Comercio

In a world where immediacy is becoming increasingly important, predictive commerce has become a key tool for anticipating consumer behaviors, optimizing decisions, and offering personalized experiences. It's no longer just about reacting to the customer's needs, it's  about predicting what they want before they even know it.

In this article we are going to explain what predictive commerce is and the importance of open data in it, including real examples.

What is predictive commerce?

Predictive commerce is a strategy based on data analysis to anticipate consumers' purchasing decisions. It uses artificial intelligence algorithms and statistical models to identify patterns of behavior, preferences, and key moments in the consumption cycle. Thanks to this, companies can know relevant information about which products will be most in demand, when and where a purchase will be made or which customers are most likely to purchase a certain brand.

This is of great importance in a market like the current one, where there is a saturation of products and competition. Predictive commerce allows companies to adjust inventories, prices, marketing campaigns or logistics in real time, becoming a great competitive advantage.

The role of open data in predictive commerce

These models are fed by large volumes of data: purchase history, web browsing, location or comments on social networks, among others. But the more accurate and diverse the data, the more fine-tuned the predictions will be. This is where open data plays a fundamental role, as it allows new variables to be taken into account when defining consumer behavior. Among other things, open data can help us:

• Enrich prediction models with external information such as demographic data, urban mobility or economic indicators.
• Detect regional patterns that influence consumption, such as the impact of climate on the sale of certain seasonal products.
• Design more inclusive strategies by incorporating public data on the habits and needs of different social groups.

The following table shows examples of datasets available in datos.gob.es that can be used for these tasks, at a national level, although many autonomous communities and city councils also publish this type of data along with others also of interest.

Real-world use cases

For years, we have already found companies that are using this type of data to optimize their business strategies. Let's look at some examples:

• Using weather data to optimize stock in large supermarkets

 Walmart department stores use AI algorithms that incorporate weather data (such as heat waves, storms, or temperature changes) along with historical sales data, events, and digital trends, to forecast demand at a granular level and optimize inventories. This allows the replenishment of critical products to be automatically adjusted according to anticipated weather patterns. In addition, Walmart mentions that its system considers "future data" such as macroweather weather patterns, economic trends, and local demographics to anticipate demand and potential supply chain disruptions.

 Tesco also uses public weather data in its predictive models. This allows you to anticipate buying patterns, such as that for every 10°C increase in temperature, barbecue sales increase by up to 300%. In addition, Tesco receives local weather forecasts up to three times a day, connecting them with data on 18 million products and the type of customers in each store. This information is shared with your suppliers to adjust shipments and improve logistics efficiency.

• Using demographic data to decide the location of premises

For years, Starbucks has turned to predictive analytics to plan its expansion. The company uses geospatial intelligence platforms, developed with GIS technology, to combine multiple sources of information – including open demographic and socioeconomic data such as population density, income level, mobility patterns, public transport or the type of nearby businesses – along with its own sales history. Thanks to this integration, you can predict which locations have the greatest potential for success, avoiding competition between stores and ensuring that each new store is located in the most suitable environment.

Domino's Pizza also used similar models to analyse whether opening a new location in one London neighbourhood would be successful and how it would affect other nearby locations, considering buying patterns and local demographics.

This approach makes it possible to predict customer flows and maximize profitability through more informed location decisions.

• Socioeconomic data for pricing based on demographics

An interesting example can be found in SDG Group, an international consulting firm specialising in advanced analytics for retail. The company has developed solutions that allow prices and promotions to be adjusted taking into account the demographic and socioeconomic characteristics of each area – such as the consumer base, location or the size of the point of sale. Thanks to these models, it is possible to estimate the elasticity of demand and design dynamic pricing strategies adapted to the real context of each area, optimizing both profitability and the shopping experience.

The future of predictive commerce

The rise of predictive commerce has been fueled by the advancement of artificial intelligence and the availability of data, both open and private. From choosing the ideal place to open a store to efficiently managing inventory, public data combined with advanced analytics allows you to anticipate consumer behaviors and needs with increasing accuracy.

However, there are still important challenges to be faced: the heterogeneity of data sources, which in many cases lack common standards; the need for robust technologies and infrastructures that allow open information to be integrated with companies' internal systems; and, finally, the challenge of ensuring ethical and transparent use, which respects people's privacy and avoids the generation of bias in models.

Overcoming these challenges will be key for predictive commerce to unfold its full potential and become a strategic tool for companies of all sizes. On this path, open data will play a fundamental role as a driver of innovation, transparency and competitiveness in the trade of the future..

The Sistema Nacional de Publicidad de Subvenciones y Ayudas Públicas (SNPSAP) is a tool that contributes to the transparency, dissemination and re-use of data related to public subsidies and grants. This system centralises all the information on calls for applications and concessions of subsidies and public aid approved by the General State Administration, the autonomous communities and local entities.

Origins of the project

We have to go back to 2014 to find the beginning of this project. That year saw a reform of the 2003 General Law on Subsidies (Law 38/2003) with a huge impact in two significant respects:

• On the one hand, the National Subsidies Database, which had been created by the aforementioned Law in 2003, became available for full consultation by the managing bodies of subsidies and grants of all public administrations and those others legally authorised.

• Moreover, the National System of Publicity of Public Subsidies and Grants was created as a public website, with free and unrestricted access for all citizens. This complied with the requirements of publicity and transparency of all public subsidies and aid granted in Spain, especially calls for applications and awards, with identification of the beneficiaries. This space began operating on 1 January 2015, offering data from the state administration. The following year it was extended to regional and local administrations, thus covering the entire spectrum of the public sector.

Initial data formats and functionalities

From its origin, there was a serious support for the re-use of data for the society that originates it. Already initially the website allowed  downloading of data in different formats (CSV, XLSX and PDF) that allow their reuse, although limiting the size of the downloads to 10,000 records for performance and technical capacity reasons.

In addition, a alert subscription mechanismwas available from the outset. A citizen or company could - and can - register as many alerts as they need so that the system automatically notifies them when any call of interest to them is published, sending them a link to it. The tedious need to consult the dozens of official newspapers published in Spain on a daily basis to find out about calls for grants and subsidies was thus eliminated at a stroke.

A project in constant evolution to cope with growth

Regulatory developments in the field of aid and subsidies in the European Union, and Spain's accession to the United Nations Open Government Partnership initiative, shaped the growth of the website in successive years, increasing the offer of specific data views (State aid and minimis, large beneficiaries, political parties, etc.), which made it easier for citizens and data reusers to access them.

At the dawn of the pandemic, the system was already supporting 1.3 million visits per year, serving 3.3 million pages of grants and calls for proposals. This posed a challenge in terms of performance, as volumes never foreseen in the initial technical designs were achieved. A deep technological reform was needed to support the high demand for information and service level.

The reform was approached not only from a technological point of view, but also taking into account:

• The new features established in the Royal Decree 130/2019 regulating the National Grants Database.
• The socially demanded need to enable a API-REST interface for downloading information in reusable JSON format to overcome technical limitations.

Characteristics of the current platform

The new website was put into production at the end of November 2023, offering multiple views of calls for proposals (520,000 as of December 2024), grants (27,700,000), State aid awards (5.000,000), grants of minimis (3,190,000), strategic grant plans (1,341), very serious infringements (4), grants to parties (7,580), and grants to large beneficiaries (145,000).

All this information is accessible today via screen, downloadable in PDF, CSV, XSLX and API-REST interface formats in JSON and XML, being freely reusable by infomediary companies and citizens, with no restrictions other than those established by law.

Figure 1. Capture of the website of the National System for the Publicity of Public Subsidies and Grants (SNPSAP).

Since it went into production 11 months ago, the new system has received 7.5 million visits from citizens and businesses. And through the API-REST interface, thousands of downloads are made daily, making a powerful contribution to the dissemination of "raw" subsidy information for reuse by society for all kinds of analyses, studies, etc. In addition, an average of 35,000 e-mail alerts are issued daily to citizens and companies to inform them of new calls for subsidies.

Advantages of SNPSAP

The social dissemination and reuse of all this information eliminates asymmetries and frictions in the markets, and allows operators and citizens to work more efficiently and productively, resulting in higher levels of welfare for society.

The publication of open data allows citizens and organisations not only to know how public funds are distributed, but also to identify new opportunities. This system ensures that all individuals and organisations have equal access to information, regardless of their size or resources, contributing to a more equitable distribution of public support.

Promoting the data culture is a key objective at the national level that is also shared by the regional administrations. One of the ways to achieve this purpose is to award those solutions that have been developed with open datasets, an initiative that enhances their reuse and impact on society.

On this mission, the Junta de Castilla y León and the Basque Government have been organising open data competitions for years, a subject we talked about in our first episode of the datos.gob.es podcast that you can listen to here.

In this post, we take a look at the winning projects in the latest editions of the open data competitions in the Basque Country and Castilla y León.

Winners of the 8th Castile and Leon Open Data Competition

In the eighth edition of this annual competition, which usually opens at the end of summer, 35 entries were submitted, from which 8 winners were chosen in different categories.

Ideas category: participants had to describe an idea to create studies, services, websites or applications for mobile devices. A first prize of 1,500€ and a second prize of 500€ were awarded.

• First prize: Green Guardians of Castilla y León presented by Sergio José Ruiz Sainz. This is a proposal to develop a mobile application to guide visitors to the natural parks of Castilla y León. Users can access information (such as interactive maps with points of interest) as well as contribute useful data from their visit, which enriches the application.
• Second prize: ParkNature: intelligent parking management system in natural spaces presented by Víctor Manuel Gutiérrez Martín. It consists of an idea to create an application that optimises the experience of visitors to the natural areas of Castilla y León, by integrating real-time data on parking and connecting with nearby cultural and tourist events.

Products and Services Category: Awarded studies, services, websites or applications for mobile devices, which must be accessible to all citizens via the web through a URL. In this category, first, second and third prizes of €2,500, €1,500 and €500 respectively were awarded, as well as a specific prize of €1,500 for students.

• First prize: AquaCyL from Pablo Varela Vázquez. It is an application that provides information about the bathing areas in the autonomous community.
• Second prize: ConquistaCyL presented by Markel Juaristi Mendarozketa and Maite del Corte Sanz. It is an interactive game designed for tourism in Castilla y León and learning through a gamified process.
• Third prize: All the sport of Castilla y León presented by Laura Folgado Galache. It is an app that presents all the information of interest associated with a sport according to the province.
• Student prize: Otto Wunderlich en Segovia by Jorge Martín Arévalo. It is a photographic repository sorted according to type of monuments and location of Otto Wunderlich's photographs.

Didactic Resource Category: consisted of the creation of new and innovative open didactic resources to support classroom teaching. These resources were to be published under Creative Commons licences. A single first prize of €1,500 was awarded in this category.

• First prize: StartUp CyL: Business creation through Artificial Intelligence and Open Data presented by José María Pérez Ramos. It is a chatbot that uses the ChatGPT API to assist in setting up a business using open data.

Data Journalism category: awarded for published or updated (in a relevant way) journalistic pieces, both in written and audiovisual media, and offered a prize of €1,500.

• First prize: Codorniz, perdiz y paloma torcaz son las especies más cazadas en Burgos, presented by Sara Sendino Cantera, which analyses data on hunting in Burgos.

Winners of the 5th edition of the Open Data Euskadi Open Data Competition

As in previous editions, the Basque open data portal opened two prize categories: an ideas competition and an applications competition, each of which was divided into several categories. On this occasion, 41 applications were submitted for the ideas competition and 30 for the applications competition.

Idea competition: In this category, two prizes of €3,000 and €1,500 have been awarded in each category.

Health and Social Category

• First prize: Development of a Model for Predicting the Volume of Patients attending the Emergency Department of Osakidetza by Miren Bacete Martínez. It proposes the development of a predictive model using time series capable of anticipating both the volume of people attending the emergency department and the level of severity of cases.
• Second prize: Euskoeduca by Sandra García Arias. It is a proposed digital solution designed to provide personalised academic and career guidance to students, parents and guardians.

Category Environment and Sustainability

• First prize: Baratzapp by Leire Zubizarreta Barrenetxea. The idea consists of the development of a software that facilitates and assists in the planning of a vegetable garden by means of algorithms that seek to enhance the knowledge related to the self-consumption vegetable garden, while integrating, among others, climatological, environmental and plot information in a personalised way for the user.
• Second prize: Euskal Advice by Javier Carpintero Ordoñez. The aim of this proposal is to define a tourism recommender based on artificial intelligence.

General Category

• First prize: Lanbila by Hodei Gonçalves Barkaiztegi. It is a proposed app that uses generative AI and open data to match curriculum vitae with job offers in a semantic way.. It provides personalised recommendations, proactive employment and training alerts, and enables informed decisions through labour and territorial indicators.
• Second prize: Development of an LLM for the interactive consultation of Open Data of the Basque Government by Ibai Alberdi Martín. The proposal consists in the development of a Large Scale Language Model (LLM) similar to ChatGPT, specifically trained with open data, focused on providing a conversational and graphical interface that allows users to get accurate answers and dynamic visualisations.

Applications competition: this modality has selected one project in the web services category, awarded with €8,000, and two more in the General Category, which have received a first prize of €8,000 and a second prize of €5,000.

Category Web Services

• First prize: Bizidata: Plataforma de visualización del uso de bicicletas en Vitoria-Gasteiz by Igor Díaz de Guereñu de los Ríos. It is a platform that visualises, analyses and downloads data on bicycle use in Vitoria-Gasteiz, and explores how external factors, such as the weather and traffic, influence bicycle use.

General Category

• First prize: Garbiñe AI by Beatriz Arenal Redondo. It is an intelligent assistant that combines Artificial Intelligence (AI) with open data from Open Data Euskadi to promote the circular economy and improve recycling rates in the Basque Country.
• Second prize: Vitoria-Gasteiz Businessmap by Zaira Gil Ozaeta. It is an interactive visualisation tool based on open data, designed to improve strategic decisions in the field of entrepreneurship and economic activity in Vitoria-Gasteiz.

All these award-winning solutions reuse open datasets from the regional portal of Castilla y León or Euskadi, as the case may be. We encourage you to take a look at the proposals that may inspire you to participate in the next edition of these competitions. Follow us on social media so you don't miss out on this year's calls!

In today's digital age, data sharing and opendatahave emerged as key pillars for innovation, transparency and economic development. A number of companies and organisations around the world are adopting these approaches to foster open access to information and enhance data-driven decision making. Below, we explore some international and national examples of how these practices are being implemented.

Global success stories

One of the global leaders in data sharing is LinkedIn with its Data for Impactprogramme. This programme provides governments and organisations with access to aggregated and anonymised economic data, based on LinkedIn's Economic Graph, which represents global professional activity. It is important to clarify that the data may only be used for research and development purposes. Access must be requested via email, attaching a proposal for evaluation, and priority is given to proposals from governments and multilateral organisations. These data have been used by organisations such as the World Bank and the European Central Bank to inform key economic policies and decisions. LinkedIn's focus on privacy and data quality ensures that these collaborations benefit both organisations and citizens, promoting inclusive, green and digitally aligned economic growth.

On the other hand, the Registry of Open Data on AWS (RODA) is an Amazon Web Services (AWS) managed repository that hosts public datasets. The datasets are not provided directly by AWS, but are maintained by government organisations, researchers, companies and individuals. We can find, at the time of writing this post, more than 550 datasets published by different organisations, including some such as the Allen Institute for Artificial Intelligence (AI2) or NASAitself. This platform makes it easy for users to leverage AWS cloud computing services for analytics.

In the field of data journalism, FiveThirtyEight, owned by ABC News, has taken a radical transparency approach by publicly sharing the data and code behind its articles and visualisations. These are accessible via GitHub in easily reusable formats such as CSV. This practice not only allows for independent verification of their work, but also encourages the creation of new stories and analysis by other researchers and journalists. FiveThirtyEight has become a role model for how open data can improve the quality and credibility of journalism.

Success stories in Spain

Spain is not lagging behind in terms of data sharing and open data initiatives by private companies. Several Spanish companies are leading initiatives that promote data accessibility and transparency in different sectors. Let us look at some examples.

Idealista, one of the most important real estate portals in the country, has published an open data set that includes detailed information on more than 180,000 homes in Madrid, Barcelona and Valencia. This dataset provides the geographical coordinates and sales prices of each property, together with its internal characteristics and official information from the Spanish cadastre. This dataset is available for access through GitHub as an R package and has become a great tool for real estate market analysis, allowing researchers and practitioners to develop automatic valuation models and conduct detailed studies on market segmentation. It should be noted that Idealista also reuses public data from organisations such as the land registry or the INE to offer data services that support decisions in the real estate market, such as contracting mortgages, market studies, portfolio valuation, etc. For its part, BBVA, through its Foundation, offers access to an extensive statistical collection with databases that include tables, charts and dynamic graphs. These databases, which are free to download, cover topics such as productivity, competitiveness, human capital and inequality in Spain, among others. They also provide historical series on the Spanish economy, investments, cultural activities and public spending. These tools are designed to complement printed publications and provide an in-depth insight into the country's economic and social developments.

In addition, Esri Spain enables its Open Data Portal, which provides users with a wide variety of content that can be consulted, analysed and downloaded. This portal includes data managed by Esri Spain, together with a collection of other open data portals developed with Esritechnology. This significantly expands the possibilities for researchers, developers and practitioners looking to leverage geospatial data in their projects. Datasets can be found in the categories of health, science and technology or economics, among others.

In the area of public companies, Spain also has outstanding examples of commitment to open data. Renfe, the main railway operator, and Red Eléctrica Española (REE), the entity responsible for the operation of the electricity system ,have developed open data programmes that facilitate access to relevant information for citizens and for the development of applications and services that improve efficiency and sustainability. In the case of REE, it is worth highlighting the possibility of consuming the available data through  RESTAPIs, which facilitate the integration of applications on data sets that receive continuous updates on the state of the electricity markets.

Conclusion

Data sharing and open data represent a crucial evolution in the way organisations manage and exploit information. From international tech giants such as LinkedIn and AWS to national innovators such as Idealista and BBVA, they are providing open access to data in order to drive significant change in how decisions are made, policy development and the creation of new economic opportunities. In Spain, both private and public companies are showing a strong commitment to these practices, positioning the country as a leader in the adoption of open data and data sharing models that benefit society as a whole.

Content prepared by Juan Benavente, senior industrial engineer and expert in technologies linked to the data economy. The contents and points of view reflected in this publication are the sole responsibility of the author.

Digital transformation has become a fundamental pillar for the economic and social development of countries in the 21st century. In Spain, this process has become particularly relevant in recent years, driven by the need to adapt to an increasingly digitalised and competitive global environment. The COVID-19 pandemic acted as a catalyst, accelerating the adoption of digital technologies in all sectors of the economy and society.

However, digital transformation involves not only the incorporation of new technologies, but also a profound change in the way organisations operate and relate to their customers, employees and partners. In this context, Spain has made significant progress, positioning itself as one of the leading countries in Europe in several aspects of digitisation.

The following are some of the most prominent reports analysing this phenomenon and its implications.

State of the Digital Decade 2024 report

The State of the Digital Decade 2024 report examines the evolution of European policies aimed at achieving the agreed objectives and targets for successful digital transformation. It assesses the degree of compliance on the basis of various indicators, which fall into four groups: digital infrastructure, digital business transformation, digital skills and digital public services.

Figure 1. Taking stock of progress towards the Digital Decade goals set for 2030, “State of the Digital Decade 2024 Report”, European Commission.

In recent years, the European Union (EU) has significantly improved its performance by adopting regulatory measures - with 23 new legislative developments, including, among others, the Data Governance Regulation and the Data Regulation- to provide itself with a comprehensive governance framework: the Digital Decade Policy Agenda 2030.

The document includes an assessment of the strategic roadmaps of the various EU countries. In the case of Spain, two main strengths stand out:

• Progress in the use of artificial intelligence by companies (9.2% compared to 8.0% in Europe), where Spain's annual growth rate (9.3%) is four times higher than the EU (2.6%).
• The large number of citizens with basic digital skills (66.2%), compared to the European average (55.6%).

On the other hand, the main challenges to overcome are the adoption of cloud services ( 27.2% versus 38.9% in the EU) and the number of ICT specialists ( 4.4% versus 4.8% in Europe).

The following image shows the forecast evolution in Spain of the key indicators analysed for 2024, compared to the targets set by the EU for 2030.

Figure 2. Key performance indicators for Spain, “Report on the State of the Digital Decade 2024”, European Commission.

Spain is expected to reach 100% on virtually all indicators by 2030.  26.7 billion (1.8 % of GDP), without taking into account private investments. This roadmap demonstrates the commitment to achieving the goals and targets of the Digital Decade.

In addition to investment, to achieve the objective, the report recommends focusing efforts in three areas: the adoption of advanced technologies (AI, data analytics, cloud) by SMEs; the digitisation and promotion of the use of public services; and the attraction and retention of ICT specialists through the design of incentive schemes.

European Innovation Scoreboard 2024

The European Innovation Scoreboard carries out an annual benchmarking of research and innovation developments in a number of countries, not only in Europe. The report classifies regions into four innovation groups, ranging from the most innovative to the least innovative: Innovation Leaders, Strong Innovators, Moderate Innovators and Emerging Innovators.

Spain is leading the group of moderate innovators, with a performance of 89.9% of the EU average. This represents an improvement compared to previous years and exceeds the average of other countries in the same category, which is 84.8%. Our country is above the EU average in three indicators: digitisation, human capital and financing and support. On the other hand, the areas in which it needs to improve the most are employment in innovation, business investment and innovation in SMEs. All this is shown in the following graph:

Figure 3. Blocks that make up the synthetic index of innovation in Spain, European Innovation Scorecard 2024 (adapted from the COTEC Foundation).

Spain's Digital Society Report 2023

The Telefónica Foundation also periodically publishes a report  which analyses the main changes and trends that our country is experiencing as a result of the technological revolution.

The edition currently available is the 2023 edition. It highlights that "Spain continues to deepen its digital transformation process at a good pace and occupies a prominent position in this aspect among European countries", highlighting above all the area of connectivity. However, digital divides remain, mainly due to age.

Progress is also being made in the relationship between citizens and digital administrations: 79.7% of people aged 16-74 used websites or mobile applications of an administration in 2022. On the other hand, the Spanish business fabric is advancing in its digitalisation, incorporating digital tools, especially in the field of marketing. However, there is still room for improvement in aspects of big data analysis and the application of artificial intelligence, activities that are currently implemented, in general, only by large companies.

Artificial Intelligence and Data Talent Report

IndesIA, an association that promotes the use of artificial intelligence and Big Data in Spain, has carried out a quantitative and qualitative analysis of the data and artificial intelligence talent market in 2024 in our country.

According to the report, the data and artificial intelligence talent market represents almost 19% of the total number of ICT professionals in our country. In total, there are 145,000 professionals (+2.8% from 2023), of which only 32% are women. Even so, there is a gap between supply and demand, especially for natural language processing engineers. To address this situation, the report analyses six areas for improvement: workforce strategy and planning, talent identification, talent activation, engagement, training and development, and data-driven culture .

Other reports of interest

 The COTEC Foundation also regularly produces various reports on the subject. On its website we can find documents on the budget execution of R&D in the public sector, the social perception of innovation or the regional talent map.

For their part, the Orange Foundation in Spain and the consultancy firm Nae have produced a report to analyse digital evolution over the last 25 years, the same period that the Foundation has been operating in Spain. The report highlights that, between 2013 and 2018, the digital sector has contributed around €7.5 billion annually to the country's GDP.

In short, all of them highlight Spain's position among the European leaders in terms of digital transformation, but with the need to make progress in innovation. This requires not only boosting economic investment, but also promoting a cultural change that fosters creativity. A more open and collaborative mindset will allow companies, administrations and society in general to adapt quickly to technological changes and take advantage of the opportunities they bring to ensure a prosperous future for Spain.

Do you know of any other reports on the subject? Leave us a comment or write to us at dinamizacion@datos.gos.es.

Digital transformation has reached almost every aspect and sector of our lives, and the world of products and services is no exception. In this context, the Digital Product Passport (DPP) concept is emerging as a revolutionary tool to foster sustainability and the circular economy. Accompanied by initiatives such as CIRPASS (Circular Product Information System for Sustainability), the DPP promises to change the way we interact with products throughout their life cycle. In this article, we will explore what DPP is, its origins, applications, risks and how it can affect our daily lives and the protection of our personal data.

What is the Digital Product Passport (DPP)? Origin and importance

The Digital Product Passport is a digital collection of key information about a product, from manufacturing to recycling. This passport allows products to be tracked and managed more efficiently, improving transparency and facilitating sustainable practices. The information contained in a DPP may include details on the materials used, the manufacturing process, the supply chain, instructions for use and how to recycle the product at the end of its life.

The DPP has been developed in response to the growing need to promote the circular economy and reduce the environmental impact of products. The European Union (EU) has been a pioneer in promoting policies and regulations that support sustainability. Initiatives such as the EU's Circular Economy Action Plan have been instrumental in driving the DPP forward. The objectives of this plan are as follows:

• Greater Transparency: Consumers no longer have to guess about the origin of their products and how to dispose of them correctly. With a machine-readable DPP (e.g. QR code or NFC tag) attached to end products, consumers can make informed purchasing decisions and brands can eliminate greenwashing with confidence.
• Simplified Compliance: By creating an audit of events and transactions in a product's value chain, the DPP provides the brand and its suppliers with the necessary data to address compliance demands efficiently.
• Sustainable Production: By tracking and reporting the social and environmental impacts of a product from source to disposal, brands can make data-driven decisions to optimise sustainability in product development.
• Circular Economy: The DPP facilitates a circular economy by promoting eco-design and the responsible production of durable products that can be reused, remanufactured and disposed of correctly.

The following image summarises the main advantages of the digital passport at each stage of the digital product manufacturing process:

CIRPASS as a facilitator of DPP implementation

CIRPASS is a platform that supports the implementation of the DPP. This European initiative aims to standardise the collection and exchange of data on products, facilitating their traceability and management throughout their life cycle. CIRPASS plays a crucial role in creating an interoperable digital framework that connects manufacturers, consumers and recyclers.

DPP applications in various sectors

On 5 March 2024, CIRPASS, in collaboration with the European Commission, organised an event on the future development of the Digital Product Passport. The event brought together various stakeholders from different industries and organisations, who, with an eminently practical approach presented and discussed various aspects of the upcoming regulation and its requirements, possible solutions, examples of use cases, and the obstacles and opportunities for the affected industries and businesses.

The following are the applications of DPP in various sectors as explained at the event:

1. Textile industry: It allows consumers to know the origin of the garments, the materials used and the working conditions in the factories.
2. Electronics: Facilitates recycling and reuse of components, reducing electronic waste.
3. Automotive: It assists in tracking parts and materials, promoting the repair and recycling of vehicles.
4. Power supply: It provides information on food traceability, ensuring safety and sustainability in the supply chain.

 The impact of the DPP on citizens' lives

But what impact will the use of this kind of novel paradigm have on our daily lives? And how does this impact on us as end users of multiple products and services such as those mentioned above? We will focus on four base cases: informed consumers in any field, ease of product repair, trust and transparency, and efficient recycling.

The DPP provides consumers with access to detailed information about the products they buy, such as their origin, materials and production practices. This allows consumers to make more informed choices and opt for products that are sustainable and ethical. For example, a consumer can choose a garment made from organic materials and produced under fair labour conditions, thus promoting responsible and conscious consumption.

Similarly, one of the great benefits of the DPP is the inclusion of repair guides within the digital passport. This means that consumers can easily access detailed instructions on how to repair a product instead of discarding it when it breaks down. For example, if an appliance stops working, the DPP can provide a step-by-step repair manual, allowing the user to fix it himself or take it to a technician with the necessary information. This not only extends the lifetime of products, but also reduces e-waste and promotes sustainability.

Also, access to detailed and transparent product information through the DPP can increase consumers' trust in brands. Companies that provide a complete and accurate DPP demonstrate their commitment to transparency and accountability, which can enhance their reputation and build customer loyalty. In addition, consumers who have access to this information are better able to make responsible purchasing decisions, thus encouraging more ethical and sustainable consumption habits.

Finally, the DPP facilitates effective recycling by providing clear information on how to break down and reuse the materials in a product. For example, a citizen who wishes to recycle an electronic device can consult the DPP to find out which parts can be recycled and how to separate them properly. This improves the efficiency of the recycling process and ensures that more materials are recovered and reused instead of ending up in landfill, contributing to a circular economy.

Risks and challenges of the DPP

Similarly, as a novel technology and as part of the digital transformation that is taking place in the product sectors, the DPP also presents certain challenges, risks and challenges such as:

1. Data Protection: The collection and storage of large amounts of data can put consumers' privacy at risk if not properly managed.
2. Security: Digital data is vulnerable to cyber-attacks, which requires robust security measures.
3. Interoperability: Standardisation of data across different industries and countries can be complex, making it difficult to implement the DPP on a large scale.
4. Costs: Creating and maintaining digital passports can be costly, especially for small and medium-sized enterprises.

Data protection implications

The implementation of the DPP and systems such as CIRPASS implies careful management of personal data. It is essential that companies and digital platforms comply with data protection regulations, such as the EU's General Data Protection Regulation (GDPR). Organisations must ensure that the data collected is used in a transparent manner and with the explicit consent of consumers. In addition, advanced security measures must be implemented to protect the integrity and confidentiality of the data.

Relationship with European Data Spaces

The European Data Spaces are an EU initiative to create a single market for data, promoting innovation and the digital economy. The DPP and CIRPASS are aligned with this vision, as they encourage the exchange of information between different actors in the economy. Data interoperability is essential for the success of the European Data Spaces, and the DPP can contribute significantly to this goal by providing structured and accessible product data.

Conclusion

In conclusion, the Digital Product Passport and the CIRPASS initiative represent a significant step towards a more circular and sustainable economy. Through the collection and exchange of detailed product data, these systems can improve transparency, encourage responsible consumption practices and reduce environmental impact. However, their implementation requires overcoming challenges related to data protection, security and interoperability. As we move towards a more digitised future, the DPP and CIRPASS have the potential to transform the way we interact with products and contribute to a more sustainable world.

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

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

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

Understanding open data and its value chain

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

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

Figure 1: Open Data Value Chain

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

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

Indicators for impact assessment

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

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

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

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

Figure 2: Indicators for impact assessmen

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

Looking to the future

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

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

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

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

Artificial intelligence (AI) has revolutionised various aspects of society and our environment. With ever faster technological advances, AI is transforming the way daily tasks are performed in different sectors of the economy.   

As such, employment is one of the sectors where it is having the greatest impact. Among the main developments, this technology is introducing new professional profiles and modifying or transforming existing jobs. Against this backdrop, questions are being asked about the future of employment and how it will affect workers in the labour market.   

What are the key figures for AI in employment?  

The International Monetary Fund has recently pointed out: Artificial Intelligence will affect 40% of jobs worldwide, both replacing some and complementing and creating new ones.   

The irruption of AI in the world of work has made it easier for some tasks that previously required human intervention to be carried out more automatically. Moreover, as the same international organisation warns, compared to other automation processes experienced in past decades, the AI era is also transforming highly skilled jobs.  

The document also states that the impact of AI on the workplace will differ according to the country's level of development. It will be greater in the case of advanced economies, where up to 6 out of 10 jobs are expected to be conditioned by this technology. In the case of emerging economies, it will reach up to 40% and, in low-income countries, it will be reflected in 26% of jobs. For its part, the International Labour Organisation (ILO) also warns in its report ‘Generative AI and Jobs: A global analysis of potential effects on job quantity and quality’ that the effects of the arrival of AI in administrative positions will particularly affect women, due to the high rate of female employment in this labour sector.  

In the Spanish case, according to figures from last year, not only is the influence of AI on jobs observed, but also the difficulty of finding people with specialised training. According to the report on talent in artificial intelligence prepared by Indesia, last year 20% of job offers related to data and Artificial Intelligence were not filled due to a lack of professionals with specialisation. 

Future projections  

Although there are no reliable figures yet to see what the next few years will look like, some organisations, such as the OECD, say that we are still at an early stage in the development of AI in the labour market, but on the verge of a large-scale breakthrough. According to its ‘Employment Outlook 2023’ report, ‘business adoption of AI remains relatively low’, although it warns that ‘rapid advances, including in generative AI (e.g. ChatGPT), falling costs and the growing availability of AI-skilled workers suggest that OECD countries may be on the verge of an AI revolution’. It is worth noting that generative AI is one of the fields where open data is having a major impact. 

And what will happen in Spain? Perhaps it is still too early to point to very precise figures, but the report produced last year by Indesia already warned that Spanish industry will require more than 90,000 data and AI professionals by 2025. This same document also points out the challenges that Spanish companies will have to face, as globalisation and the intensification of remote work means that national companies are competing with international companies that also offer 100% remote employment, ‘with better salary conditions, more attractive and innovative projects and more challenging career plans’, says the report.   

What jobs is AI changing?  

Although one of the greatest fears of the arrival of this technology in the world of work is the destruction of jobs, the latest figures published by the International Labour Organisation (ILO) point to a much more promising scenario. Specifically, the ILO predicts that AI will complement jobs rather than destroy them.   

There is not much unanimity on which sectors will be most affected. In its report ‘The impact of AI on the workplace: Main findings from the OECD AI surveys of employers and workers', the OECD points out that manufacturing and finance are two of the areas most affected by the irruption of Artificial Intelligence.   

On the other hand, Randstad has recently published a report on the evolution of the last two years with a vision of the future until 2033. The document points out that the most affected sectors will be jobs linked to commerce, hospitality and transport. Among those jobs that will remain largely unaffected are agriculture, livestock and fishing, associative activities, extractive industries and construction. Finally, there is a third group, which includes employment sectors in which new profiles will be created. In this case, we find programming and consultancy companies, scientific and technical companies, telecommunications and the media and publications. 

Beyond software developers, the new jobs that artificial intelligence is bringing will include everything from natural language processing experts or AI Prompt engineers (experts in asking the questions needed to get generative AI applications to deliver a specific result) to algorithm auditors or even artists.  

Ultimately, while it is too early to say exactly which types of jobs are most affected, organisations point to one thing: the greater the likelihood of automation of job-related processes, the greater the impact of AI in transforming or modifying that job profile.   

The challenges of AI in the labour market  

One of the bodies that has done most research on the challenges and impacts of AI on employment is the ILO. At the level of needs, the ILO points to the need to design policies that support an orderly, just and consultative transition. To this end, it notes that workers' voice, training and adequate social protection will be key to managing the transition. ‘Otherwise, there is a risk that only a few countries and well-prepared market participants will benefit from the new technology,’ it warns.  

For its part, the OECD outlines a series of recommendations for governments to accommodate this new employment reality, including the need to: 

• Establish concrete policies to ensure the implementation of key principles for the reliable use of AI. Through the implementation of these mechanisms, the OECD believes that the benefits that AI can bring to the workplace are harnessed, while at the same time addressing potential risks to fundamental rights and workers' well-being.   

• Create new skills, while others will change or become obsolete. To this end, he points to training, which is needed ‘both for the low-skilled and older workers, but also for the high-skilled’. Therefore, ‘governments should encourage business to provide more training, integrate AI skills into education and support diversity in the AI workforce’.   

In summary, although the figures do not yet allow us to see the full picture, several international organisations do agree that the AI revolution is coming. They also point to the need to adapt to this new scenario through internal training in companies to be able to cope with the needs posed by the technology. Finally, in governmental matters, organisations such as the ILO point out that it is necessary to ensure that the transition in the technological revolution is fair and within the margins of reliable uses of Artificial Intelligence. 

The year 2023 was undoubtedly the year of artificial intelligence. This has brought data, and therefore open data, back to the forefront, as it is the raw material that fuels this technology, which is key to value creation in our increasingly digital economy.

Perhaps that is why 2023 has also left us a number of new developments in terms of the drive to open data, many of which could lead to the creation of significant economic and social value through re-use. One of these developments is the  obligation for public sector bodies to open in the first half of 2024 a number of high-value datasets, already specified in a regulation that was published in the last few days of 2022 in order to implement the provisions of open data directive (2019). Specifically, there are six high-value thematic categories: geospatial, earth observation and environmental, meteorology, statistics, companies and company ownership and mobility.

In order to comply with this obligation and with the rest of the obligations set out in Directive 2019/1024, in 2023, Spain has amended the Law 37/2007 on the re-use of public sector information has been amended in Spain in 2023. This amendment emphasises the duty to encourage the openness of high-value data published under an open data attribution licence (CC BY 4.0 or equivalent), in machine-readable format and accompanied by metadata describing the characteristics of the datasets.

The European Statistical System and the National Statistical Plan 2021-2024

Of the six thematic categories, number four, Statistics, is dedicated to statistical datasets, characterised by their broad definition and specification. It is based on the European Statistical System which ensures that European statistics produced in all Member States are reliable, following common criteria and definitions and treating data in an appropriate way, so that they are always comparable between EU countries. Specifically, the regulation defines 21 statistical datasets as high-value  (it actually includes 22, but one of them is redundant as it is broken down into three components: population, fertility and mortality).

The National Statistical Institute] is part of the European Statistical System and is in charge of the production of the harmonised national statistics that Eurostat then compiles and analyses to provide comparable figures, so that Community policies can be defined, implemented and analysed.

In Spain, the National Statistical Plan is the main instrument that organises the statistical activity of the General State Administration, the backbone of statistics for state purposes. The current plan was published at the end of 2020, covering the 2021-2024 period.

The National Statistical Plan 2021-2024 includes new strategic lines such as the use of new sources of information, including, for example, Big Data and massive databases. It also promotes new production models, such as experimental statistics, and incorporates a special focus on the inclusion of gender, disability, age and nationality perspectives, as well as improvements in real estate market information, especially on rentals.

High-value statistical datasets

In these strategic lines, the plan does not yet contain any mention of high-value datasets. However, as the plan is developed and implemented through specific annual programmes detailing the statistical operations to be carried out, their objectives, the bodies involved, and the budget appropriations statistical operations to be carried out, their objectives, the bodies involved and the budget appropriations needed to finance them, it is possible to get an idea of which of these statistical operations are aligned with the 21 categories of high value Ssatistical datasets regulation.

The following table shows the possible equivalences:

 En definitiva, parece que la mayor parte de las variables clave que el reglamento europeo ha previsto para los conjuntos estadísticos de alto valor están ya produciéndose de acuerdo con el plan estadístico nacional vigente. El plan estadístico nacional, que sucederá al actualmente vigente, comenzará en 2025 y a buen seguro se publicará a lo largo de este 2024. Este año veremos en Europa un intenso trabajo para cumplir con las obligaciones del reglamento, ya que, además, la Comisión Europea ha publicado recientemente el informe "Identification of data themes for the extensions of public sector High-Value Datasets" donde se incluyen siete nuevas categorías que se estudia considerar como datos de alto valor y que previsiblemente acabarán siendo incluidas en el reglamento.

All in all, it seems that most of the key variables that the European regulation has foreseen for high value statistical datasets are already being produced according to the existing national statistical plan. The national statistical plan, which will succeed the current one, will start in 2025 and will most likely be published in the course of 2024. This year will see intense work in Europe to comply with the obligations of the regulation, as the European Commission has also recently published the report "Identification of data themes for the extensions of public sector High-Value Datasets" which includes seven new categories that are being considered as high-value datasets and are expected to be included in the regulation and which will foreseeably end up being included in the regulation.