Paula Valles Rodríguez

This article is the last in the series on Data Spaces. We will leave the previous articles here as a reference in case you missed them: In the first article we explore the fundamental pillars, roles, benefits, and challenges that these environments present for companies, thus serving as an introductory guide. In the second one, we analyze the technological aspects and key components that distinguish them from other technologies. On this third installment, we will focus on the organizations involved, examining the role of entities such as DSBA, GAIA-X, IDSA, and DSSC, as well as presenting the different connectors that exist and the constant evolution of these ecosystems. We will also analyze the prominent role of Telefónica in this context. Key organizations to consider There are different organizations whose objective is to drive business transformation in the data economy. The Data Space Business Alliance (DSBA) was created with this goal in mind. This alliance is an initiative that unites the main players in the data industry to shape the future of this technology. The members of the Data Spaces Business Alliance are, among others: Gaia-X, the Big Data Value Association (BDVA), the FIWARE, Foundation, and the International Data Space Association (IDSA). Let's take a look at what each of them does: GAIA-X: European initiative for the creation of an open, federated and interoperable data infrastructure. The stated goal of this GAIA is to ensure that Europe's companies and business models can be competitive and share data in a trusted environment. BDVA: Industry-driven research and innovation organization whose mission is to develop an innovation ecosystem that enables the digital transformation of the economy and society in Europe based on data and artificial intelligence. FIWARE: Non-profit organization that drives the definition and fosters the adoption of open standards (implemented through open-source technologies) that facilitate the development of smart solutions in domains such as smart cities, smart energy, agri-food and smart industry. Established in 2016, the foundation counts among its Platinum members Atos, Engineering, NEC, Red Hat, Telefónica, and Trigyn Technologies. IDSA: A non-profit organization dedicated to creating standards for data spaces. These standards are designed to ensure the secure exchange of data between participants within a specific governance framework, fostering trust and maintaining data sovereignty. Together they represent many key industry players, associations, research organizations, innovators, and policy makers worldwide. Figure 4. DSBA HUBspace One more organization is also worth mentioning: the Data Spaces Support Centre (DSSC). DSSC aims to accelerate the formation of sovereign European data spaces, while maintaining control of the companies and individuals that generate the data and respecting EU values. DSSC is funded by the European Commission as part of the Digital Europe Program and aimed at both the public sector and companies interested in creating sovereign data spaces. Its objective is to explore the needs, common requirements and best practices of existing data space initiatives, as well as to provide guidelines and support to accelerate the creation of new data spaces that enable data sharing while respecting the principles of data sovereignty, interoperability and trust. Apart from organizations, there is also a wide landscape of options at the technology level. Let's see what connectors are on the market. Connectors This component is basic to participate in any ecosystem that takes the form of a Data Space. Its main functions are to provide a point-to-point connection between two entities: the data provider (which sends the data) and the data consumer (which receives the data). It contains the entity's identifier and allows managing its participation in the ecosystem. It manages the transfer of data between the data provider and the data consumer, registering this transfer in the Clearing House. It allows the data provider to publish the metadata describing the information available, the terms of use and billing. This component is key to enabling secure and reliable participation in the data exchange ecosystem, managing the connection, metadata and data transfer between providers and consumers. The main features of the connector are: Trust: Trust is the basis of data spaces. This is achieved through evaluation and certification prior to access granted to the data space. · Data security and sovereignty: Secure data exchange, thus maintaining control over data through security protocols and policy implementation. Data decentralization: Decentralization is a crucial aspect of data spaces, with the principle of keeping data as close as possible to its origin and sharing it only when explicitly allowed. Interoperability: Standardized communication patterns between connectors enable the creation and interoperability of different connector implementations. There are multiple connectors, with varying degrees of development, maturity, and objectives. The following is a summary table updated to 2024. Image source: Data Connector Report May 2024. International Data Spaces Association As relevant connectors, we can highlight Eclipse Data Components, which is becoming a de facto standard for many ecosystems. Another consolidated project in the industry would be FIWARE, which started with a focus on Smart Cities, but has extended its field of action. Telefónica in Data Spaces We have played an important role at Telefónica in the promotion and development of Data Spaces. As a member of the Gaia-X Spain Association and a member of the board of directors of GAIA-X Spain, Telefónica has been committed to the promotion of Data Spaces. The company has organized events for GAIA-X Spain, where data spaces and the European GAIA-X initiative were addressed, with the aim of promoting the data economy and fostering networking among participants. The company has also been an active part of the FIWARE initiative, an open source platform that promotes the creation of standards for the development of Smart applications in different areas, such as Smart Cities, Smart Ports, Smart Logistics, and Smart Factories. In collaboration with other organizations such as Atos, Engineering and Orange, at Telefónica we have contributed to the development of FIWARE standards, which led to the creation of the FIWARE Foundation in 2016. This foundation supports FIWARE activities, protecting the brand and promoting the principles of openness, transparency, and meritocracy in the community. Telefónica has been a key player in the consolidation of FIWARE as a reference platform for the development of solutions and applications in sectors such as IoT. The company has worked to make FIWARE a neutral and open standard, without being tied to any specific vendor. The FIWARE Foundation continues to grow and expand its open-source ecosystem with more than 2,000 members, with the addition of relevant members such as Amazon Web Services and Red Hat. A future in constant evolution The creation and development of Data Spaces in Spain is constantly evolving, thanks to the collaboration between companies, institutions and organizations. Telefónica's initiative in this area is an example of how the industry can work together to create an open and secure data ecosystem. Data Spaces have the potential to transform the way businesses and organizations work with data, and to create new opportunities for innovation and growth. However, it also poses challenges and challenges that must be effectively addressed. In this sense, Telefónica's experience and commitment to creating Data Spaces can be a model for other companies and organizations to follow. Its work in the GAIA-X initiative and its support for the creation of standards through FIWARE are examples of how collaboration and innovation can lead to an improved technology marketplace. AUTHORS Santiago Morante AI Alliances and Solutions Development Manager Paula Valles Data Sales Consulting * * * Telefónica Tech IA & Data Smart Steps: understanding mobility May 23, 2024 Imagen: rawpixel.com / Freepik.

In a world awash with information, businesses need tools that make it easier to collaborate with each other and monetize their data, all while maintaining data security and privacy. Data Spaces present themselves as the solution. In this first part of our guide, we will explore the fundamental pillars of Data Spaces, the key roles involved and the benefits they can bring to your business. What is a Data Space? A Data Space is an ecosystem where value is generated to data through voluntary sharing, in an environment governed by sovereignty, trust, and security. In a Data Space it is possible to establish who can access certain data and under what conditions, facilitating in this way the implementation of diverse use cases that respond to the specific needs of each participant, without compromising data privacy. Operating as a controlled but open, heterogeneous, and decentralized environment, Data Spaces promote a free and equitable flow of information becoming the ideal scenario to monetize information in a secure manner. Data Spaces are set to become essential tools for the exchange of information between the so-called “participants” (companies, associations, and administrations, but also individual users depending on what each Data Space allows). These digital ecosystems in the cloud allow participants to collaborate through coordinated and regulated data management. The benefits of Data Spaces Data Spaces can offer a number of benefits in different sectors. Some of them are highlighted below: Economic benefit: Data Spaces have a business model associated with them for the participants (especially for those who contribute or consume data), so the first benefit is directly obtained from the profitability of the information. Fostering innovation: Data spaces catalyze the development of new products and services by facilitating access to a variety of data and insights that no single participant has on its own. Creation of strategic alliances: They enable collaboration between different organizations, which can lead to the creation of new business models and joint market strategies. Process optimization: Improving operational efficiency by enabling the analysis of data sets to identify inefficiencies and opportunities for improvement. Competitiveness improvement: The use of data to identify areas for improvement allows companies to adjust their strategies with respect to their competitors. Positioning: Due to data sharing and visibility, organizations can strengthen their position in the marketplace. Challenges There are currently several standards being developed simultaneously, such as the International Data Space Association (IDSA) standard, or the Gaia-X standard. These efforts are critical to shaping the future of data spaces, as the field is still characterized by a certain degree of fragmentation and immaturity. Different standards and frameworks often focus on specific domains or industries, which can lead to siloed approaches and hinder the creation of a unified, global data space ecosystem. Figure. Challenges of a Data Space. Building blocks Despite these challenges, the potential benefits of data spaces are undeniable. Data spaces can unlock new business models, foster innovation and drive economic growth by enabling the secure and standardized exchange of data. In this sense, any Data Space proposal must take into account the basic building blocks that are taken as a reference in different organizations (DSSC, OpenDEI, etc.). These blocks identify the business and technical components that must be developed in each Data Space to be considered useful, secure and productive. Figure 1. Overview of the building blocks of a data space. In this way, a series of main pillars to consider when we talk about data spaces are identified: Figure 2. Main pillars of a data space. Business: The essential and necessary concepts for the development of the business model of a data space should be provided. When talking about business models in the context of Data Spaces, we must make a clear distinction between: The business model of the Data Space as an infrastructure that can support multiple use cases. The business models of the individual Data Space participants involved in one or more Data Space use cases. Governance: Governance will need to adapt as these evolve. This includes two key elements: Organizational governance: guides the creation of governance authorities to ensure inclusive and transparent management. Data sharing governance: establishes common rules for efficient and secure data transactions. Compliance: Ensure compliance with the law and the establishment of a robust contractual framework. This includes two main components: Regulatory compliance: provides initiatives with an understanding of the legal environment and helps assess applicable regulatory requirements to ensure legal compliance and, typically, alignment with EU values (as drivers of GAIA-X). Contractual framework: establishes clear and enforceable rights and obligations for space participants and provides contractual remedies to regulate their data transactions. Interoperability: Data interoperability is a main pillar for the smooth and efficient integration of heterogeneous systems. In this sense, maximizing interoperability mechanisms with other sectoral and European data spaces is crucial to foster collaboration, efficiency and innovation. In this regard, it is important to consider: Adoption of recognized interoperability standards. Participation and alignment with European initiatives and projects: DSSC (Data Space Support Center), DBSA (Data Spaces Business Alliance)... Sovereignty and trust: Data sovereignty and trust are two fundamental aspects. Thus, the Data Space must have clear rules that establish who has access to what data and under what conditions, as well as establish limitations on its use. Value creation: The creation of value through data sharing, whether in the form of a new product, service or the generation of efficiencies, is another key pillar. As a general rule, data sharing occurs when the value of sharing is greater than the cost of making that data available. It is important to keep in mind that the value of the data is not only about its monetization, but other factors such as the generation of benefits or opportunities, as well as risk reduction (i.e. the generation of new partnerships) must be taken into account. Any new Data Space has to define the value creation models to get participants to join this space. Roles in a Data Space A Data Space is made up of different participants who play different roles depending on the scope of action they are focused on: Figure 3. Roles in the data space Resource providers and consumers: The participants that provide data and can interact with the data of other participants. Technology provider: The participant that provides components for the space to operate correctly, making it a secure and trusted environment. Intermediaries: The participant that encompasses third parties that provide the services necessary for publishing, searching resources and recording transactions. Operators of the space: Participants dedicated to the complete administration of the space. They are also in charge of certifying participants, overseeing the governance of the data space and establishing the roadmap for the development of new functionalities. AUTHORS Santiago Morante AI Alliances and Solutions Development Manager Paula Valles Data Sales Consulting * * * ■ MORE IN THIS SERIES IA & Data Decoding Data Spaces (part II): Technological Aspects September 16, 2024 Image: rawpixel.com / Freepik.

It is essential to prepare and define an appropriate data strategy to determine the ability of organizations to participate in the current revolution in a world where generative AI promises to revolutionize entire industries. Advances in generative AI have so far involved the emergence of intelligent systems with linguistic capabilities, image, video or audio generation capabilities and reasoning skills. All this has opened the door to the development of different use cases in different sectors such as education, industry, health... However, to maximize the usefulness of these systems, it is crucial to integrate them with updated and high-quality knowledge bases, so the successful implementation of this technology in any business will depend fundamentally on having a robust and efficiently managed data infrastructure. Having an effective data strategy is therefore key to making the most of generative AI as well as laying the foundations for its future development. It is important to consider the impact that innovation and adaptability have on business in order to advance in the definition of this strategy. On the one hand, an organization's ability to innovate in the way it collects, processes, and uses data will determine its success in leveraging generative AI. This includes exploring new data sources, adopting emerging technologies for data processing, and developing AI models that can adapt and learn autonomously. Adaptability on the part of businesses is also key to responding to changes in the data environment and market needs, ensuring that generative AI solutions remain relevant and effective. Considering all this, we can define several key aspects to take into account when defining our strategy: Data Structure and Organization: Data must be well structured and organized for easy access and processing by generative AI models. This includes the creation of consistent data repositories and the implementation of metadata schemas that enable efficient searching. Data Quality and Diversity: Ensuring that our data is diverse and of high quality is crucial. This may involve implementing data cleansing, validation, augmentation (Data Augmentation) and enrichment processes to thereby improve accuracy and reduce biases in generative models. Continuous data updating: Continuous data updating is key to having useful and relevant knowledge bases. The development of processes that facilitate synchronization with data sources in real time, as well as the establishment of feedback loops to integrate new data, are essential strategies. These processes will allow knowledge bases to be kept up to date with the latest information and trends, thus enabling us to obtain answers that are accurate and up to date. Data security and privacy: Data security and privacy is critical. In this sense, when defining our data strategy, it will be important to focus on three key areas: Identify and prioritize security risks to corporate data. Manage access to personal data. Be aware of evolving regulations It will therefore be essential to expand data protection measures, and to be quick to adjust strategies as new regulations emerge, such as the European Union's AI Law. This will enable us to protect the company's data assets and operate within an ethical and legal framework. Interoperability: As part of this strategy, it is important to promote interoperability between different systems and platforms within the same organization, in order to facilitate the exchange and combination of data. This may serve to enrich the available knowledge bases. Illustration 1. Data strategy pillars Challenges in the Data World In defining new data strategies, we are faced with the challenge of managing constantly growing volumes of data, while striving to ensure its quality. This task becomes even more complex in the field of Generative AI with the imperative need to update such data on a regular basis. As such, organizations will need to be able to balance innovation with data integrity and security to drive the development of use cases powered by this technology. In order to address these challenges, it is important to consider the following: It is essential to adopt a data strategy that prioritizes value, identifying where and what data is crucial to capture, in order to lead effectively in the era of Generative Artificial Intelligence. This involves adapting the data architecture so that we can embrace diverse use cases and ensure high data quality throughout the entire data lifecycle, from collection to final exploitation. It is important to maintain an agile and proactive posture to protect sensitive data, adapting quickly to new regulations that may appear. In this environment, strengthening data engineering talent becomes a priority, making it possible to use generative AI not only as an end but also as a tool to optimize data management. Implementing tracking and monitoring systems will ensure continuous improvement in data performance. This will be fundamental to any generative AI initiative. Adapting and preparing data for Generative AI is an investment in creating a promising future for our businesses. It is key at this point for businesses to understand that the suitability of data for Generative AI use is critical. As Peter F. Drucker rightly pointed out, "The best way to predict the future is to create it." Adapting and preparing data for Generative AI is just that: an investment in creating a promising future for our businesses. Ultimately, adopting a well-articulated data strategy will be the pillar on which success in the era of generative AI is built and implies a commitment to innovation and adaptability, indisputable pillars of success in the digital age. IA & Data Generative AI in the time series domain January 16, 2024

The emergence of generative Artificial Intelligence (GAI) has come to play a significant role in revolutionizing education today. Models such as LLaMA2, GPT4-Turbo and Dall-E 3 are redefining the possibilities of teaching and learning, taking educational potential to new levels and introducing into our vocabulary a new concept that we are sure to talk about a lot, EdTech. Generative AI has emerged as a valuable tool in education, providing innovative opportunities to improve the way students learn and educators teach. In this sense, the latter will be able to offer personalized learning experiences that adapt to the needs of each student in an individualized way, without having to worry about routine tasks that can be automated. Likewise, the emergence of AI-powered virtual assistants such as ChatGPT, Copilot, or WatsonX reinforce personalized educational experiences by offering students the possibility of receiving instant answers at any time, feedback or even the resolution of doubts, thus becoming a kind of tutor or personalized guide for students to improve their performance. New paths are opening up at all levels of education: Transforming schools and universities Creating impact in corporate training. Transforming and fostering autonomous learning. In schools and universities, in addition to offering individualized education through hyper-personalization and intelligent assistants, we will be able to address major problems that our education system is facing today such as dropouts and outdated curricula. AI-driven predictive analytics can be used to try to prevent or solve these problems, either by allowing us to identify early on students at risk of dropping out and thus re-evaluate their needs and increase the retention rate, or by creating personalized curricula with an individualized learning style and based on specific academic goals. At the corporate level, Generative AI is redefining learning roadmaps by adapting them to the role, skills and learning style of each employee. In addition, this technology gives us the ability to evaluate employee performance and adjust training modules on an ongoing basis, thus ensuring that we get the best possible performance out of them. On the other hand, in terms of autonomous learning, the recent release of GPT4-Turbo has marked a turning point, especially in the area of language learning. This version includes speech capabilities and has become the perfect tool for those who, for example, want to learn a new language or perfect another one in a self-taught way. ✅ The revolutionary thing about GPT4-Turbo is the opportunity it gives us to create customized assistants that are specifically tailored to the specific needs and objectives of each user, offering an interactive and personalized learning experience. Challenges in the implementation of Generative AI in the educational environment However, despite its benefits, generative AI faces major challenges. Among them are data privacy, the possible existence of biases in the models, accessibility, as well as the limitations and ethical issues related to the use of content generated by generative AI and the impact that all this can mean on the development of skills such as critical thinking in students. We will discuss some of these below: Data privacy: the increasing use of AI in education brings with it an increase in data collection and use. This raises concerns regarding invasion of student privacy that could be addressed by implementing data protection and privacy policies as well as other security techniques. Limitations of AI in education: AI lacks the ability for self-correction and nuanced understanding of exercises. This can lead to inaccurate assessments of students. To improve this point, feedback mechanisms could be incorporated into AI models (Reinforcement Learning from Human Feedback) and encourage educator collaboration in order to better understand student needs and the educational context. Existence of biases in AI models: AI models in education may use biased datasets, accentuating inequalities. Therefore, it is very important to encourage the use of models that use curated datasets, thus avoiding the reliance of AI on biased datasets that may further disadvantage minority groups. Accessibility and equity: the ability to implement AI-based learning will depend on the resources, funding and accessibility of each educational institution which could increase the digital divide between students from different backgrounds. This could be addressed with state grant programs aimed at making AI more accessible or developing simplified versions of some AI models that require fewer computational resources for those educational settings with fewer resources. Regulation and ethics of AI in education: it is important to establish clear and effective regulatory frameworks regarding the use of AI in education. These frameworks should address key issues such as privacy of student data, equity in access to the technology, and prevention of bias in algorithms. The involvement of educators, technology experts and regulators will be key to promoting the ethical and responsible use of AI. AI will be a complement in education and we must train both educators and students with the necessary skills to work with Generative AI technologies and know their limitations. All this will undoubtedly mean a paradigm shift from a one-size-fits-all educational model to individualized education. The emergence of generative AI in the educational sector should be understood as a step forward in our way of understanding and transmitting learning. As Socrates said, "Education is the lighting of a flame, not the filling of a vessel," and it is precisely this spark of curiosity and critical thinking that generative AI seeks to ignite in every classroom. By evolving and growing with this technology, we will open the doors to a future where education will not only be more personalized, but also more inspiring and able to adapt to the challenges of an ever-changing world. References: New models and developer products announced at DevDay AI of Things Creative AI in business: how to adapt ChatGPT (and similar) to my customer's needs August 14, 2024