Labour Market & Skills Information Systems for VET Policies

On 26th June EfVET attended to CEDEFOP seminar focused on Big Data and Artificial Intelligence, which was organise in cooperation with the Bulgarian Presidency of the Council of the EU. 

In the focus were the Big Data and AI for collecting, analyzing and disseminating a wide range of data on skill supply, forecast skill needs and skill mismatches to various actors, while allowing for designing personalized training courses and up-skilling for employees and job seekers.

In his opening speech, Yasen Gyurov, First Secretary for Education and Training, assured the participants that education and VET, and more specifically VET mobility, are priority not only during the Bulgarian Presidency, but will continue to be on the top of the Agenda of the following Austrian Presidency of the Council of the EU with the aim of creating the European Education Area.

Alena Zukersteinova and Konstantinos Pouliakas from CEDEFOP set the scene for discussing systems for collecting and analyzing vacancies, as well as the importance of using Big Data in the mix of methods for labour market and changing skill needs analysis and forecasting.

Jasper van Loo (CEDEFOP) shared insights from a pilot project in Member States where online published vacancies are analysed to get data about skills. The first findings show that in Finland, Sweden, Estonia, the Netherlands and Bulgaria almost 100% of the vacant posts are published online. Furthermore, the online vacancies published in Italy, Luxembourg and Denmark include a lot of information about hard skills while the demand for soft skills is trending in Italy, The Netherlands, Latvia, Lithuania, Sweden, Ireland, Denmark, Bulgaria, UK, Romania and Portugal. Less or insignificant information about needed soft skills is found in the vacancies published in Greece, Cyprus, Hungary and Malta. Another interesting finding is the signal for potential hybrid jobs, such as data analytics, web development, mobile development and digital marketing and marketing automation. Mr van Loo’s message to the audience was to use Big data and AI for LM analysis, but not to forget the human (researcher) in the centre of this process.

David Barnes, IBM Director for Global Workforce Policy, presented a model of career development and personalised training in a company with more than 360 000 employees worldwide. It is a values- based approach to align the existing skills of the employees with the new job requirements. In other words, making the employees adaptable and creating a culture of learning. The new hiring at IBM has focus on soft skills as they are much more durable and a better indicator for long term career of the employee in the company. There are already “new collar jobs”, or hybrid jobs, such as software engineer, designer and apps developer, related to cloud and cognitive technologies. IBM encourages flexibility of working hours and requires each employee to dedicate 40 hours per year for training and upskilling. The programmes are personalised and in an e-learning environment. “Digital will bring new jobs and will change all jobs. Protect the worker, not the job” concluded Mr. Barnes.

Erik Kiewais from VDAB, Flanders informed about three use cases Big Data for predicting how long a job seeker would be unemployed, by comparing the personal profile, interests, activity on one hand and the published vacancies. So far 30 000 job seekers in Flanders are profiled in this way and although it is just an experiment, the Jobnet tool is planned to be widely used as of September.

Claudia Plaimauer from 3s Unternehmensberatung, Austria, presented the company expertise in taxonomy and validation of skill terms. Their latest work led to adding 635 terms, of which 366 are non-preferred terms(NPT), 172 are hidden search terms and only 97 are preferred terms (PT). The later are linked to skills in economy or law; transversal specialist skills and IT skills.

Fabio Manca from OECD Skills and Employability Division presented their work and findings with regards to ranking of occupations to identify those in high demand and low supply as well as the lessons learned from the Getting Skills Right initiative.

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