📈Skills analytics dashboards

This use case would allow to answer needs such as :

• As an organization (employer, training, community), I would like to be able to access aggregate skills data on a territory to establish precise statistics on the needs and prediction of skills in order to orient my recruitment policy, the financing of my training courses or the development of new training.

Functionality:

This use case enables stakeholders to build Skill-Analytics Dashboards with underlying skill-analysis services to provide a flexible tool to support L&D departments and other stakeholders. These Skill-analytics dashboard shall be able to aggregate and analyse skill-data coming from different sources within an organisation but also across different organisations and regions.

The skill analytics dashboard service will be offered within the European Skills Dataspace. Organisations with the respective access rights will be able to book the service and feed the service with skill-data that match their needs and of course the access policies. Ideally, the tool proactively suggests to the organisation skill-datasets that match their requirements and the policies and rules. The user can then load the data, trigger analyses and drill-down the information to the respective level of granularity. Potential views of the dashboard - which need to be designed with stakeholders within the SIMPL project - are:

• Skill-status analysis: Which skills and levels are available within an organisation by how many staff members in which department, region, company branch etc.

• Skill-gap analysis: L&D manager can feed into the tool known job profiles of the organisation required to be able to offer the services. The tool helps to find skill-gaps, viz. for which skills does the company lack sufficient staff members in a specific region.

• Skill-status comparison: Compare the skill-status of different but related departments / organisations / regions.

• General analyses: Show typical current and expected future job profiles of a selected industry sector

These Dashboards will be useful in particular for larger multi-national companies or for public institutions responsible for skill and personnel planning in distributed organisations (across regions or even countries). The tool can also be used by educational training providers to analyse how they should update their educational offers to respond to skill-needs of the market.

The implementation of such a service requires that (anonymized) skill-data and skill-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 skill-needs. Relevant dataset concern e. g. skill-profiles for certain job roles, (anonymized) skill-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 skill-taxonomies need to be mutualized before they can be aggregated and analyzed in the skill-analytics dashboard.

Building blocks mobilized:

In order to make such a use case a reality, different building blocks are needed. Prometheus-X is developing them:

• Contract: to ensure trust and compliance between organisations sharing data

• Identity: to ensure authentification of orgs and people sharing data

• Skills data interop: to allow the translation of profiles into competences, to ensure interoperability between the different repositories and data models concerning competences, to allow the tracing of the recognition and validation of a competence.

  • AI metadata enrichment: algorithms capable of extracting from raw data the activity that allowed the acquisition of competencies on the basis of common reference systems (ROME/ESCO/RECTEC...);

  • inter-repository translators: algorithms capable of semantically linking skills repositories to each other and to central repositories, thus making it possible to create a first ontology, making it possible to link sectorial skills (for example, to identify the skills of an individual using different repositories)

• distributed data visualisation: to allow results of matchings (jobs, trainings, ressources) to be shown wherever the user is and without

• anonymisation: to ensure all data used by the dashboards is not personal data

Prometheus-X gathers numerous organisations, data and AI providers that can make these use cases happen:

Dashboard users	AI Providers	Data providers
​​Grande Ecole du Numérique: national public body, addresses virtually all french citizens and french digital skills training orgs Dashboard to show skills needed in the digital sector	HeadAI: cognitive AI providing advanced analytics available via API, skills demand forecasting and simulations using a variety of sources	HEA: skills demand and offering in local, country, industry, or global level generated through analysis of 25 m job offers, 10m online content, 2 m books
SCHUELERKARRIERE: company, 4,5 million german pupils and a network of teacher and schools Dashboard to show teachers skills of pupils	MindMatcher: advanced analysis on the labour market, skills identification generated from semi-structured data sources (ex: CVs, skills needs in a territory), personalised analysis of skills and matching with career opportunities in a region, analysis of cross-functional skills in a geographical location or market	MM -4076 Semantic Entities for Ontologies building created through publicly available data (W3C, HRopen Standards, AFNOR,...) -Jobs & Skills related Semantic Entities (50k+ entities) -Semantic Relationships between jobs and skills in the Rome & Esco Graph (25k+ mappings)
Antares: company, 75% of schools in Germany Dashboard to show skills of pupils	Neobrain (NEO): Skills management solution offering skills repository, analysis and identification of missing skills, visualisation of jobs accessible to employees based on competencies and matching skills with internal job offerings that correspond to the employee profiles in an organisation	Neobrain: -15.700 job titles entries, and around 64.000 skills associated with their corresponding type
Univeristy of Koblenz (UoK): University, 10 000 students Dashboard to show skills of students, predict needs and tailor trainings	WEANLY (WEA) : AI specialising in defining skills through analysis of experiential learning activities	TTV: samples ranging from 15 million to 20 million+ job offers from public job boards in France, key information such as job titles, job descriptions, and publication date, location, salary, required qualifications and skills.
Institut Mines Telecom (IMT): University, 13000 students Dashboard to show skills of students, predict needs and tailor trainings	Inokufu: edtech company providing API access to learning object recommendation engine and competency matching.	Cozy Cloud: develops Cozy, an open-source and versatile personal data store to import, store and cross any kind of data from external providers, through connectors. The platform is used by more than 80K users
Games for Citizens (GC): Association of 30 universities Dashboard to show skills of students, predict needs and tailor trainings	Schuelerkarriere: AI enabling students to find suitable studies and careers based on their skills	IMC: formalised job profiles (set of skills and levels for a specific job), user-specific skill profiles, skill-taxonomies with levels, training offer descriptions (metadata of different types of offers), (anonymized) user profiles and course learning histories, organisational data Number of datasets depend on final deployment partners
France Digitale (FD): association, 1500 employers in digital domain Dashboard to analyze skills gaps for employers and help them recruit	LA Group (PALM): skills gap prediction, career trajectory estimations, matching and scoring employees with available positions within the company	GEN: -15000 training offer descriptions 103 -Family/Job/descriptions in digital domain -Skills analytics on job demand in the digital sector based on 15 to 20 million job offers
Sikt (SIK): public body, enabling the innovation Norwegian higher ed Dashboard to analyze skills and skills gaps for Norwegian citizens across universities	TrouveTaVoie: comprehensive analysis of job postings data and analysis of trends in the job market, identifying on-demand skills and tracking the evolution of sectors over time	ANT: More than 27M xAPI learning traces generated from learner activity
Le Cnam: high. Ed. organization dedicated to lifelong professional training, with more than 200 training centers in France Dashboard to predict skills needed		SK: -4.5 million orientation and skills profiles generated through user activity
BME: Largest technical university in Hungary, 10000 students Dashboard to show skills of students, predict needs and tailor trainings		WEANLY (Oktonine): -10 000 learner profiles years of study, domain, specialization, activities, list of to-be-acquired competencies) list of performed activities, list of validated competencies
Brest Business School (BBS): school, 500 learners Dashboard to show skills of students, predict needs and tailor trainings		EDUNAO: Edunao deliver managed Moodle environments to hundreds of organisations
		Inokufu: dataset of learning traces (xAPI) for 2k users in transition to high demand job
		UoK: student data from 10 000 students