In the previous task, T3.1 – “Identifying Key Needs of the Stakeholders and Their Ongoing and Preferred Solutions on All Key Topics” of the DS4Skills project, a series of group interviews and co-creation sessions were conducted. These involved key stakeholders from 20 relevant initiatives to understand their design approaches and needs concerning the use of and access to skills data.
Through these interviews and co-creation sessions, various key skills data usage scenarios were identified. A human-centred approach to sharing skills data was also seen as a core mechanism for creating value in a skills data space. Reflections on this led to the articulation of five aggregate main usage scenarios. These serve as the foundation for informing the usage scenarios detailed in the blueprint.
According to our research, the value creation of skills data can be summarised into five usage scenarios: Prepare, Digitalise, Map, Match, and Forecast.
The strategic value of a skills data space is defined through mapping, matching, and forecasting skills. Each of these strategic use cases is summarised below:
- MAP: Mapping the available skills and those required by jobs and markets:
- Mapping competency-based education and career plans.
- Mapping individual and collective skills and capabilities.
- Applying interoperable skills semantics and taxonomies.
- Strategies for sourcing skills data from multiple platforms (e.g., online courses, professional certificates) and quality assurance measures for validating mapped skills.
- MATCH: Facilitating matches between organisations and individuals:
- Matching individuals to jobs based on personal skills profiles and job descriptions.
- Matching individual skills with education programmes and providers.
- Feedback loops for continuously improving the matching algorithms based on user reviews and data.
- FORECAST: Predicting the future of skills, as well as learning and training needs, while encouraging the market-driven development of new products for skill development:
- Trend analysis and prediction algorithms for anticipating future skills gaps, along monitoring and evaluation frameworks to assess the effectiveness of forecasting tools.
- Strategic data-driven decision-making for public and private organisations (e.g., what skills their workforce needs, what profiles they should employ, the employability of students, or the adaptability of educational programs to the labour market).
- Strategic data-driven decision-making for individuals (e.g., actionable information to help individuals build their career paths, personalised education and training plans, or employability predictions).
Each of these use cases need interconnections of various data sets and services, today siloed, to be the most efficient. A Skills & Education Data Space will boost such use cases.
4.2.1. In depth Map Usage scenario #
This usage scenario aims to create a comprehensive, interactive and evolving of the jobs, skills and competencies landscape. The base scenario is defined by a skills mapping platform that serves as a one-stop solution providing market insights for all stakeholders. Whether it is an individual trying to chart out a career path or a policy maker trying to understand regional job market, the platform offers the necessary tools to understand the complex landscape of skills and competencies in the evolving job market.
This skills mapping platform is a tool that allows for:
- Landscape analysis: Offers detailed insights into various skill domains, how they relate to one another, and their relevance over time.
- Skills profiling: Allows both individuals and organizations to create detailed skills profiles.
- Interoperable semantics and taxonomies: Incorporates various globally accepted skills taxonomies and enables translation between them for seamless integration and understanding.
4.2.1.1. Objectives #
The overall goals the ecosystem aims to achieve in this mapping use case are defined as follows:
As an individual:
- Understand and articulate skills, by gaining clarity on their own skills, strengths, and weaknesses.
- Share a comprehensive professional profile (including formal and informal skills, experiences, interests, preferences, etc.) with relevant stakeholders in a easy way.
- Articulate skill development, by identifying existing learning resources.
- Enable access to controls over how my personal data is collected and used that are easy to locate and I could adjust at my convenience.
As an employer:
- Streamline recruitment processes and finding the right candidates based on skills mapping.
- Employee development, by identifying available training options in the market.
- Facilitate strategic planning, by making informed decisions about workforce planning, project assignments, and organizational development based on skill availability.
As an educational organisation:
- Curriculum development, by designing and modifying courses and training programs in alignment with jobs and skills market demand.
- Student guidance, by assisting students in identifying their own skills and market demands.
- Industry collaboration, by engaging with industries to understand their in-demand skills.
4.2.1.2. Benefits #
These are some of the key advantages that the different stakeholders may obtain from participating in the mapping use case data ecosystem:
- Gather and share a comprehensive skills profile across all organisations in the ecosystem, without having to repeat information.
- Clear understanding of current individual skills standing.
- Identify areas where training is lacking or where there is emerging demand.
- Understand the evolving nature of skills.
- Gain insights to guide informed education and employment policy decisions.
4.2.1.3. Functionality #
In essence, the functionality in this mapping use case revolves around collecting and disseminating skills data that will enable alignment of education, individual development, and market demands, ultimately benefiting both job seekers and employers. These are some of the most characteristic functions for this use case:
Skill assessment and profiling
Individuals can assess their own skills, giving them a clear picture of their profiles.
Interoperable skill semantics and taxonomies application
Systems adopt universal or adaptable skill taxonomies to ensure consistency and compatibility across the ecosystem. This promotes the seamless sharing of data and reduces misunderstandings or misinterpretations due to differing terminologies.
Competency-based education and career plans mapping
Educational institutions leverage data to understand current market demands. This ensures that they can equip students with the skills needed in the job market.
Real-time skills and market trends analytics
The system could constantly update, reflecting real-time data. This offers insights into emerging skills, declining competencies, and changing market needs.
Job and education mapping
Mapping job openings and education options provides individuals with a thoughtful understanding of the market offering for them that could guide them through their upskilling and/or reskilling journey.
Feedback loop integration
Employers can provide feedback on the actual quality and relevance of skills of their hires or on course content relevance. This feedback can inform job platforms and educational institutions about the efficacy of their services and the areas of improvement.
4.2.1.4. Components #
Ensuring that skills mapping accurately represents the market reality requires a robust technological and governance framework. At the heart of any mapping solution, you will find a comprehensive repository that classifies all skills and jobs along with other information. Simultaneously, proper data governance stands as a cornerstone to ensure regulatory adherence for the stored skills data.
Together, these components (as defined in Chapter 7.4) form a cohesive system, enabling stakeholders to navigate the evolving job market landscape confidently and effectively.
- Skills Repository (data interoperability): A comprehensive repository of skills, categorised based on various taxonomies.
- Taxonomy Translator (data interoperability): Translates skills descriptions to facilitate seamless communication across different skills taxonomies and standards.
- Identity Management (data sovereignty and trust): Tools for individuals to manage their identities across partners.
- Consent Management Tool (data sovereignty and trust): Allows individuals and organisations to control who access their data.
- Data Anonymisation (data sovereignty and trust): Ensures data privacy and adherence to privacy regulations while still enabling meaningful analysis.
Other more generic data space components that may be useful, not only for this use case but also for others, are explained in Chapter 7.
4.2.1.5. Stakeholders and roles #
The primary entities involved in the ecosystem and the main different roles (as defined in Chapter 6.3) they play are as follows:
- Individuals (data providers and end users): Understand their skills and the available roles and can share their full profile with relevant stakeholders..
- Employers (data providers and end users): Map the skills of their workforce against industry benchmarks and identify the existing catalog of training. Contribute job offers/descriptions, skills ontologies, learning content, and organisational charts
- Educational Institutions (data providers and end users): Map industry needs to adapt curricula and guide students on potential career paths. Contribute their training catalogues and skills ontologies, and obtain precise student profiles.
- Employment agencies (service providers and end users): Get precise profiles of job seekers.
- Edtechs/AI Providers (data and service providers): Offer services, standards and taxonomies for skills to ensure that the mapping remains current and globally relevant.
- Infrastructure Providers: Offer services and building blocks to enable data sharing, including consent, contract, interoperability, and data visualisation.
4.2.1.6. Data Interconnections #
Data interconnections refer to the ways data flows, is shared, and is leveraged among various stakeholders. Given the nature of the mapping use case, it is crucial to ensure that these interconnections are efficient, timely, and secure. Here is a breakdown of some of the potential interconnections:
Individuals to Data Providers: Individuals use skill assessment tools to understand their skillsets. They feed personal data, undertake assessments, and receive feedback.
Individuals to Educational Institutions and Employers: Individuals share their skills data (like digital badges or certificates) with educational institutions for admissions or with employers during job applications.
Data Providers to Data Consumers: Platforms and tools share aggregated, anonymised data (while respecting privacy concerns) with employment agencies, career counselors, and research institutes.
Competency Assessments: Data from various competency assessment tools, which gauge an individual’s capabilities in specific areas.
Educational Institutions to and from Data Providers: Institutions use data to modify curriculums. They also provide data on the courses offered, skills taught, and student performance.
Employers to and from Data Providers: Employers provide job requirements and desired skill profiles. In return, they can access individual skills databases to understand skill trends in the market.
Government & Policy Makers to and from Data Consumers and Providers: Governments may use aggregated data to inform policies, and they might also provide data on labor market trends, industry growth, etc.
Data Providers to Data Intermediaries: Platforms and tools may need skills data integration services to connect with other platforms or to adapt to certain standards. This is especially relevant when considering the application of interoperable skills semantics and taxonomies.
Data Consumers to Data Intermediaries: Employment agencies, career counselors, and research institutes might use integration services to pull data from multiple sources or to push their insights to other platforms.
Data Provider Interconnections: Different skill assessment tools and platforms might share data standards, taxonomies, or integrate data to provide more comprehensive services to their users.
4.2.2. In depth Match Usage Scenario #
Various initiatives are working on skills data use cases to enable matching functionalities through the interconnection of different stakeholders. The base scenario for this use case is defined by a personal skills matching portal to easily access all these functionalities in an interconnected manner:
- Data from multiple sources can be shared with app providers to identify the best career moves and skill gaps.
- Career moves and training/job offers can be easily integrated into any interface within the ecosystem, providing relevant information to users where they are.
- Optimising matches between job seekers’ skills and market demands to seamlessly interconnect those.
- Ensuring training programs align with current and future industry needs.
These portals can be adapted for people in different situations:
- High school students deciding on higher education or career sectors.
- Job seekers looking for opportunities.
- Employees looking to upskill or reskill.
The portal can also be adapted to specific contexts:
- Employer skill ontologies, organisational charts, and learning content.
- A company is looking for an employee with a specific combination of competences and certifications.
- University/training provider catalogues.
- Regional demands for jobs and skills
4.2.2.1. Objectives #
The overall goals the ecosystem aims to achieve in this matching use case are defined as follows:
As an individual:
- Find the right career path, discover skill gaps, and receive career recommendations.
- Match my profile with relevant training and job offers.
- Connect all relevant data sources about my professional profile to fuel all interconnected tools and organisations.
- Enable career progression by finding career paths and roles according to their own interest.
As a educational organisation:
- Enable access to the detailed profile of a person’s skills and track skill acquisition to match with the right training at the right time.
- Student guidance, by assisting students in recognizing their strengths, areas for improvement, and guiding them towards suitable career paths.
As an employer:
- Enable access to a person’s detailed skill profile and track skill acquisition to match with the right job.
4.2.2.2. Benefits #
These are some of the advantages that the different stakeholders may obtain from participating in the matching data space use case:
- Job seekers could access to a broader range of job opportunities tailored to their unique skill sets.
- Job seekers could get Insights into training and courses that can bridge their skill gaps, enhancing employability.
- Employers would benefit from improved access to talent through connections with potential employees whose skills closely align with job requirements.
- Employers could also reduce the time and resources spent on the hiring process through more efficient matching.
- Training and education providers could explore new potential partnerships to collaborate with industries in creating specialised training programs.
- Insights into in-demand skills would also allow education providers for course content to be updated accordingly and curricula that are more aligned with job market demands.
4.2.2.3. Functionality #
The functionality in the matching use case is designed to actively bridge the gap between individual skills and market demands, ensuring that both job seekers and employers can find the most suitable matches for their needs. While similar functionality may have already been provided through traditional job platforms, those frequently rely on siloed and not updated data sources leading to potential inaccuracies. These are some of the most characteristic functions for this use case:
Job and training matching
Based on the skills profile (coming from various sources) of an individual, the system suggests suitable job opportunities and relevant training programs (coming from various sources) that align with their skills and career aspirations. The data space can suggest continuous learning pathways based on real time data about the person’s evolution and about the trainings and jobs available, adapting in real-time to the user’s progress, feedback, and changing market demands. This is a smoother and more adaptive approach as the data space allows for access to data from other organisations.
Skill gap identification
The system can determine where an individual’s skills fall short of the requirements for a particular job role or industry trend, or any general market gaps, based on large and real time data about the job markets. This analysis is invaluable for both job seekers and employers.
Career pathway suggestions
Beyond immediate job matches, the system can chart out potential career pathways for individuals based on their current skills.
Personalized training recommendations
Depending on the identified skill gaps, the system recommends tailored training programs or courses that can help individuals bridge those gaps. With the access to comprehensive data that a data space can offer, personalised and more effective learning pathways can be created for individuals.
4.2.2.4. Components #
These are some of the technological and data governance infrastructures (as defined in Chapter 7.4) that could enable the matching ecosystem to function:
- Catalogue (data value creation): Interface for users to find all interconnected services and be guided through the experience.
- Taxonomy Translator (data interoperability): Translates skills descriptions to facilitate seamless communication across different skills taxonomies and standards.
- Identity Management (data sovereignty and trust): Tools for individuals to manage their identities across partners.
- Consent Management Tool (data sovereignty and trust): Allows individuals and organizations to control who accesses their data.
- Data Anonymisation (data sovereignty and trust): Ensures data privacy and adherence to privacy regulations while still enabling meaningful analysis.
Other more generic data space components that may be useful, not only for this use case but also for others, are explained in Chapter 7.
4.2.2.5. Stakeholders and Roles #
The primary entities involved in the ecosystem and the main different roles (as defined in Chapter 6.3) they play are as follows:
- Individuals (end users): Get innovative employment and orientation services.
- Universities/Training Providers (data and service providers): Match their offers with relevant profiles.
- Employers (data providers and end users): Match their offers with relevant profiles and get precise employee profiles.
- Educational Institutions (service providers and end users): Provide students with innovative employment and orientation services.
- Governments and Policy Makers (data providers and end users): Frame training policies based on regional skills gap analysis and promote courses and training that fill the identified skills gaps.
- Employment Agencies (service providers and end users): Offer innovative employment and orientation services.
- Edtechs/AI Providers (data and service providers): Offer better-personalised services due to better data access.
- Infrastructure Providers: Offer services and building blocks to enable data matching, including consent, contract, interoperability, data visualisation, and decentralised processing.
- Orchestrator: Provides the ecosystem portal and coordinates governance, use cases, and business model discussions.
4.2.2.6. Data Interconnections #
The matching ecosystem is fundamentally about connecting individuals with appropriate opportunities based on their skills and aspirations. The data interconnections in this ecosystem enable this alignment. creating a responsive, dynamic, and efficient matching system. The smooth and secure integration of these diverse data sources and flows is fundamental for the success of this ecosystem. Here’s a breakdown of some of the most representative data interconnections in the matching use case:
Individual Profiles to Employers, and Training and Education Providers: Comprehensive datasets detailing individuals’ skills, education, experiences, and aspirations for skills and career matching. This includes their learning histories, credentials, soft skills, and more from all relevant sources.
Employer Databases to Individuals and AI/ML models: Detailed job descriptions, requirements, and future needs. This would include not just hard skill requirements but also soft skills, cultural fit, and more.
Training and Education Providers to Individuals: Curriculums, course outcomes, and competency developments that can be matched against industry requirements and career aspirations.
Skills Taxonomies and Ontologies to Employers and AI/ML models: Structured systems for classifying skills in a standardised manner, allowing for a consistent and standardised matching process across various sectors and geographies.
Career Pathway Repositories to Individuals: Data about typical career progressions, which can help guide individuals on what skills they might need to develop next.
Feedback and Reviews to Employers and Individuals: Data from individuals about job fits, satisfaction levels, and training efficacy. Similarly, employer feedback on candidates and hires for job suitability and satisfaction feedback.
Individuals to Mentorship and Peer Networks: Connecting to mentors or peers who can offer guidance, thus facilitating a more organic matching process based on shared experiences.
Historical Data to AI/ML Models: Past matching successes and failures, which can be used to refine the algorithms and processes improving future talent matching.
4.2.3. In depth Forecast Usage Scenario #
Different initiatives are working on skills data space use cases to enable forecasting functionalities through the interconnection of different stakeholders. The base scenario in this case is defined by a skills analytics portal to easily access all these functionalities in an interconnected manner:
- Assess for how long a particular skill will remain relevant.
- Knowing the future skills required by an organisation to be able to offer the catalogue of services.
- Compare the skills status of different but related departments, organisations or regions in order to predict future tendencies.
- Show typical current and expected future skills of a selected industry sector through general market analysis.
In essence, these skills analytics portal aims to offer a flexible and comprehensive analytics service for organisations to make informed decisions about skills needs and gaps, aiding in precise training, development, and recruitment planning.
These portals would be particularly useful for larger multi-national companies or for public institutions responsible for skills and personnel planning in distributed organisations (across regions or even countries) but will also be very valuable for other stakeholders such as SMEs or universities.
4.2.3.1. Objectives #
The overall goals the ecosystem aims to achieve in this forecasting use case are defined as follows:
As an individual:
- Access summarised future skills needs data forecast to make informed decisions about my own career development.
As a public institution:
- Enable the use of local and global skills trends data for policy formulation related to education, employment, and skills development.
As an employer:
- Access aggregate skills data on a territory to establish precise statistics on the needs and prediction of skills to orient my recruitment policy.
- Find skills gaps for which my organisation lacks sufficient staff members in a specific region.
- Ensure the company is investing on skills and skills groups that will also be valid in the future, short-, mid- and long time frame.
As an educational training provider:
- Tailor my curricula based on market needs predictions, making my educational programs more relevant and attractive.
4.2.3.2. Benefits #
These are the advantages that the different stakeholders may obtain from participating in the forecasting use case data ecosystem:
- Enables organisations to anticipate future skills needs and strategically plan their human resource development through data-driven decision making.
- Forecast industry growth and job market shifts based on evolving skill trends and cater to them proactively.
- Tracks performance and effectiveness of training programs over time.
- Provides a flexible skills market analysis tool for various stakeholders.
- The data could also be used to predict skills gaps in underprivileged areas or demographics, serving as a basis for targeted educational programs.
- A workforce that has the right skills is essential for economic growth.
4.2.3.3. Functionality #
The interconnected nature of a skills data space amplifies the benefits by leveraging the collective strengths of various stakeholders in the skills ecosystem. The sum becomes greater than its parts, leading to more responsive, adaptive, and efficient skills services. The rich, interconnected data within a skills data space can be a breeding ground for innovative services. New tools, platforms, or solutions that address particular gaps or pain points can be developed, leading to services that traditional EdTech or HR tech platforms haven’t even conceptualised.
The forecasting use case functionality is designed to provide stakeholders with advanced, actionable insights into the future of the skills landscape. By harnessing this data, organisations can better prepare for and shape the future, ensuring that education and training align closely with market needs. These are some of the most characteristic functions for this use case.
Skills analytics dashboards
A platform where stakeholders can analyse aggregated skills data from various sources, making it easier to discern trends, gaps, and patterns. A skills data space can provide a comprehensive view of the skills landscape richer than any other single solution can provide in isolation.
Proactive data suggestions
The tool can actively recommend datasets to organisations that align with their requirements, adhering to policies and access rights.
Skills status analysis
Evaluate the particular skills future needs within an organisation, breaking it down by departments, regions, and other relevant categories.
Skills gap analysis
Allows organisations to determine where there’s a shortage risk of necessary skills, especially in relation to specific job roles or regions. In the rapidly evolving job market, some skills might become obsolete while others rise in prominence. A skills data space can track the lifecycle of skills, identifying which ones are waxing or waning in importance.
Trend analysis for skills
Tools to track the evolution of skill demands over time, enabling organisations to anticipate and adapt to future requirements. A unified data space, by its nature, facilitates real-time data gathering, allowing trends to be spotted as they emerge.
Skills status comparison
A feature to contrast the skills available in one department, organisation, or region with another, facilitating benchmarking and strategic planning.
General job profile analysis
A broad overview of current and expected skills within a selected industry, giving stakeholders a clearer understanding of the market. Traditional methods might focus solely on hard skills or qualifications. A unified data space can combine these with soft skills, work experience, peer reviews, and other holistic metrics, offering a more rounded trend analysis.
Tailored recommendations for educational providers
Based on the analysed data, the system can offer suggestions to educational and training institutions about how they might adapt their programs to better meet future skills needs.
4.2.3.4. Components #
The implementation of such a service requires that (anonymised) skills data and skills models are shared on a broad basis by different stakeholders in different sectors and domains so that models can be trained to analyse current and predict future skills needs. Relevant dataset concern e. g. skills profiles for certain job roles, skills profiles of individual users, organisational structures and groups, information about domains and sectors etc. For example, data from different sources, in different formats, following different skills taxonomies need to be mutualized before they can be aggregated and analysed in the skills analytics dashboard.
These are some of the specific technological and data governance infrastructures (as defined in Chapter 7.4) that could enable the matching ecosystem to function:
- Data collection engine (data interoperability): Automates the process of data ingestion, reducing manual errors and ensuring timeliness.
- Data warehouse (data value creation): Provides a single point of truth for all analytics, ensuring data consistency and integrity.
- Data processing engine (data value creation): Preprocesses data to be ready for analysis, thereby accelerating the analytics process.
- Analytics engine (data value creation): Performs real-time analysis, enabling faster decision-making and responsiveness to emerging skills trends.
- Data sharing (data interoperability): Facilitates easy sharing of insights and data with other stakeholders, promoting cooperation and mutual benefit.
- Data quality management (data sovereignty and trust): Ensures that the data used for analytics is accurate and reliable, leading to better insights and decisions and provides a mechanism for accountability and traceability.
- Data anonymisation (data sovereignty and trust): Allows for the use of sensitive data in analytics without compromising privacy, thereby broadening the range of usable data.
- Secure data sharing (data sovereignty and trust): Protects sensitive data, thereby fostering trust among users and stakeholders.
Other more generic data space components that may be useful, not only for this use case but also for others, are explained in Chapter 7.
4.2.3.5. Stakeholders and Roles #
The primary entities involved in the ecosystem and the main different roles (as defined in Chapter 3) they play are as follows:
- Organisational and employers (end users and data providers): they are primary data consumers that analyse the skills inventory, identify gaps, and strategize for future needs, they contribute data about their job positions.
- Learning and development departments (end users and data providers): they use the dashboard for targeted training and contribute data on training efficacy and skill development.
- Educational and training providers (data providers and end users): contribute data on training outcomes and skill development, while using the dashboard to tailor their educational programs.
- Public institutions (data providers and end users): use data for policy formulation and governance, while also contributing public-sector data to enrich the dataset.
- Individuals and employees (data providers and end users): their skills data enriches the ecosystem, and they also benefit from better job and training matching.
- Observatories (data providers): collect, store, and provide data from multiple sources, including public and private sectors.
- Infrastructure Providers: Offer services and building blocks to enable data sharing, including consent, contract, interoperability, data visualisation, and decentralised processing.
- Orchestrator: Provides the ecosystem portal and coordinates governance, use cases, and business model discussions.
4.2.3.6. Data Interconnections #
Data interconnections form the backbone of the forecast ecosystem, facilitating the seamless transfer, aggregation, and analysis of data from various sources. These interconnections enable a rich, multi-dimensional view of the skills landscape, driving precise and actionable forecasts for stakeholders in the ecosystem. The smooth interaction and flows of these diverse data sources is fundamental for the success and accuracy of the forecasting model. Here’s a breakdown of some of the potential data interconnections:
Individuals, Employers, Educational institutions and Training Organisations to Educational and Training Skills Data Repositories: Centralised or distributed databases where individual or aggregated skills data are stored, coming from various entities. This data can then be utilised alongside other datasets for improved forecasting.
Industry and Business Entities to other Peer Organisations: Data from industry bodies interconnects and can be cross-referenced to provide context on industry trends and insights and validate the forecasting prediction models.
Education Institutions to Educational Data Streams: Input from higher education catalogues, vocational schools, and universities interconnects with open access repositories and recommendation engines, helping individuals align their education with market needs.
Job Boards and Employment Agencies to Job Market Analytics: Real-time data from job boards and employment agencies offers insights into current job requirements and emerging trends.
Industry Trends and Insights: Real-time job data from job boards and employment agencies interconnects with industry trends and insights, allowing for the analysis of job requirements and trends.
Open Access Repositories to Educational Data Streams: Open databases interconnect with educational data streams and industry trends, enriching the depth of forecasting analysis and ensuring a well-rounded understanding of the educational and job landscape.
Recommendation Engines to Individuals: Algorithms that suggest courses, jobs, or opportunities based on user profiles and behavior can also play a role in forecasting by predicting the skills and careers that individuals are likely to pursue in the future.
4.2.4. A Real Example of a Data Ecosystem #
In this section, we bridge the gap between theoretical strategic usage scenarios and practical implementation by exploring a real-world data ecosystem use case. Through the lens of this compelling matching example, we will illustrate how the concepts and models earlier introduced are effectively put into action, how different stakeholders interact and how it could be further expanded in the future.
This real-life scenario provides a tangible demonstration of how data spaces can be harnessed strategically around specific ecosystems to address skills matching challenges. By dissecting this concrete case, we aim to showcase how organisations can translate theory into impactful outcomes, offering valuable insights and practical guidance for those seeking to navigate the complex landscape of skills and talent management in today’s digital era.
4.2.4.1. The case of VisionsGalaxy #
VisionsGalaxy [VisionsGalaxy] is a data intermediary portal where various stakeholders interact in a French “match” data space use case:
Edtechs and Analytics Companies (data and service providers) that allow people to define their skills, career paths and be matched with opportunities
- Some examples are: OrienToi [OrienToi], Jobready [Jobready], NEOBRIDGE [NEOBRIDGE], TrouveTaVoie [TrouveTaVoie], HeadAI [HeadAI], and Inokufu [Inokufu].
Job boards (data and service providers) that provide the ecosystem job offers to match people with
- Some examples are: LinkedIn [Linkedin], Indeed [Indeed], and HelloWork [HelloWork].
Training catalogs (service providers) that provide the ecosystem training offers to match people with
- Some examples are: CPF [CPF], ONISEP [ONISEP], and Oriane FormPro [Oriane FormPro].
Employers (data providers and end users) that receive full profiles of people matched with job offers
- For example: Orange [Orange].
Training organisations (service providers and end users) that receive full profiles of people matched with training offers
- Some examples are: Grande École du Numérique [Grande École du Numérique], OpenClassrooms [OpenClassrooms], and the Cergy Pontoise University [Cergy Pontoise University].
Employment agencies (data providers and end users) that use the system with unemployed people to provide them the best opportunities and counsel them
- Some examples are: ARML [ARML], CIDJ [CIDJ], TINGARI [TINGARI], and Pole Emploi [Pole Emploi].
Data intermediaries that allow participants to share data in a trustworthy way
- For example: VisionsTrust [VisionsTrust].
The following graph illustrates the interactions among the various stakeholders in this ecosystem:
