datos abiertos

On the occasion of Open Data Day 2026, the Open Knowledge Foundation (OKFN) held an online conference  entitled "The Future of Open Data", an open-access event that brought together a diverse community of data professionals from governments, civil society organizations, universities, newsrooms and activist collectives. From datos.gob.es we follow the day live and share here a summary of the main ideas that marked the day.

Three approaches to understanding the role of open data in the age of AI

The conference was structured around three main thematic blocks:

1. Navigating open data regulation in the public interest: interventions by representatives of academia, public policy makers and researchers from different countries who discussed the regulatory framework of open data in the current context of AI.
2. Community Voices, Open Data, and AI: Short presentations of concrete projects from around the world exploring the intersection between open data and artificial intelligence, from tools for judicial analysis to  citizen science dashboards.
3. 20 years of CKAN: The future in the age of AI: reflections on the two decades of history of open data and CKAN, on the past, present and challenges to come.

Overall, the day combined political reflection, technical innovation and community vision, with voices from Spain, France, India, Ukraine, Kenya, the United States and Australia, among other countries. And the common thread of the event was the question that today runs through digital policy forums around the world: what is the role of open data in an ecosystem increasingly dominated by artificial intelligence?

Thematic block 1. A movement that was born out of activism

In its origins, the open data movement began in conversations between activists committed to transparency, accountability and access to public information to citizens.

This episode of the datos.gob.es podcast also discusses the origin of open data and its evolution

Today, however, the movement is more diversified because there are now more agents that influence, such as artificial intelligence. There is also a regulatory context that functions as a framework in the development of the open data movement.

The topic of regulation and governance was the backbone of the first session of the event, moderated by Renata Ávila, CEO of OKFN. The following participated in it:

• Jonathan Gray, author of the book Public Data Cultures (Polity, 2025) and professor at King's College London, presented his work as a reference source for reflecting on data as an open asset: how this openness is built and how it can help us respond to great collective challenges. His proposal is that public data is not simply technical information, but the result of cultural and political decisions about what we tell, how we tell it, and for whom.
• Renato Berrino Malaccorto, research manager of the Open Data Charter, stressed that the openness of data is fundamental for the ethical development of AI. Without open, auditable and quality data, it is not possible to build artificial intelligence systems that are accountable to citizens. At the same time, he pointed out that there is a real capacity gap: many organizations and governments lack the technical and human resources necessary to harness the potential of open data in this new context.
• Ruth del Campo, general director of data at the Ministry for Digital Transformation and Public Function of the Government of Spain, offered a very relevant institutional perspective for our context. He recalled that "The data economy is part of the economy", and underlined the boost that the Government is giving to initiatives such as datos.gob.es and Impulsa Data (aimed at modernizing internal management and feeding the Sectoral Data Spaces). He also stressed the importance of the data strategy incorporating AI ready principles, guaranteeing adequate resources – such as linguistic corpora – to train AI models efficiently and without generating new inequalities. Finally, he pointed out the need to simplify and harmonize data regulations, a process in which progress is already being made at the European level.

The panel's underlying message was clear: open data needs to be placed at the heart of the digital agenda, adequately resourced and explicitly connected to public AI strategies. AI of social interest cannot be built without open data; and open data without a vision of AI risks being relegated to irrelevance.

Thematic block 2. Lightning Talks: Projects That Demonstrate the Potential of Open Data

The second session of the day brought together short presentations of concrete projects that illustrated how open data and artificial intelligence can work together in the public interest. Some examples are:

• Ihor Samokhodskyi from the Ukrainian initiative Policy Genome presented an open data-based analysis tool for judicial practice that demonstrates how public information, combined with AI techniques, can contribute to transparency and the improvement of justice systems.
• Javier Conde, from the Polytechnic University of Madrid, presented the proposal he has developed together with his colleagues Andrés Muñoz-Arcentales and Álvaro Alonso to improve the integration of European open data in  data spaces. This project facilitates the automatic generation of high-quality metadata, thus ensuring the interoperability and reuse of datasets. A directly relevant initiative for the improvement of portals such as datos.gob.es and its connection with data.europa.eu.
• Renu Kumari, from #semanticClimate and Frictionless Data (India), presented a project that works at the intersection between open climate data and semantic tools to make scientific literature and data on climate change more accessible, structured and reusable.
• Richard Muraya, from The Demography Project (Kenya), presented Uhai/Life, a  citizen science dashboard that aggregates open data on natural resource use to provide insight into human and environmental well-being at the local scale. An example of how open data can empower communities to tell their own story, without relying on external narratives or institutions.

Figure 1. Presentation slide of one of the presentations of the event. Source: conference "The Future of Open Data" organized by OKFN.

• Finally, Sayantika Banik from DataJourney (India) showed an autonomous analytics assistant capable of transforming open datasets into easily understandable information.

Thematic block 3. Round table: 20 years of CKAN and the challenges of the future

The longest session of the day was also the most reflective: a round table to celebrate two decades of CKAN, the open data portal management tool born within OKFN and which today feeds hundreds of data portals around the world, including datos.gob.es. The panel was moderated by Jamaica Jones, CKAN/POSE community manager  at the University of Pittsburgh. The following participated in this table:

• Rufus Pollock, founder of OKFN and Datopian, and co-founder of Life Itself, stressed the importance of keeping power in the hands of citizens and of betting on open source as a driver of economic development and shared knowledge. For Pollock, AI must be understandable and accessible to most, not just large corporations.
• Joel Natividad is Co-CEO and co-founder of datHere, a company specializing in open data solutions and analytics tools for the public sector. As a CKAN user for more than 15 years, he insisted on one idea: "We have always tried to learn how machines think, and now it is machines that are learning how humans think."
• Patricio Del Boca is Tech Lead and Open Activist at OKFN, where he leads the technical development of initiatives related to CKAN and open data infrastructures. He shared OKFN's next steps for 2026: building more community and developing use cases that demonstrate the practical value of open data in the current context.
• Andrea Borruso is an expert in Geographic Information Systems (GIS) and open data. As president of onData, an Italian non-profit association that promotes access to and reuse of public data, he highlighted data activism and citizen science as drivers of technological development that involve the community.
• Antonin Garrone of data.gouv.fr, France's national open data portal, brought to the table the perspective of an established portal that has spent years exploring how to integrate new technologies without losing sight of its public service mission.
• Steven De Costa is CEO of Link Digital, an Australian company specializing in the implementation and development of CKAN-based solutions, and Co-Steward of the CKAN project. His perspective combined technical vision with a concern to maintain an open and participatory governance model.
• Finally, Public AI research engineer Mohsin Yousufi insisted on the intersection between artificial intelligence, public data infrastructures, and technology policies, exploring how AI systems can be designed and governed to serve the public interest.

Final Thought: Open Data as Democratic Infrastructure

If there is one conclusion that ran through all the sessions of Open Data Day 2026, it is that open data is not in crisis, but at a decisive moment. The opportunities offered by artificial intelligence are real, but so are the risks. It is important to know them in order to know how to address them. Some of those that were mentioned are:

• Prevent public data from becoming the raw material of private systems without transparency or accountability.
• Preserve the political will to keep open data portals functional and updated.
• Bridging the digital skills and training gap to facilitate the participation of all countries and communities in the new AI ecosystem.

In the face of this, the message of the event was one of mobilization: it is necessary to vindicate open data as a democratic infrastructure, explicitly connect data policies with public AI strategies, and ensure that the benefits of artificial intelligence reach all citizens, and not only those who already have access to technological resources.

From datos.gob.es we will continue to work in that direction, and we celebrate the existence of spaces such as Open Data Day to remind us why we started and where we want to go.

You can watch the event video again here

Just a few months after the success of its first award, the Madrid City Council has opened the call for the second edition of the Open Data Reuse Awards. It is an initiative that seeks to recognize and promote innovative projects that use the datasets published on the datos.madrid.es portal. With a total endowment of 15,000 euros, these awards consolidate the municipal commitment to data culture, transparency and the creation of social and economic value from public information.

In this article we tell you some of the keys you must take into account to participate.

Two award categories to consider

The call establishes two categories, each with several prizes:

1) Web services, applications and visualizations: rewards projects that generate services, visualizations or web or mobile applications.

• First prize: €4,000
• Second prize: €3,000
• Third prize: €1,500
• Student prize: €1,500

2) Studies, research and ideas: focuses on research projects, analysis or description of ideas to create services, studies, visualizations, web or mobile applications. This category is also open to university end-of-degree and end-of-master's projects (TFG-TFM).

• First prize: €2,500
• Second prize: €1,500
• Third prize: €1,000
• Projects already awarded, subsidized or contracted by the Madrid City Council.
• Projects that do not use any datasets from the municipal portal.

In both categories, it is necessary that at least one set of data from the municipal portal is used, and can be combined with public or private sources from any territorial area. Projects can be recent or have been completed in the two years prior to the closing of the call.

Awards may be declared void if the minimum quality is not reached. In this case, the remaining amounts will be redistributed proportionally among the rest of the winners.

Requirements to participate

The call is open to natural and legal persons who are the authors of the projects or initiatives. The aim is for any person or entity with an interest in the reuse of data to be able to submit their proposal, regardless of their technical level. Therefore, both professionals and companies, researchers, journalists and developers, as well as amateurs and amateurs interested in data analysis and visualization can participate.

In the case of the student prize, only those individuals enrolled in official courses 2023/24, 2024/25 or 2025/26 may participate.

On the other hand, the following are excluded from all categories:

Process Phases

The municipal portal details the phases of the call, which include:

1. Publication of the call.  On March 3,  the regulatory bases were published in the Official Gazette of the Madrid City Council.
2. Submission of nominations. The deadline for submitting applications is from March 4 to May 4 (both included). They can be submitted online or in person, as explained below.

3. Analysis and correction. Until June 3, the review of the documentation submitted will be carried out. If necessary, applicants will be contacted to correct errors.

4. Assessment and deliberation. A jury will evaluate all the admitted projects, according to the criteria established in the rules of the call. Their usefulness, economic value, social value and contribution to transparency will be taken into account; their degree of innovation and creativity; the variety of datasets used from the Madrid Open Data Portal; and its technical quality. This phase will run until September 15.

5. Resolution. In the months of September and October , the proposal for the granting and official publication of the resolution will be carried out.

6. Awards ceremony. The awards will be presented at a public event, estimated for the month of November.

The official website will update dates and documentation as the process progresses.

How applications are submitted

As mentioned above, applications can be submitted electronically or in person:

• Online, through the electronic headquarters of the Madrid City Council. Identification and electronic signature are required for this.
• In person, at the registration assistance offices of the Madrid City Council, as well as at the registries of other public administrations.

Individuals may submit the application in both ways, while legal persons may only submit the application electronically.

In both cases, nominations must include:

• Official application form, to be downloaded from the Madrid City Council's electronic headquarters.
• Project report, based on a model to be downloaded from the aforementioned electronic office.  This document will include the title, authorship and a detailed description, as well as the list of datasets used, the objectives, the target audience, the expected impact, the degree of innovation and the technology used.
• Responsible declaration.
• Collaboration agreement, in the case of presenting itself as a group.

Get inspired by the winning projects of the first edition

The second edition of the Open Data Reuse Awards comes on the heels of the success of the previous edition. In 2025, the Madrid City Council held the first edition of these awards, which brought together 65 nominations of great quality and diversity. Among them, proposals promoted by university students, startups, multidisciplinary teams and citizens committed to  the intelligent use of public data stood out.

The award-winning projects demonstrated that open data can become real tools to improve urban life, boost transparency and generate useful knowledge for the city. In this article we summarize what these projects consisted of.

In summary, the II Open Data Reuse Awards 2026 are an opportunity to demonstrate how public data can be turned into real innovation. An invitation to develop projects that promote a smarter, more transparent and participatory Madrid.

The European High-Value Datasets (HVD) regulation, established by Implementing Regulation (EU) 2023/138, consolidates the role of APIs as an essential infrastructure for the reuse of public information, making their availability a legal obligation and not just a good technological practice.

Since 9 June 2024, public bodies in all Member States are required to publish datasets classified as HVDs free of charge, in machine-readable formats and accessible via APIs. The six categories regulated are: geospatial data, Earth observation, environment, statistics, business information and mobility.

This framework is not merely declarative. Member States must report to the European Commission compliance status every two years, including persistent links to APIs that give access to such data. The situation in Spain in terms of transparency, open data and Systematic API Provisioning can be consulted in the indicators published by the Open Data Maturity Report.

In practice, this means that APIs are the bridge between the norm and reality. The regulation not only says what data must be opened, but also requires it to be done in such a way that it can be automatically integrated into applications, studies or digital services. Therefore, reviewing the public APIs available in Spain is a concrete way to understand how this framework is being applied on a day-to-day basis.

Inventory of public APIs in Spain

INE — API JSON (Tempus3)

The National Institute of Statistics offers a API REST that Exposes the entire database Tempus3 broadcast format JSON, which includes official statistical series on demography, economy, labour market, industry, services, prices, living conditions and other socio-economic indicators.

To make calls, the structure must follow the pattern https://servicios.ine.es/wstempus/js/{language}/{function}/{input}. The tip=AM parameter  allows you to get metadata along with the data, and tv filters by specific variables. For example, to obtain the population figures by province, simply consult the corresponding operation (IOE 30243) and filter by the desired geographical variable.

No authentication or API key required: any well-formed GET request returns data directly.

Example in Python — get the resident population series with metadata:

import requests

url = ("https://servicios.ine.es/wstempus/js/ES/"

       "DATOS_TABLA/t20/e245/p08/l0/01002.px?tip=AM")

response = requests.get(url)

data = response.json()

for serie in data[:3]:  # primeras 3 series

    name = series["Name"]

    last = series["Date"][-1]

    print(f"{name}: {last['Value']:,.0f} ({last['PeriodName']})")

    TOTAL AGES, TOTAL, Both sexes: 39,852,651 (1998)

    TOTAL AGES, TOTAL, Males: 19,488,465 (1998)

    TOTAL EDADES, TOTAL, Mujeres: 20,364,186 (1998)

AEMET — OpenData API REST

The State Meteorological Agency exposes its data through a REST API, documented with Swagger UI (an open-source tool that generates interactive documentation), observed meteorological data and official predictions, including temperature, precipitation, wind, alerts and adverse phenomena.

Unlike the INE, AEMET requires a Free API key, which is obtained by providing an email address in the portal opendata.aemet.es. A API key works as A type of "password" or identifier: it is used to allow the agency to know who is using the service, control the volume of requests and ensure proper use of the infrastructure.

A relevant technical aspect is that AEMET implements a two-call model: the first request returns a JSON with a temporary URL in the data field, and a second request to that URL retrieves the  actual dataset. The rate limit is 50 requests per minute.

Example in Python — daily weather data (double call):

import requests

API_KEY = "tu_api_key_aqui"

headers = {"api_key": API_KEY}

#1st call: Get temporary data URLs

url = ("https://opendata.aemet.es/opendata/api/"

        "Values/Climatological/Daily/Data/"

        "fechaini/2025-01-01T00:00:00UTC/"

       "fechafin/2025-01-10T23:59:59UTC/"

       "allseasons")

resp1 = requests.get(url, headers=headers).json()

#2nd call: Download the actual dataset

datos = requests.get(resp1["datos"], headers=headers).json()

for estacion in datos[:3]:

     print(f"{station['name']}: "

           f"Tmax={station.get('tmax','N/A')}°C, "

          f"Prec={estacion.get('prec','N/A')}mm")

CITFAGRO_88_GAITERO: Tmax=8.8°C, Prev=0.0mm

ABANILLA: Tmax=14,8°C, Prec=0,0mm

LA RODA DE ANDALUCÍA: Tmax=15.7°C, Prec=0.2mm

CNIG / IDEE — Servicios OGC y OGC API Features

The National Center for Geographic Information It publishes official geospatial data – base mapping, digital terrain models, river networks, administrative boundaries and other topographic elements – through interoperable services. These have evolved from WMS/WFS to the OGC API (Features, Maps and Processes), implemented with open software such as pygeoapi.

The main advantage of OGC API Features over WFS is the response format: instead of GML (heavy and complex), the data is served in GeoJSON and HTML, native formats of the web ecosystem. This allows them to be consumed directly from libraries such as Leaflet, OpenLayers or GDAL. Available datasets include Cartociudad addresses, hydrography, transport networks and geographical gazetteer.

Example in Python — query geographic features via OGC API:

import requests

# OGC API Features - Basic Geographical Gazetteer of Spain

base = "https://api-features.idee.es/collections"

collection = "falls" # Waterfalls

url = f"{base}/{collection}/items?limit=5&f=json"

resp = requests.get(url).json()

for feat in resp["features"]:

props = feat["properties"]

coords = feat["geometry"]["coordinates"]

print(f"{props['number']}: ({coords[0]:.4f}, {coords[1]:.4f})")

None: (-6.2132, 42.8982)

Cascada del Cervienzo: (-6.2572, 42.9763)

El Xaral Waterfall: (-6.3815, 42.9881)

Rexiu Waterfall: (-7.2256, 42.5743)

Santalla Waterfall: (-7.2543, 42.6510)

MITECO — Open Data Portal (CKAN)

The Ministry for the Ecological Transition maintains a CKAN-based portal  that exposes three access layers: the CKAN Action API for metadata and dataset search, the Datastore API (OpenAPI) for live queries on tabular resources, and  RDF/JSON-LD endpoints compliant with DCAT-AP and GeoDCAT-AP. In its catalogue you can find data on air quality, emissions and climate change, water (state of masses and hydrological planning), biodiversity and protected areas, waste, energy and environmental assessment.

Featured datasets include Natura 2000 Network protected areas, bodies of water, and greenhouse gas emissions projections.

Example in Python — search for datasets:

import requests

BASE = "https://catalogo.datosabiertos.miteco.gob.es/ catalog"

# Search for datasets containing 'natura 2000'

busqueda = requests.get(

    f"{BASE}/api/3/action/package_search",

params={"q": "natura 2000", "rows": 3},

).json()

for ds in busqueda["result"]["results"]:

print(f"{ds['title']} ({ds['num_resources']} resources)")

Protected Areas of the Natura 2000 Network (13 resources)

Database of Natura 2000 Network Protected Areas of Spain (CNTRYES) (1 resources)

Protected Areas of the Natura 2000 Network - API - High Value Data (1 resources)

Technical comparison

Figure 1. Comparative table of the APIs from various public agencies discussed in this post. Source: Compiled by the author – datos.gob.es.

The availability of public APIs isn't just a matter of technical convenience. From a data perspective, these interfaces enable three critical capabilities:

• Pipeline automation: the periodic ingestion of public data can be orchestrated with standard tools (Airflow, Prefect, cron) without manual intervention or file downloads.
• Reproducibility: API URLs act as static references to authoritative sources, facilitating auditing and traceability in analytics projects.
• Interoperability: the use of open standards (REST, OGC API, DCAT-AP) allows heterogeneous sources to be crossed without depending on proprietary formats.

The public API ecosystem in Spain has different levels of development depending on the body and the sectoral scope. While entities such as the INE and AEMET have consolidated and well-documented interfaces, in other cases access is articulated through CKAN portals or traditional OGC services. The regulation regarding High Value Datasets (HVDs) is driving the progressive adoption of REST standards, although the degree of implementation evolves at different rates. For data professionals, these APIs are already a fully operational source that is increasingly common to integrate into data architectures in engineering and analytical environments.ás habitual en entornos analíticos y de ingeniería.

Content produced by Juan Benavente, a senior industrial engineer and expert in technologies related to the data economy. The content and views expressed in this publication are the sole responsibility of the author.

On Wednesday, March 4, the Cajasiete Big Data, Open Data and Blockchain Chair of the University of La Laguna held a webinar to present the winning ideas of the Cabildo de Tenerife Open Data Contest: Reuse Ideas. An event to highlight the potential of public information when it is put at the service of citizens. The recording of the presentation is available here.

In this post we will review what each of the winning projects consists of – which are still pending ideas for development in apps – and what challenges they would answer.

Cultiva+ Tenerife: precision agriculture for the Tenerife countryside

The first prize-winning project was born from a very specific need that every farmer on the island knows well: to make the right decisions at the right time. Which crop is most profitable this season? What are the weather conditions forecast for the coming weeks? Is there a fair or event in the sector that should not be missed?

Cultiva+ Tenerife is an application designed specifically for the agricultural sector that integrates open data from the Cabildo to answer these questions in a simple and intuitive way.

Specifically, it is aimed at both workers already established in the sector and new farmers. In the first case, the app would facilitate daily work through irrigation recommendations and other issues that improve production; while for new farmers the application would help to select the best plot to start an agricultural activity according to soil type, weather conditions, etc.

Figure 1. Possible uses of the Cultiva+ Tenerife application according to the type of user. Source: presentation by Cultiva+Tenerife in the Webinar "From data to innovation: Reuse ideas awarded in the I Open Data Contest of the Cabildo de Tenerife, Universidad de la Laguna".

The application would intuitively and clearly collect information such as:

• Price information: the farmer can consult the evolution of market prices of different products, which allows him to plan what to grow based on the expected profitability.
• Weather conditions: the app crosses weather data with the specific needs of each type of crop, helping to anticipate irrigation, protection or harvests.

• Agenda of activities of interest: agricultural fairs, technical conferences, calls for grants... All relevant information for the sector, centralized in one place.

  

Figure 2. Visual structure of the Cultiva+Tenerife application. Source: presentation by Cultiva+Tenerife in the Webinar "From data to innovation: Reuse ideas awarded in the I Open Data Contest of the Cabildo de Tenerife, Universidad de la Laguna".

Something that was highlighted as valuable about this project in the webinar is its focus on a group that has historically had less access to digital tools: farmers in Tenerife. The proposal does not seek to complicate their day-to-day life with unnecessary technology, but to simplify decisions that today are often made by eye or with incomplete information. Precision agriculture is no longer just a matter for large farms: with open data and a good application, it can be within the reach of any local producer.

Analysis of trends and models on tourism in Tenerife: when the data reveal a crisis

The second winning project addresses one of the most complex and urgent issues in the reality of Tenerife: the relationship between tourism, housing and the labour market. An equation with multiple variables that directly affects the quality of life of residents and that, until now, was difficult to analyse rigorously without access to reliable data.

The starting point of the project is revealing: in June 2024, 35% of the new employment contracts signed in Tenerife corresponded to the hospitality sector. A figure that perfectly illustrates the structural dependence of the island's economy on tourism, but which also opens up uncomfortable questions: to what extent is tourism growth transforming the housing market? Are you displacing habitual residents from certain areas? How will tourist arrivals evolve in the coming years?

This project proposes to answer these questions through an analysis and prediction model built with data science tools. Its developer proposes to use data such as the number of tourists staying in Tenerife according to category and area of establishment, available in datos.tenerife.es, to build models with Python and NumPy that allow identifying trends and projecting future scenarios.

The objectives of the project are ambitious but concrete:

• Analyse the relationship between tourist demand and accommodation supply, identifying which areas of the island suffer the greatest pressure and at what times of the year.
• To develop a predictive model capable of estimating the future arrival of tourists and their impact on the tourist housing sector.
• Contribute to mitigating the housing crisis by providing data and analysis that allow us to understand how tourism is affecting the availability of housing for residents.
• To support business and urban planning, offering companies, investors and administrations an analysis tool that facilitates strategic decision-making.

In short, it is a matter of putting the intelligence of data at the service of one of the most current debates that Tenerife has on the table.

The university as a bridge between data and society

The choice of the Cajasiete Big Data, Open Data and Blockchain Chair of the University of La Laguna as a space to give visibility to the winners is in itself a message: the University has a key role in the construction of the open data ecosystem in Tenerife.

This chair has been working for years on the border between academic research and the practical application of technologies such as big data analysis, blockchain or the reuse of public information. Their involvement in this competition and in the dissemination of its results reinforces the idea that open data is also a valuable resource for training, research and local economic development.

The success of this first call has confirmed that there was a real demand for this type of initiative. So much so that the Cabildo has already launched the II Open Data Contest: APP Development, which gives continuity to the process by taking ideas to the next level: the development of functional applications.

If in the first edition ideas and conceptual proposals were awarded, in this second edition the challenge is to build real solutions, with code, user interface and proven functionalities. The economic endowment is 6,000 euros divided into three prizes.

Projects such as Cultiva+ Tenerife or the Analysis of the impact of tourism on housing show that there are ideas with the potential to become useful and sustainable tools. This second phase is the opportunity to materialize them.

Every year, the international open knowledge advocacy organization Open Knowledge Foundation (OKFN) organizes Open Data Day (ODD), a framework initiative that will bring together activities around the world to demonstrate the value of open data. It is a meeting point for public administrations, civil society, universities, technology companies and citizens interested in the reuse of public information. It is, above all, an invitation to move from theory to practice: to open data, reuse it and turn it into concrete solutions.

From datos.gob.es, national open data portal, we join this celebration by also compiling other activities that put data and related technologies at the center. In this post we review some events that will be held during this month of March. Take note and write down the agenda!

Data against misinformation: celebrate Open Data Day with Open Data Barcelona Initiative

This meeting is part of the activities organized in Spain on the occasion of Open Data Day 2026, and is focused on the role of open data as a tool to strengthen the quality of public information and combat disinformation. The event will give visibility to projects that use open data to promote a more transparent democracy, encourage informed citizen participation and contribute to the development of responsible artificial intelligence based on reliable data.

• When? On Tuesday, March 10 at 5:30 p.m.
• Where? Ca l'Alier C/ de Pere IV, 362 in Barcelona
• Learn more

The future of Open Data: OKFN's anniversary

On the occasion of Open Data Day 2026, the Open Knowledge Foundation (OKFN) is organizing an online conference to bring together the open data community and celebrate two decades of CKAN, the tool that emerged from OKFN's work that today powers data portals around the world. The meeting will provide an opportunity to discuss the current role of open data and data infrastructures in the face of contemporary technical and political challenges. It is aimed at professionals from governments, civil society, the media, activist groups and all those interested in reflecting on the future of open data in a rapidly changing technological context, marked especially by the emergence of artificial intelligence tools.

• When? On Wednesday, March 11 from 11 a.m. to 4 p.m.
• Where? Online
• Learn more

Data as a public good:  European webinar

Organized by the data.europa.eu academy in the framework of Open Data Day, this webinar addresses how open data can act as a public good to improve decision-making in all territories, especially in rural areas. Through case studies from the United Kingdom and Ireland, the session will show how open information can identify local needs, reduce territorial inequalities and design evidence-based public policies that ensure more equitable access to essential services.

• When? Friday, March 13 from 10 a.m. to 11.30 a.m.
• Where? Online event
• Learn more

Solid World: innovation in the sharing and reuse of scientific data

This event will explore how to model, analyze, and share research data using technologies from the Solid* ecosystem. The session will feature representatives from W3C and Open Data Institute to present the SpOTy project, a web application for organizing and analyzing linguistic data that has migrated from RDF to Solid to give researchers greater control over the sharing of their data, also addressing challenges of interoperability and responsible reuse of scientific information.

*The Solid Ecosystem is a set of technologies, standards, and tools that enable individuals and organizations to control their own data on the web and decide how, when, and with whom it is shared.

• When? Monday, March 23 from 5 p.m. to 6 p.m.
• Where? Online event
• Learn more

How to prepare public portals for the AI era

The thirteenth edition of the Data Centric AI cycle, organized by the Open Data Institute (ODI), will explore how public data portals must evolve to adapt to new ways of interacting with datasets. It will address the transformation of infrastructures such as data.gov.uk, plans for the National Data Library and the role of academic research in the design of new public data architectures, combining preparation for artificial intelligence with a user-centric approach and reflecting on the social context surrounding data and AI.

• When? Thursday, March 26 from 5 pm to 6 pm
• Where? Online event
• Learn more

Online events on open data in different sectors with Open Data Week

 Open Data Week is an annual festival of events held every March in New York City and organized by the NYC Open Data team  in conjunction with BetaNYC and Data Through Design. The week commemorates the anniversary of the city's first open data law, signed on March 7, 2012, and also coincides with Open Data Day, reinforcing its connection with the international open data movement. Some of the scheduled activities will be in virtual format.

• When? From 22 to 29 March
• Where? Some events can be followed in streaming
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Data ethics keys for organizations

This session of the Data Ethics Professionals cycle  organized by ODI will focus on the main lessons learned by organizations that have initiated processes of integrating data ethics into their structures and workflows. The seminar will address common challenges such as obtaining management support, the practical incorporation of ethical tools and frameworks, and the management of workloads in organizational transformation processes.

• When? On Monday, March 30 from 2 p.m. to 3 p.m.
• Where? Online
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In short, the calendar for the coming weeks offers multiple opportunities to delve into the strategic value of open data and associated technologies. From local initiatives against disinformation to sectoral data spaces and European seminars on data as a public good, the ecosystem continues to grow and diversify. We encourage you to participate, share these calls and transfer the learnings to your organization. Because Open Data Day is just the starting point: true transformation is built throughout the year, connecting community, knowledge and action through open data.

These are some of the events that are scheduled for this month of March. In any case, don't forget to follow us on social networks so you don't miss any news about innovation and open data. We are on X and LinkedIn you can write to us if you need extra information.

The Organisation for Economic Co-operation and Development (OECD) has published the main findings of the 2025 edition of the Open, Useful and Re-usable Data Index (OURdata) and the Digital Government Index (DGI), two indices that evaluate the good work of governments in fields related to digital transformation.

Both studies are born from a central idea: "digital transformation is no longer optional for governments: it is an absolute necessity". It enables better services, smarter decision-making and collaboration across borders, but for this to work, a bold and balanced vision is needed, supported by a strong and reliable foundation. Thanks to the analysis offered by the two indices published by the OECD, it is possible to guide policies, prioritize investments and measure the progress of digital transformation in the public sector.

Specifically, the indices assess:

• OURdata Index: national efforts to design and implement useful and reusable open data policies.
• Digital Government Index (DGI): Governments' progress in building the foundations for a coherent and people-centred digital transformation.

Both analyses are based on data collected during the first half of 2025, covering initiatives and policies implemented between January 1, 2023 and December 31, 2024. Its results will also feed into the OECD Digital Government Outlook 2026, which will include more in-depth analysis, key trends and country notes.

Keys to the OURdata Index 2025

The OURdata Index 2025 shows important progress in the opening and reuse of public data in OECD countries. In this index, Spain is in the top 5, consolidating its position among the countries with the best open data policies.

The OECD average rises from 0.48 to 0.53 out of a total score of 1, with almost 60% of countries exceeding the 0.50 threshold. France leads the ranking, followed by South Korea, Poland, Estonia and the aforementioned Spain, as can be seen in the following graph.

Figure 1. Result by country of the Open, Useful and Re-usable Data Index (OURdata). Source: 2025 Open, Useful and Re-usable Data Index (OURdata), OECD.

To arrive at these data, the report analyzes three pillars, as in 2023:

• Pillar 1: Data availability. It measures the extent to which governments have adopted and implemented formal requirements for publishing open data. It also assesses the involvement of relevant actors to identify the demand for data and the availability of high-value datasets such as open data. It should be noted that, although the report talks about high value datasets, it is not the same concept that the EU handles. In the case of the OECD, other high-impact categories are also taken into account, such as health, education, crime and justice or public finances, among others.
• Pillar 2: Data accessibility. It assesses the existence of requirements to offer open data in reusable formats. In addition, it focuses on the degree to which high-value government datasets are published in a timely manner, in open formats, with standardized and detailed metadata, and through Application Programming Interfaces (APIs). It also analyzes the participation of relevant actors (stakeholders) in the central open data portal and in initiatives to improve its quality.
• Pillar 3: Government support for data reuse. It measures the extent to which governments play a proactive role in promoting the reuse of open data both inside and outside the public sector. Specifically, it analyzes whether there are alliances and organizes events that increase awareness of open data and promote its reuse; whether public officials are involved in the publication of open data and in data analysis and reuse activities; and whether impact assessments of open data are carried out and examples of reuse are collected.

The results show that, as in previous editions, OECD countries perform better in Data Availability (Pillar 1) and Data Accessibility (Pillar 2) than in Government Support for Data Reuse. However, Spain is an exception: it ranks third (0.91) in government support when it comes to promoting the creation of public value from open data and in measuring its real impact. In the rest of the pillars, 1 and 2, it is in 14th position, also ahead of the average of OECD countries.

Claves del Digital Government Index

The 2025 edition of the DGI assesses the digital maturity of governments. To do this, it analyzes whether they have the necessary foundations to leverage data and technology in a comprehensive transformation of the public sector focused on people.

As with the OURData index, the DGI score is based on the same methodology used in the 2023 edition, which allows a longitudinal evaluation to be carried out and progress between that year and 2025 to be compared. In this period, the OECD average in the DGI increased by 0.08 points, from 0.61 (out of 1) in 2023 to 0.70 in 2025, representing a total increase of 14%. Almost all governments exceeded the 0.50 threshold, and 17 of them were above the OECD average, including Spain.

The ranking is headed by South Korea, Australia, Portugal, the United Kingdom and Norway, with Spain in twelfth position, as shown in the following graph.

Figure 2. Result by country of the Digital Government Index. Source: 2025 Digital Government Index (DGI), OECD.

The DGI measures the maturity of digital government along six dimensions:

• Dimension 1: Digital by design.  It assesses how digital government policies enable the public sector to use digital tools and data consistently to transform services.
• Dimension 2: Data-driven.  It discusses advances in governance and the enablers for data access, sharing, and reuse in the public sector.
• Dimension 3: Government as a platform. It measures the deployment of common components such as guides, tools, data, digital identity, and software to drive consistent transformation of processes and services.
• Dimension 4: Open by default. It assesses openness beyond open data, including the use of technologies and data to communicate and engage with different actors.
• Dimension 5: User-centered. It measures the ability of governments to place people's needs at the centre of the design and delivery of policies and services.
• Dimension 6: Proactivity. It analyzes the ability to anticipate the needs of users and service providers to proactively offer public services.

The DGI assessment focuses on both the strategic and operational levels. Therefore, for each dimension, it examines four cross-cutting facets of the policy cycle: strategic approach (strategies and general frameworks), policy levers (resources and tools), implementation (concrete practices), and monitoring (monitoring and evaluation).

While countries have made progress compared to 2023, the 2025 results show that there is still room to increase the pace and depth of digital government policies. As in 2023, OECD countries excel in the Digital by Design, Data-Driven Public Sector, Government as a Platform and User-Centric dimensions, with widespread improvements in their scores. These advances are explained by the strengthening of governance and the use of data, the development of digital infrastructures -such as digital identity systems and service platforms-, the consolidation of digital talent in public administrations and the adoption of service standards.

In contrast, the Proactivity and Open dimensions by default continue to show lower performance, as was already the case in 2023. This is due to weaker results in the use and governance of artificial intelligence in the public sector, in service design and delivery practices, and in open data. Even so, improvements are observed in areas such as the availability of governance instruments for a reliable use of AI and the expansion of tools to test and monitor whether services are adapted to the needs of users.

In this case, Spain does follow the general trend, standing out especially in Digital by design where it enters the top 10 with a ninth position, although with one exception: it also obtains a good score in Proactivity, with a 12th place. In the rest of the indicators, it remains fairly stable, between positions 13 and 19.

Conclusion

Governments around the world face a common challenge: rigid structures, slow processes, and rules that sometimes make it difficult to respond with agility to today's challenges. As a result, digital modernization has become a strategic necessity.

Embracing digital technologies, connecting data, and working with agile methodologies allows governments to be faster, more efficient, and proactive while remaining active in accountability and facilitates collaboration between institutions and countries. Studies conducted by the OECD allow countries to determine their areas for improvement, facilitating informed decision-making regarding digital infrastructure, data or the use of AI.

To find out more about the details of Spain's position, we will have to wait for the country notes to be published in the OECD Digital Government Outlook 2026, but for now, we can take note of our strengths (government support for the reuse of data or the development of digital government policies) and the challenges to be faced (continuing to promote the accessibility and availability of data).

Since its origins, the open data movement has focused mainly on promoting the openness of data and promoting its reuse. The objective that has articulated most of the initiatives, both public and private, has been to overcome the obstacles to publishing increasingly complete data catalogues and to ensure that public sector information is available so that citizens, companies, researchers and the public sector itself could create economic and social value.

However, as we have taken steps towards an economy that is increasingly dependent on data and, more recently, on artificial intelligence – and in the near future on the possibilities that autonomous agents bring us through agentic artificial intelligence – priorities have been changing and the focus has been shifting towards issues such as improving the quality of published data.

It is no longer enough for the datasets to be published in an open data portal complying with good practices, or even for the data to meet quality standards at the time of publication. It is also necessary that this publication of the datasets meets service levels that transform the mere provision into an operational commitment that mitigates the uncertainties that often hinder reuse.

When a developer integrates a real-time transportation data API into their mobility app, or when a data scientist works on an AI model with historical climate data, they are taking a risk if they are uncertain about the conditions under which the data will be available. If at any given time the published data becomes unavailable because the format changes without warning, because the response time skyrockets, or for any other reason, the automated processes fail and the data supply chain breaks, causing cascading failures in all dependent systems.

In this context, the adoption  of service level agreements (SLAs) could be the next step for open data portals to evolve from the usual "best effort" model  to become critical, reliable and robust digital infrastructures.

What are an SLA  and a Data Contract in the context of open data?

In the context of site reliability engineering (SRE), an SLA is a contract negotiated between a service provider and its customers in order to set the level of quality of the service provided. It is, therefore, a tool that helps both parties to reach a consensus on aspects such as response time, time availability or available documentation.

In an open data portal, where there is often no direct financial consideration, an SLA could help answer questions such as:

• How long will the portal and its APIs be available?
• What response times can we expect?
• How often will the datasets be updated?
• How are changes to metadata, links, and formatting handled?
• How will incidents, changes and notifications to the community be managed?

In addition, in this transition towards greater operational maturity, the concept, still immature, of the data contract (data contract) emerges. If the SLA is an agreement that defines service level expectations, the data contract is an implementation that formalizes this commitment. A data contract would not only specify the schema and format, but would act as a safeguard: if a system update attempts to introduce a change that breaks the promised structure or degrades the quality of the data, the data contract allows you to detect and block such an anomaly before it affects end users.

INSPIRE as a starting point: availability, performance and capacity

The European Union's Infrastructure for Spatial Information (INSPIRE) has established one of the world's most rigorous frameworks for quality of service for geospatial data. Directive 2007/2/EC, known as INSPIRE, currently in its version 5.0, includes some technical obligations that could serve as a reference for any modern data portal. In particular , Regulation (EC) No 976/2009 sets out criteria that could well serve as a standard for any strategy for publishing high-value data:

• Availability: Infrastructure must be available 99% of the time during normal operating hours.
• Performance: For a visualization service, the initial response should arrive in less than 3 seconds.
• Capacity: For a location service, the minimum number of simultaneous requests served with guaranteed throughput must be 30 per second.

To help comply with these service standards, the European Commission offers tools such as the INSPIRE Reference Validator. This tool helps not only to verify syntactic interoperability (that the XML or GML is well formed), but also to ensure that network services comply with the technical specifications that allow those SLAs to be measured.

At this point, the demanding SLAs of the European spatial data infrastructure make us wonder if we should not aim for the same for critical health, energy or mobility data or for any other high-value dataset.

What an SLA could cover on an open data platform

When we talk about open datasets in the broad sense, the availability of the portal is a necessary condition, but not sufficient. Many issues that affect the reuser community are not complete portal crashes, but more subtle errors such as broken links, datasets that are not updated as often as indicated, inconsistent formats between versions, incomplete metadata, or silent changes in API behavior or dataset column names.

Therefore, it would be advisable to complement the SLAs of the portal infrastructure with "data health" SLAs that can be based on  already established reference frameworks such as:

• Quality models such as ISO/IEC 25012, which allows the quality of the data to be broken down into measurable dimensions such as accuracy (that the data represents reality), completeness (that necessary values are not missing) and consistency (that there are no contradictions between tables or formats) and convert them into measurable requirements.
• FAIR Principles, which stands for Findable, Accessible, Interoperable, and Reusable. These principles emphasize that digital assets should not only be available, but should be traceable using persistent identifiers, accessible under clear protocols, interoperable through the use of standard vocabularies, and reusable thanks to clear licenses and documented provenance. The FAIR principles can be put into practice by systematically measuring the quality of the metadata that makes location, access and interoperability possible. For example,  data.europa.eu's Metadata Quality Assurance (MQA) service  helps you automatically evaluate catalog metadata, calculate metrics, and provide recommendations for improvement.

To make these concepts operational, we can focus on four examples where establishing specific service commitments would provide a differential value:

• Catalog compliance and currency: The SLA could ensure that the metadata is always aligned with the data it describes. A compliance commitment would ensure that the portal undergoes periodic validations (following specifications such as DCAT-AP-ES or HealthDCAT-AP) to prevent the documentation from becoming obsolete with respect to the actual resource.
• Schema stability and versioning: One of the biggest enemies of automated reuse is "silent switching." If a column changes its name or a data type changes, the data ingestion flows will fail immediately. A service level commitment might include a versioning policy. This would mean that any changes that break compatibility would be announced at least notice, and preferably keep the previous version in parallel for a reasonable amount of time.
• Freshness and refresh frequency: It's not uncommon to find datasets labeled as daily but last actually modified months ago. A good practice could be the definition of publication latency indicators. A possible SLA would establish the value of the average time between updates and would have alert systems that would automatically notify if a piece of data has not been refreshed according to the frequency declared in its metadata.
• Success rate: In the world of data APIs, it's not enough to just receive an HTTP 200 (OK) code to determine if the answer is valid. If the response is, for example, a JSON with no content, the service is not useful. The service level would have to measure the rate of successful responses with valid content, ensuring that the endpoint not only responds, but delivers the expected information.

A first step, SLA, SLO, and SLI: measure before committing

Since establishing these types of commitments is really complex, a possible strategy to take action gradually is to adopt a pragmatic approach based on industry best practices. For example, in reliability engineering, a hierarchy of three concepts is proposed that helps avoid unrealistic compromises:

• Service Level Indicator (SLI): it is the measurable and quantitative indicator. It represents the technical reality at a given moment. Examples of SLI in open data could be the "percentage of successful API requests", "p95 latency" (the response time of 95% of requests) or the "percentage of download links that do not return error".
• Service Level Objective (SLO): this is the internal objective set for this indicator. For example: "we want 99.5% of downloads to work correctly" or "p95 latency must be less than 800ms". It is the goal that guides the work of the technical team.
• Service Level Agreement (SLA): is the public and formal commitment to those objectives. This is the promise that the data portal makes to its community of reusers and that includes, ideally, the communication channels and the protocols for action in the event of non-compliance.

Figure 1. Visual to explain the difference between SLI, SLO and SLA. Source: own elaboration - datos.gob.es.

This distinction is especially valuable in the open data ecosystem due to the hybrid nature of a service in which not only an infrastructure is operated, but the data lifecycle is managed.

In many cases, the first step might be not so much to publish an ambitious SLA right away, but to start by defining your SLIs and looking at your SLOs. Once measurement was automated and service levels stabilized and predictable, it would be time to turn them into a public commitment (SLA).

Ultimately, implementing service tiers in open data could have a multiplier effect. Not only would it reduce technical friction for developers and improve the reuse rate, but it would make it easier to integrate public data into AI systems and autonomous agents. New uses such as the evaluation of generative Artificial Intelligence systems, the generation and validation of synthetic datasets or even the improvement of the quality of open data itself would benefit greatly.

Establishing a data SLA would, above all, be a powerful message: it would mean that the public sector not only publishes data as an administrative act, but operates it as a digital service that is highly available, reliable, predictable and, ultimately, prepared for the challenges of the data economy.

Content created by Jose Luis Marín, Senior Consultant in Data, Strategy, Innovation & Digitalisation. The content and views expressed in this publication are the sole responsibility of the author.

For more than a decade, open data platforms have measured their impact through relatively stable indicators: number of downloads, web visits, documented reuses, applications or services created based on them, etc. These indicators worked well in an ecosystem where users – companies, journalists, developers, anonymous citizens, etc. – directly accessed the original sources to query, download and process the data.

However, the panorama has changed radically. The emergence of generative artificial intelligence models has transformed the way people access information. These systems generate responses without the need for the user to visit the original source, which is causing a global drop in web traffic in media, blogs and knowledge portals.

In this new context, measuring the impact of an open data platform requires rethinking traditional indicators to incorporate new ones to the metrics already used that also capture the visibility and influence of data in an ecosystem where human interaction is changing.

Figure 1. Metrics for measuring the impact of open data in the age of AI.

A structural change: from click to indirect consultation

The web ecosystem is undergoing a profound transformation driven by the rise of large language models (LLMs). More and more people are asking their questions directly to systems such as ChatGPT, Copilot, Gemini or Perplexity, obtaining immediate and contextualized answers without the need to resort to a traditional search engine.

At the same time, those who continue to use search engines such as Google or Bing are also experiencing relevant changes derived from the integration of artificial intelligence on these platforms. Google, for example, has incorporated features such as AI Overviews, which offers automatically generated summaries at the top of the results, or AI Mode, a conversational interface that allows you to drill down into a query without browsing links. This generates a phenomenon known as Zero-Click: the user performs a search on an engine such as Google and gets the answer directly on the results page itself. As a result, you don't need to click on any external links, which limits visits to the original sources from which the information is extracted.

All this implies a key consequence: web traffic is no longer a reliable indicator of impact. A website can be extremely influential in generating knowledge without this translating into visits.

New metrics to measure impact

Faced with this situation, open data platforms need new metrics that capture their presence in this new ecosystem. Some of them are listed below.

1. Share of Model (SOM): Presence in AI models

Inspired by digital marketing metrics, the Share of Model measures how often AI models mention, cite, or use data from a particular source. In this way, the SOM helps to see which specific data sets (employment, climate, transport, budgets, etc.) are used by the models to answer real questions from users, revealing which data has the greatest impact.

This metric is especially valuable because it acts as an indicator of algorithmic trust: when a model mentions a web page, it is recognizing its reliability as a source. In addition, it helps to increase indirect visibility, since the name of the website appears in the response even when the user does not click.

2. Sentiment analysis: tone of mentions in AI

Sentiment analysis allows you to go a step beyond the Share of Model, as it not only identifies if an AI model mentions a brand or domain, but how it does so. Typically, this metric classifies the tone of the mention into three main categories: positive, neutral, and negative.

Applied to the field of open data, this analysis helps to understand the algorithmic perception of a platform or dataset. For example, it allows detecting whether a model uses a source as an example of good practice, if it mentions it neutrally as part of an informative response, or if it associates it with problems, errors, or outdated data.

This information can be useful to identify opportunities for improvement, strengthen digital reputation, or detect potential biases in AI models that affect the visibility of an open data platform.

3. Categorization of prompts: in which topics a brand stands out

Analyzing the questions that users ask allows you to identify what types of queries a brand appears most frequently in. This metric helps to understand in which thematic areas – such as economy, health, transport, education or climate – the models consider a source most relevant.

For open data platforms, this information reveals which datasets are being used to answer real user questions and in which domains there is greater visibility or growth potential. It also allows you to spot opportunities: if an open data initiative wants to position itself in new areas, it can assess what kind of content is missing or what datasets could be strengthened to increase its presence in those categories.

4. Traffic from AI: clicks from digests generated

Many models already include links to the original sources. While many users don't click on such links, some do. Therefore, platforms can start measuring:

• Visits from AI platforms (when these include links).
• Clicks from rich summaries in AI-integrated search engines.

This means a change in the distribution of traffic that reaches websites from the different channels. While organic traffic—traffic from traditional search engines—is declining, traffic referred from language models is starting to grow.

This traffic will be smaller in quantity than traditional traffic, but more qualified, since those who click from an AI usually have a clear intention to go deeper.

It is important that these aspects are taken into account when setting growth objectives on an open data platform.

5. Algorithmic Reuse: Using Data in Models and Applications

Open data powers AI models, predictive systems, and automated applications. Knowing which sources have been used for their training would also be a way to know their impact. However, few solutions directly provide this information. The European Union is working to promote transparency in this field, with measures such as the template for documenting training data for general-purpose models, but its implementation – and the existence of exceptions to its compliance – mean that knowledge is still limited.

Measuring the increase in access to data through APIs could give an idea of its use in applications to power intelligent systems. However, the greatest potential in this field lies in collaboration with companies, universities and developers immersed in these projects, so that they offer a more realistic view of the impact.

Conclusion: Measure what matters, not just what's easy to measure

A drop in web traffic doesn't mean a drop in impact. It means a change in the way information circulates. Open data platforms must evolve towards metrics that reflect algorithmic visibility, automated reuse,  and integration into AI models.

This doesn't mean that traditional metrics should disappear. Knowing the accesses to the website, the most visited or the most downloaded datasets continues to be invaluable information to know the impact of the data provided through open platforms. And it is also essential to monitor the use of data when generating or enriching products and services, including artificial intelligence systems. In the age of AI, success is no longer measured only by how many users visit a platform, but also by how many intelligent systems depend on its information and the visibility that this provides.

Therefore, integrating these new metrics alongside traditional indicators through a web analytics and SEO strategy * allows for a more complete view of the real impact of open data. This way we will be able to know how our information circulates, how it is reused and what role it plays in the digital ecosystem that shapes society today.

*SEO (Search Engine Optimization) is the set of techniques and strategies aimed at improving the visibility of a website in search engines.

In the last fifteen years we have seen how public administrations have gone from publishing their first open datasets to working with much more complex concepts. Interoperability, standards, data spaces or digital sovereignty are some of the trendy concepts. And, in parallel, the web has also changed. That open, decentralized, and interoperable space that inspired the first open data initiatives has evolved into a much more complex ecosystem, where technologies, new standards, and at the same time important challenges such as information silos to digital ethics and technological concentration coexist.

To talk about all this, today we are fortunate to have two voices that have not only observed this evolution, but have been direct protagonists of it at an international level:

• Josema Alonso, with more than twenty-five years of experience working on the open web, data and digital rights, has worked at the World Wide Web Foundation, the Open Government Partnership and the World Economic Forum, among others.
• Carlos Iglesias, an expert in web standards, open data and open government, has advised administrations around the world on more than twenty projects. He has been actively involved in communities such as W3C, the Web Foundation and the Open Knowledge Foundation.

Listen to the full podcast (only available in Spanish)

Summary / Transcript of the interview

1. At what point do you think we are now and what has changed with respect to that first stage of open data?

Carlos Iglesias: Well, I think what has changed is that we understand that today that initial battle cry of "we want the data now" is not enough. It was a first phase that at the time was very useful and necessary because we had to break with that trend of having data locked up, not sharing data. Let's say that the urgency at that time was simply to change the paradigm and that is why the battle cry was what it was. I have been involved, like Josema, in studying and analyzing all those open data portals and initiatives that arose from this movement. And I have seen that many of them began to grow without any kind of strategy. In fact, several fell by the wayside or did not have a clear vision of what they wanted to do. Simple practice I believe came to the conclusion that the publication of data alone was not enough. And from there I think that they have been proposing, a little with the maturity of the movement, that more things have to be done, and today we talk more about data governance, about opening data with a specific purpose, about the importance of metadata, models. In other words, it is no longer simply having data for the sake of having it, but there is one more vision of data as one of the most valuable elements today, probably, and also as a necessary infrastructure for many things to work today. Just as infrastructures such as road or public transport networks or energy were key in their day. Right now we are at the moment of the great explosion of artificial intelligence. A series of issues converge that have made this explode and the change is immense, despite the fact that we are only talking about perhaps a little more than ten or fifteen years since that first movement of "we want the data now". I think that right now the panorama is completely different.

Josema Alonso: Yes, it is true that we had that idea of "you publish that someone will come and do something with it". And what that did is that people began to become aware. But I, personally, could not have imagined that a few years later we would have even had a directive at European level on the publication of open data. It was something, to be honest, that we received with great pleasure. And then it will begin to be implemented in all member states. That moved consciences a little and moved practices, especially within the administration. There was a lot of fear of "let's see if I put something in there that is problematic, that is of poor quality, that I will be criticized for it", etc. But it began to generate a culture of data and the usefulness of very important data. And as Carlos also commented in recent years, I think that no one doubts this anymore. The investments that are being made, for example, at European level and in Member States, including in our country, in Spain, in the promotion and development of data spaces, are hundreds of millions of euros. Nobody has that kind of doubt anymore and now the focus is more on how to do it well, on how to get everyone to interoperate. That is, when a European data space is created for a specific sector, such as agriculture or health, all countries and organisations can share data in the best possible way, so that they can be exchanged through common models and that they are done within trusted environments.

2. In this context, why have standards become so essential?

Josema Alonso: I think it's because of everything we've learned over the years. We have learned that it is necessary for people to be able to have a certain freedom when it comes to developing their own systems. The architecture of the website itself, for example, is how it works, it does not have a central control or anything, but each participant within the website manages things in their own way. But there are clear rules of how those things then have to interact with each other, otherwise it wouldn't work, otherwise we wouldn't be able to load a web page in different browsers or on different mobile phones. So, what we are seeing lately is that there is an increasing attempt to figure out how to reach that type of consensus in a mutual benefit. For example, part of my current work for the European Commission is in the Semantic Interoperability Community, where we manage the creation of uniform models that are used across Europe, definitions of basic standard vocabularies that are used in all systems. In recent years it has also been instrumentalized in a way that supports, let's say, that consensus through regulations that have been issued, for example, at the European level. In recent years we have seen the regulation of data, the regulation of data governance and the regulation of artificial intelligence, things that also try to put a certain order and barriers. It's not that everyone goes through the middle of the mountain, because if not, in the end we won't get anywhere, but we're all going to try to do it by consensus, but we're all going to try to drive within the same road to reach the same destination together. And I think that, from the part of the public administrations, apart from regulating, it is very interesting that they are very transparent in the way it is done. It is the way in which we can all come to see that what is built is built in a certain way, the data models that are transparent, everyone can see them participate in their development. And this is where we are seeing some shortcomings in algorithmic and artificial intelligence systems, where we do not know very well the data they use or where it is hosted. And this is where perhaps we should have a little more influence in the future. But I think that as long as this duality is achieved, of generating consensus and providing a context in which people feel safe developing it, we will continue to move in the right direction.

Carlos Iglesias: If we look at the principles that made the website work in its day, there is also a lot of focus on the community part and leaving an open platform that is developed in the open, with open standards in which everyone could join. The participation of everyone was sought to enrich that ecosystem. And I think that with the data we should think that this is the way to go. In fact, it's kind of also a bit like the concept that I think is behind data spaces. In the end, it is not easy to do something like that. It's very ambitious and we don't see an invention like the web every day.

3. From your perspective, what are the real risks of data getting trapped in opaque infrastructures or models? More importantly, what can we do to prevent it?

Carlos Iglesias: Years ago we saw that there was an attempt to quantify the amount of data that was generated daily. I think that now no one even tries it, because it is on a completely different scale, and on that scale there is only one way to work, which is by automating things. And when we talk about automation, in the end what you need are standards, interoperability, trust mechanisms, etc. If we look ten or fifteen years ago, which were the companies that had the highest share price worldwide, they were companies such as Ford or General Electric. If you look at the top ten worldwide, today there are companies that we all know and use every day, such as Meta, which is the parent company of Facebook, Instagram, WhatsApp and others, or Alphabet, which is the parent company of Google. In other words, in fact, I think I'm a little hesitant right now, but probably of the ten largest listed companies in the world, all are dedicated to data. We are talking about a gigantic ecosystem and, in order for this to really work and remain an open ecosystem from which everyone can benefit, the key is standardization.

Josema Alonso: I agree with everything Carlos said and we have to focus on not getting trapped. And above all, from the public administrations there is an essential role to play. I mentioned before about regulation, which sometimes people don't like very much because the regulatory map is starting to be extremely complicated. The European Commission, through an omnibus decree, is trying to alleviate this regulatory complexity and, as an example, in the data regulation itself, which obliges companies that have data to facilitate data portability to their users. It seems to me that it is something essential. We're going to see a lot of changes in that. There are three things that always come to mind; permanent training is needed. This changes every day at an astonishing speed. The volumes of data that are now managed are huge. As Carlos said before, a few days ago I was talking to a person who manages the infrastructure of one of the largest streaming  platforms globally and he told me that they are receiving requests for data generated by artificial intelligence in such a large volume in just one week as the entire catalog they have available. So the administration needs to have permanent training on these issues of all kinds, both at the forefront of technology as we have just mentioned, and what we talked about before, how to improve interoperability, how to create better data models, etc. Another is the common infrastructure in Europe, such as the future European digital wallet, which would be the equivalent of the national citizen folder.  A super simple example we are dealing with is the birth certificate. It is very complicated to try to integrate the systems of twenty-seven different countries, which in turn have regional governments and which in turn have local governments. So, the more we invest in common infrastructure, both at the semantic level and at the level of the infrastructure itself, the cloud, etc., I think the better we will do. And then the last one, which is the need for distributed but coordinated governance.  Each one is governed by certain laws at local, national or European level. It is good that we begin to have more and more coordination in the higher layers and that those higher layers permeate to the lower layers and the systems are increasingly easier to integrate and understand each other. Data spaces are one of the major investments at the European level, where I believe this is beginning to be achieved. So, to summarize three things that are very practical to do: permanent training, investing in common infrastructure and that governance continues to be distributed, but increasingly coordinated.

At the crossroads of the 21st century, cities are facing challenges of enormous magnitude. Explosive population growth, rapid urbanization and pressure on natural resources are generating unprecedented demand for innovative solutions to build and manage more efficient, sustainable and livable urban environments.

Added to these challenges is the impact of climate change on cities. As the world experiences alterations in weather patterns, cities must adapt and transform to ensure long-term sustainability and resilience.

One of the most direct manifestations of climate change in the urban environment is the increase in temperatures. The urban heat island effect, aggravated by the concentration of buildings and asphalt surfaces that absorb and retain heat, is intensified by the global increase in temperature. Not only does this affect quality of life by increasing cooling costs and energy demand, but it can also lead to serious public health problems, such as heat stroke and the aggravation of respiratory and cardiovascular diseases.

The change in precipitation patterns is another of the critical effects of climate change affecting cities. Heavy rainfall episodes and more frequent and severe storms can lead to urban flooding, especially in areas with insufficient or outdated drainage infrastructure. This situation causes significant structural damage, and also disrupts daily life, affects the local economy and increases public health risks due to the spread of waterborne diseases.

In the face of these challenges, urban planning and design must evolve. Cities are adopting sustainable urban planning strategies that include the creation of green infrastructure, such as parks and green roofs, capable of mitigating the heat island effect and improving water absorption during episodes of heavy rainfall. In addition, the integration of efficient public transport systems and the promotion of non-motorised mobility are essential to reduce carbon emissions.

The challenges described also influence building regulations and building codes. New buildings must meet higher standards of energy efficiency, resistance to extreme weather conditions and reduced environmental impact. This involves the use of sustainable materials and construction techniques that not only reduce greenhouse gas emissions, but also offer safety and durability in the face of extreme weather events.

In this context,  urban digital twins have established themselves as one of the key tools to support planning, management and decision-making in cities. Its potential is wide and transversal: from the simulation of urban growth scenarios to the analysis of climate risks, the evaluation of regulatory impacts or the optimization of public services. However, beyond technological discourse and 3D visualizations, the real viability of an urban digital twin depends on a fundamental data governance issue: the availability, quality, and consistent use of standardized open data.

What do we mean by urban digital twin?

An urban digital twin is not simply a three-dimensional model of the city or an advanced visualization platform. It is a structured and dynamic digital representation of the urban environment, which integrates:

• The geometry and semantics of the city (buildings, infrastructures, plots, public spaces).

• Geospatial reference data  (cadastre, planning, networks, environment).

• Temporal and contextual information, which allows the evolution of the territory to be analysed and scenarios to be simulated.

• In certain cases, updatable data streams from sensors, municipal information systems or other operational sources.

From a standards perspective, an urban digital twin can be understood as an ecosystem of interoperable data and services, where different models, scales and domains (urban planning, building, mobility, environment, energy) are connected in a coherent way. Its value lies not so much in the specific technology used as in its ability to align heterogeneous data under common, reusable and governable models.

In addition, the integration of real-time data into digital twins allows for more efficient city management in emergency situations. From natural disaster management to coordinating mass events, digital twins provide decision-makers with a real-time view of the urban situation, facilitating a rapid and coordinated response.

In order to contextualize the role of standards and facilitate the understanding of the inner workings of an urban digital twin, Figure 1 presents a conceptual diagram of the network of interfaces, data models, and processes that underpin it. The diagram illustrates how different sources of urban information – geospatial reference data, 3D city models, regulatory information and, in certain cases, dynamic flows – are integrated through standardised data structures and interoperable services.

Figure 1. Conceptual diagram of the network of interfaces and connected processes in urban digital twin platforms. Source: own elaboration – datos.gob.es.

In these environments, CityGML and CityJSON act as urban information models that allow the city to be digitally described in a structured and understandable way. In practice, they function as "common languages" to represent buildings, infrastructures and public spaces, not only from the point of view of their shape (geometry), but also from the point of view of their meaning (e.g. whether an object is a residential building, a public road or a green area). As a result, these models form the basis on which urban analyses and the simulation of different scenarios are based.

In order for these three-dimensional models to be visualized in an agile way in web browsers and digital applications, especially when dealing with large volumes of information, 3D Tiles can be incorporated. This standard allows urban models to be divided into manageable fragments, facilitating their progressive loading and interactive exploration, even on devices with limited capacities.

The access, exchange and reuse of all this information is usually articulated through OGC APIs, which can be understood as standardised interfaces that allow different applications to consult and combine urban data in a consistent way. These interfaces make it possible, for example, for an urban planning platform, a climate analysis tool or a citizen viewer to access the same data without the need to duplicate or transform it in a specific way.

In this way, the diagram reflects the flow of data from the original sources to the final applications, showing how the use of open standards allows for a clear separation of data, services, and use cases. This separation is key to ensuring interoperability between systems, the scalability of digital solutions and the sustainability of the urban digital twin over time, aspects that are addressed transversally in the rest of the document.

Real example: Urban regeneration project in Barcelona

An example of the impact of urban digital twins on urban construction and management can be found in the urban regeneration project of the Plaza de las Glòries Catalanes, in Barcelona (Spain). This project aimed to transform one of the city's most iconic urban areas into a more accessible, greener and sustainable public space.

Figure 2. General view. Image by the joint venture Fuses Viader + Perea + Mansilla + Desvigne.

By using digital twins from the initial phases of the project, the design and planning teams were able to create detailed digital models that represented not only the geometry of existing buildings and infrastructure, but also the complex interactions between different urban elements, such as traffic, public transport and pedestrian areas.

These models not only facilitated the visualization and communication of the proposed design among all stakeholders, but also allowed different scenarios to be simulated and their impact on mobility, air quality, and walkability to be assessed. As a result, more informed decisions could be made, contributing decisively to the overall success of the urban regeneration initiative.

The critical role of open data in urban digital twins

In the context of urban digital twins, open data should not be understood as an optional complement or as a one-off action of transparency, but as the structural basis on which sustainable, interoperable and reusable digital urban systems are built over time. An urban digital twin can only fulfil its function as a planning, analysis and decision-support tool if the data that feeds it is available, well defined and governed according to common principles.

When a digital twin develops without a clear open data strategy, it tends to become a closed system and dependent on specific technology solutions or vendors. In these scenarios, updating information is costly and complex, reuse in new contexts is limited, and the twin quickly loses its strategic value, becoming obsolete in the face of the real evolution of the city it intends to represent. This lack of openness also hinders integration with other systems and reduces the ability to adapt to new regulatory, social or environmental needs.

One of the main contributions of urban digital twins is their ability to base public decisions on traceable and verifiable data. When supported by accessible and understandable open data, these systems allow us to understand not only the outcome of a decision, but also the data, models and assumptions that support it, integrating geospatial information, urban models, regulations and, in certain cases, dynamic data. This traceability is key to accountability, the evaluation of public policies and the generation of trust at both the institutional and citizen levels. Conversely, in the absence of open data, the analyses and simulations that support urban decisions become opaque, making it difficult to explain how and why a certain conclusion has been reached and weakening confidence in the use of advanced technologies for urban management.

Urban digital twins also require the collaboration of multiple actors – administrations, companies, universities and citizens – and the integration of data from different administrative levels and sectoral domains. Without an approach based on standardized open data, this collaboration is hampered by technical and organizational barriers: each actor tends to use different formats, models, and interfaces, which increases integration costs and slows down the creation of reuse ecosystems around the digital twin.

Another significant risk associated with the absence of open data is the increase in technological dependence and the consolidation of information silos. Digital twins built on non-standardized or restricted access data are often tied to proprietary solutions, making it difficult to evolve, migrate, or integrate with other systems. From the perspective of data governance, this situation compromises the sovereignty of urban information and limits the ability of administrations to maintain control over strategic digital assets.

Conversely, when urban data is published as standardised open data, the digital twin can evolve as a public data infrastructure, shared, reusable and extensible over time. This implies not only that the data is available for consultation or visualization, but that it follows common information models, with explicit semantics, coherent geometry and well-defined access mechanisms that facilitate its integration into different systems and applications.

This approach allows the urban digital twin to act as a common database on which multiple use cases can be built —urban planning, license management, environmental assessment, climate risk analysis, mobility, or citizen participation—without duplicating efforts or creating inconsistencies. The systematic reuse of information not only optimises resources, but also guarantees coherence between the different public policies that have an impact on the territory.

From a strategic perspective, urban digital twins based on standardised open data also make it possible to align local policies with the European principles of interoperability, reuse and data sovereignty. The use of open standards and common information models facilitates the integration of digital twins into wider initiatives, such as sectoral data spaces or digitalisation and sustainability strategies promoted at European level. In this way, cities do not develop isolated solutions, but digital infrastructures coherent with higher regulatory and strategic frameworks, reinforcing the role of the digital twin as a transversal, transparent and sustainable tool for urban management.

Figure 3. Strategies to implement urban digital twins. Source: own elaboration – datos.gob.es.

Conclusion

Urban digital twins represent a strategic opportunity to transform the way cities plan, manage and make decisions about their territory. However, their true value lies not in the technological sophistication of the platforms or the quality of the visualizations, but in the robustness of the data approach on which they are built.

Urban digital twins can only be consolidated as useful and sustainable tools when they are supported by standardised, well-governed open data designed from the ground up for interoperability and reuse. In the absence of these principles, digital twins risk becoming closed, difficult to maintain, poorly reusable solutions that are disconnected from the actual processes of urban governance.

The use of common information models, open standards and interoperable access mechanisms allows the digital twin to evolve as a public data infrastructure, capable of serving multiple public policies and adapting to social, environmental and regulatory changes affecting the city. This approach reinforces transparency, improves institutional coordination, and facilitates decision-making based on verifiable evidence.

In short, betting on urban digital twins based on standardised open data is not only a technical decision, but also a public policy decision in terms of data governance. It is this vision that will enable digital twins to contribute effectively to addressing major urban challenges and generating lasting public value for citizens.