2.1. Blueprint Introduction: Why a data space?

As the world is entering a complex transition, intertwining environmental challenges, energy transition, demographic expansion, digitalization and fast technological advances in AI and robotics, the education of new generations and the training and upskilling of the workforce at large scale needs to be improved.

Europe is currently experiencing an increase in the need for training, career changes and professional development. Figures from a recent McKinsey study [McKinsey Study] on the future of work in Europe show that more than 94 million people in Europe will need to learn new skills due to the adoption of automation in various sectors. By 2030, more than 21 million people in Europe could be looking for new career paths due to the decline of certain occupations, not to mention the impact of the global COVID-19 crisis. Moreover, one of the 4 pillars of the European Data Strategy [EU Data Strategy] is to upskill Europeans with digital skills that are lacking and that are essential to the XXIst century’s economy.

DS4Skills is convinced that a Skills and Education Data Space is a key framework to enable disruptive innovation for learning and preparing the EU population to face the upcoming world with the skills they need.

In order to offer personalized and lifelong learning services, matching individuals with suitable opportunities at the appropriate moment, and tailoring their learning experiences to their circumstances and proficiency levels, a foundation of interconnected data is essential. Similarly, to equip organizations with the means to pinpoint necessary skills, develop relevant training programs, anticipate hiring needs, recognize skill deficiencies, and enhance their workforce’s skill set, a network of interconnected data is indispensable.

This data can be personal data about people’s profiles (skills, hobbies, personality, experiences, preferences, etc) as well as non-personal data (job offers, training offers, skills ontologies, skills needed, etc).

This data is today scattered across a multitude of organisations, big/small, public/private, (training organisations, universities, schools, employment agencies, employers, institutions, EdTechs, HRtechs, job boards, training catalogues, etc). A combination of data sources and AI service providers to produce recommendations, analytics, dashboards, can help these stakeholders tackle those challenges. However, there is today no easy way to build such data and service interconnections while making sure all stakeholders and citizens maintain sovereignty on their data, with trust and with powerful ecosystem business models.

An infrastructure is required to facilitate the mutual sharing and accessibility of this data, catering to a diverse array of stakeholders in a secure, ethical, and decentralised manner. Devoid of such infrastructure, data will remain isolated in separate silos, hindering the promotion of ethical innovation. This distributed infrastructure, designed to enable data sharing while preserving privacy and sovereignty, is referred to as a “data space.”

A human-centric data space, where individuals have control over their data, is deemed pivotal for the future of training and skills development. To achieve this, it is imperative to furnish individuals and organizations with tools that empower them to manage their data and facilitate its trustworthy sharing and utilization. Furthermore, it is crucial to devise and embrace innovative governance models that encompass the participation and trust of multiple stakeholders, alongside ecosystem business models that ensure the equitable distribution of value among all parties involved.

To allow these data space use cases to be implemented, a new set of technical components must be established to enable such decentralized data sharing. These new services should rely on digital commons (commonly governed open-source code) referred to as “technical building blocks.” These building blocks will ensure that any organisation can easily connect to the data space and participate in data space use cases without becoming locked into any infrastructure provider. This aligns perfectly with the Digital Europe Programme [Digital Europe Programme], which is investing 7+ billion euros to enable data sovereignty for the European Union, its member states, organizations, and citizens through an innovative and decentralized data infrastructure. This vision is realized through the European common data spaces and the Simpl smart middleware platform.

However, the development of the building blocks alone is insufficient. It is crucial to ensure that they offer concrete value, fostering adoption and supplanting the current paradigm in which individuals or organizations lack control over their data, while the value remains confined and controlled by external gatekeepers. This requires the creation of potent and value-generating use cases that can showcase the transformative potential of these building blocks.

The needed technical, governance, legal and business models need to be designed and approved as they don’t exist today. The DS4Skills Blueprint provides first content, propositions and materials to build the Skills & Education Data Space.

DS4Skills, thanks to its set of expert partners and to a series of workshops with a wide community of interested stakeholders, has delivered a first version of the skills data space blueprint including:

  • Usage scenarios for the data space,
  • Business models for the data space and data space use cases,
  • Governance models for the data space and data space use cases,
  • Technical architecture and building blocks for the data space,
  • UX recommendations for the data space use cases.

This blueprint proposes concrete and illustrated tools to help build the Skills & Education Data Space, help organisations onboard it and create concrete data space use cases to bring value to citizens and organisations. Throughout the document a fictional skills data space use case, called EU-Dune, is used to illustrate each section and its recommendations. This use case is described under the “Usage Scenarios” chapter.

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