open data Researcher and consultant, World Wide Web Foundation.

The contents and views expressed in this publication are the sole responsibility of the author.

What are the needs of European public institutions that reuse open data? This is the question posed by the European Commission, through the European open data initiative data.europa.eu, and which is the starting point of the report "Measuring data demand within the public sector", recently published by the initiative.

The report is part of a series of actions that data.europa.eu is undertaking to encourage the re-use of data by the public sector. It is a year-long campaign that will include a series of articles and a webinar. The campaign will culminate in a second report on the findings.

This first report lays the groundwork for the issues to be addressed, setting out 3 objectives:

• Clarify the importance of public institutions as data re-users.
• Identify methods and good practices for assessing the demand from public institutions.
• Stimulate debate on the most appropriate way to foster the re-use of open data by public institutions.

Ultimately, the aim is to foster a data-driven public sector that recognises data as an integral asset for policy formulation, service delivery, management and public innovation.

The benefits of open data re-use by public institutions

Traditionally, in the open data ecosystem, an approach has been followed where the roles were divided: the public sector was the provider of data and the private sector was the re-user. However, this is changing and more and more institutions are realising the benefits of harnessing the potential of open data.

The report highlights how the OECD has identified three areas where data re-use can improve the effectiveness of public institutions:

1. Anticipating governance. Open data helps to predict trends and patterns in order to mitigate emerging risks and respond to developing crises. One example is the interactive dashboard developed by Eurostat with statistical, monthly and quarterly indicators. This dashboard is used by different countries to monitor the economic and social recovery linked to the COVID-19 pandemic.

2. Policy and service design and delivery. Open data helps to understand issues, engage citizens and drive evidence-based policy making. The report gives the example of the Baltic Sea regions, where cross-border use of open government data is being applied to improve social services.

3. Performance management. Open data can also have an impact on increased public sector productivity, more efficient use of resources and better policy evaluation. In this respect, the European Commission's agri-food data portal, which integrates data from multiple European institutions, facilitates the calculation of key indicators for the evaluation of agricultural policies in all countries.

Open data therefore helps to make public services and policies more efficient, but also more sustainable, inclusive and trustworthy, benefiting citizens and businesses. However, despite these advantages, we find that much data in public institutions still does not flow freely, but is siloed. There is a lack of incentive to share, but also a fear of loss of control, among other factors.

The report suggests that the solution lies in taking into account and measuring the demand for data from public institutions. Knowing the benefits of opening up certain data encourages other institutions to open up and reuse it. In this regard, the report highlights, among others, Spain's efforts in engaging with user communities and monitoring the re-use of public data through the Aporta Initiative.

Existing approaches to assessing public institutions' demand for data

After this first part focusing on the benefits, the report goes on to analyse the approaches and indicators that currently exist in the European landscape for assessing the needs of public institutions as data re-users, in order to determine which methods are the most appropriate.

The literature review and the analysis of international measurement frameworks show that demand measurement is not common. The approaches and indicators developed by EU institutions and Member States are few and far between, and focus on fostering demand for data that is already available, without determining the extent of actual re-use of publicly available data and the impact achieved with it. Moreover, they usually focus on re-users in general and on groups such as businesses, journalists, civil society, etc., while neglecting the public sector.

Among the most advanced countries in this area, the report mentions Spain, where a proactive strategy is adopted through challenges and hackathons, co-creation events, information sessions and regular training.

When it comes to measuring impact, a combination of quantitative and qualitative methods is recommended, for example:

• Analyse IP addresses and web statistics.
• Implement web crawling techniques (e.g. search APIs) to identify mentions of open data reuse.
• Quantitative analysis of Tweets mentioning open data.
• Conduct online questionnaires and interviews with re-users.
• Use of contact forms so that users can share use cases or rating systems so that they can rate datasets.

In Spain, quite a few initiatives have already implemented these mechanisms, along with additional ones.

The report also reviews some international indices and reports in search of indicators that measure demand. Many, such as the Global Open Data Index, the Open Data Barometer or the Open Data Inventory (ODIN) focus on data provision and do not include any indicators to calculate the demand for open data by or within public institutions. However, here too the situation is changing and we find other reports such as the European Open Data Maturity Assessment or the Open Data Readiness Assessment (ODRA) that do include this type of metric. Also the forthcoming Global Data Barometer, produced by the Latin American open data initiative ILDA and the Data for Development Network, will include demand indicators as part of the study.

In this sense, the report includes in the annex two tables, one with the overview of the frameworks examined and another one that groups the indicators included in these frameworks that can be used to analyse the demand for open data from public institutions.

Next steps

The paper concludes with a list of key questions emerging from the research, which will be used to trigger a debate among stakeholders on appropriate methods and indicators to measure the demand for data from public institutions, in the context of the data.europa.eu portal. Examples of such questions are: what are the appropriate activities to attract re-users in the public sector or how can automated metrics be leveraged to measure the demand for data from public institutions.

A webinar will take place on Tuesday 29 March to discuss this report. Speakers from different agencies will explain how they engage with data re-users in public institutions, measure their demand and incorporate data into their open data policies. You can register at this link.

Today, no one can deny that open data holds great economic power. The European Commission itself estimates that the turnover of open data in the EU27 could reach 334.2 billion in 2025, driven by its use in areas linked to disruptive technologies such as artificial intelligence, machine learning or language technologies.

But in addition to its economic impact, open data also has an important value for society: it provides information that makes social reality visible, driving informed decision-making for the common good.

There are thousands of areas where open data is essential, from refugee crises to the inclusion of people with disabilities, but in this article we will focus on the scourge of gender violence.

Where can I obtain data on the subject?

Globally, agencies such as the UN, the World Health Organization and the World Bank offer resources and statistics related to violence against women.

In our country, local, autonomous and state agencies publish related datasets. To facilitate unified access to them, the Government Delegation against Gender Violence has a statistical portal that includes in a single space data from various sources such as the Ministry of Finance and Public Administration, the General Council of the Judiciary or the Public Employment Service of the Ministry of Employment and Social Security. The user can cross-reference variables and create tables and graphs to facilitate the visualization of the information, as well as export the data sets in CSV or Excel format. 

Projects to raise awareness and visibility

But data alone can be complicated to understand. Data need a context that gives them meaning and transforms them into information and knowledge. This is where different projects arise that seek to bring data to the public in a simple way.

There are many associations and organizations that take advantage of published data to create visualizations and stories with data that help to raise awareness about gender violence. As an example, the Barcelona Open Data Initiative is developing the "DatosXViolenciaXMujeres" project. It is a visual and interactive tour on the impact of gender violence in Spain and by Autonomous Communities during the period 2008-2020, although it is updated periodically. Using data storytelling techniques, it shows the evolution of gender violence within the couple, the judicial response (orders issued and final convictions), the public resources allocated, the impact of COVID-19 in this area and crimes of sexual violence. Each graph includes links to the original source and to places where the data can be downloaded so that they can be reused in other projects.

Another example is "Datos contra el ruido” (Data against noise), developed within the framework of GenderDataLab, a collaborative platform for the digital common good that has the support of various associations, such as Pyladies or Canodron, and the Barcelona City Council, among others. This association promotes the inclusion of the gender perspective in the collection of open data through various projects such as the aforementioned "Dotos contra el ruido", which makes visible and understandable the information published by the judicial system and the police on gender violence. Through data and visualizations, it provides information on the types of crimes or their geographical distribution throughout our country, among other issues. As with "DatosXViolenciaXMujeres", a link to the original source of the data and download spaces are included.

Tools and solutions to support victims

But in addition to providing visibility, open data can also give us information on the resources dedicated to helping victims, as we saw in some of the previous projects. Making this information available to victims in a quick and easy way is essential. Maps showing the location of help centers are of great help, such as this one from the SOL.NET project, with information on organizations that offer support and care services for victims of gender-based violence in Spain. Or this one with the centers and social services of the Valencian Community aimed at disadvantaged groups, including victims of gender violence, prepared by the public institution itself.

This information is also incorporated in applications aimed at victims, such as Anticípate. This app not only provides information and resources to women in vulnerable situations, but also has an emergency call button and allows access to legal, psychological or even self-defense advice, facilitating access to a social criminologist.

In short, we are facing a particularly sensitive issue, which we must continue to raise awareness and fight to put an end to. A task to which open data can make a significant contribution.

If you know of any other example that shows the power of open data in this field, we encourage you to share it in the comments section or send us an email to dinamizacion@datos.gob.es.

Content prepared by the datos.gob.es team.

Measuring the impact of open data is not always easy. As we saw a few weeks ago, there are several theoretical models that are not easy to implement, so we have to look for different approaches. In the Aporta Initiative we use a mixed approach, as explained here: a quantitative analysis through indicators on data publication and its characteristics, and a qualitative one through the collection of cases of data use.

This approach is also used by various local, regional and state initiatives in our country. In today's article, we will focus on concrete examples of mechanisms implemented by Spanish open data initiatives to monitor and measure the impact of the use of their data.

Quantitative analysis

One of the first steps in monitoring impact is to know quantitatively if users are accessing the published data. To do this we can use different tools.

Dashboards

Thanks to the incorporation of web analytics tools in open data platforms, such as Google Analytics or Motomo (which until 2018 was called PIWIK), a series of indicators can be set around data consumption variables, such as how many users visit the web, what is their origin, which data sets are most in demand or in what format they are downloaded. All of this information is of great value when it comes to making decisions that imply improvements to continue promoting the reuse of public information.

With this data, dashboards can be created so that users can also know this information. This is the case of the Castellón Provincial Council, the Madrid City Council, the Catalan Government, Renfe, the Basque Government or the datos.gob.es itself.

Conducting surveys and periodic studies

In addition, it is advisable to carry out frequent public consultations and studies that allow us to know directly the impact of our data. The ONTSI periodically carries out a characterization study of the sector, and an analysis of the Public Administrations as reusers of their own data and that of third parties. Another example is the report on the Infomediary Sector of ASEDIE, now in its 8th edition. This report measures the products and services based on open data that have been generated. Both reports use a stable methodology that allows comparisons between different years. 

Qualitative analysis

It consists of the identification of use cases through different mechanisms, such as

Application and enterprise tracking

Thanks to the mapping of open data use cases, we can know what the impact of a certain data set is. In many open data platforms, whether local, regional or state, we can find a section of applications or companies with examples and reuse success stories that in turn serve as reference and inspiration for the creation of new value services. In the case of datos.gob.es, we have a form for companies or applications that wish to register their information, but we also carry out a proactive search, through contact with the main actors in the ecosystem and media alerts.

Other examples of portals that have applications sections are Andalusia, Castilla y Leon, Navarra, Barcelona, Santander, Malaga, Zaragoza, Valencia, Vitoria or Bilbao, although there are many more.

Implementation of data communities

In order to be aware of new developments in the field of reuse and to exchange knowledge and experiences to align the data publication strategy with the needs of reusers, some initiatives have opted for the implementation of communities. This is the case of the Basque Country, which has created a space to centralize everything that happens around the reuse of its data. This community has been especially useful to collect and measure the work that has been developed on COVID-19 using open data. Under its umbrella there are also activities and competitions that encourage reuse.

For its part, the Castellón Provincial Council has created a Provincial Council of Reusers, a mixed public-private body made up of technicians from the provincial institution itself, and people with recognized professional backgrounds in different economic sectors. These professionals meet once a quarter to hold a conversation to monitor use cases and which favours constant feedback and the enrichment of the Provincial Council's open data strategy. 

The National Library of Spain is working along the same lines and has launched a collaborative work platform so that those citizens who wish to do so can participate in specific projects to enrich the Library's data, making it more accessible and easier to reuse.

In short, all these activities allow monitoring the activity of an open data initiative and its impact on society. They help us to know what challenges we are solving in fields as important for humanity as the environment, health or education. In this way, we will be able to know its evolution over time and easily detect trends and possible areas of improvement, which will lead us to distribute the efforts and resources available in a more effective way.  

Content elaborated by datos.gob.es team.