NRK shows success stories from Norway’s new job matching platform

JANZZ.technology is the provider of the technology behind the semantic search and matching engine of the Norwegian Labour and Welfare Administration’s (NAV) new job matching platform.

NAV’s digital job search platform, Arbeidsplassen.no, is now launched and endorsed by both job seekers and employers. NAV hopes it will now be easier for job seekers to find a new position.

Yusuf became a call center agent within a week. For him, taking a year off after high school also meant a gap in the CV, which turned out to be problematic when he was applying for a job again. Through NAV, he started work training, which would actively help open the door to the labor market. Arbeidsplassen.no was the solution for Yusuf. He got an interview and started as a call center agent the following week. For the recruitment company “Maskineriet”, NAV’s website has made it easier to find new employees. The recruiters search through NAV’s CV-database, find candidates and make further relations. The candidates may then be employed in a permanent position.

The managing director of Virke, the Enterprise Federation of Norway, praises the job search platform. Arbeidsplassen.no is seen as very important, as it connects companies’ needs and good candidates more efficiently. The fact that NAV is investing in digitization simplifies the recruitment process for companies.

On the job seeker side, the CV service has been significantly improved, thus candidates can more conveniently find the right job by using NAV’s platform. Candidates can now define and target the job search profile, and simultaneously apply to a wider range of available positions. The employers demonstrate positive feedback on both the candidate matching process and the platform Arbeidsplassen.no – which ultimately is important to NAV.

Learn more on NRK.no: https://tv.nrk.no/serie/distriktsnyheter-oestfold/201907/DKOS99070519/avspiller

 

 

Middle-skilled workers to be hit hardest by digitization

While low-skilled workers are going to suffer the most from the consequences of digital transformation long-term (with some exceptions), opportunities for middle-skilled jobs are shrinking the most, according to recent observations in OECD countries.

We used to talk about digitalization and automatization only as processes that will change our working environment in the future, for example through the replacement of humans by robots. Meanwhile, the situation has changed: many of us already feel the effects of digitization and automation. These effects are likely to be amplified even further in future.

Due to the energy revolution that digitalization induced, many companies in the energy business, blindsided by the speed of this revolution, are faced with overcapacity. In Switzerland, General Electric (GE) has just announced a major workforce reduction, which causes 450 employees in Baden and Birr to lose their job. In order to compete with international online providers Migros, one of Switzerland’s biggest retail companies, too, undergoes such transformation. In June, the Genossenschaft Migros Ostschweiz released the dismissal of 90 employees in Gossau. The Organization for Economic Cooperation and Development (OECD) predicts difficult times for Swiss employees: 700,000 jobs are associated with a “high risk of automation.” [1] Moreover, this is only a small, partial reflection of the whole, global labor market.

It is difficult to account for the full impact of digitalization since it bears both positive and negative effects for the job market. Statistical evidence however indicates that digitalization affects the distribution of work, income and wages. [2] With skills like problem-solving, creative thinking and complex communication that are complementary to digitization, high-skilled workers tend to benefit the most from digitalization. As a result, we can observe an increase in high-skilled jobs in most OECD countries. Likewise, the share of low-skilled jobs grew while the share of middle-skilled jobs decreased. [3]

Why are middle-skilled workers at greatest risk to be disadvantaged under digitalization? Martin Wörter, Professor of Innovation Economics at ETH Zurich explains that “repetitive activities in the office or in industrial production can be replaced more easily by computers or robots.” Federal employment statistics for Switzerland back up this statement. Within 20 years the number of office workers decreased by 150,000 while the number of craftsmen fell by 90,000. Conversely, the number of academic professions grew by 470,000. [1]

However, it is short-sighted for companies to simply lay off unqualified workers and to replace them with employees who fit the demanded skills profile. Since skill requirements are changing faster than ever, even if companies could replace their unqualified workers today, what about tomorrow? The only way to solve this problem is to enable reskilling of the existing workforce. Further training could largely reduce redundancies and benefit the company at large. As Bruno Staffelbach, Professor of Human Resource Management and President of the University of Lucerne, says: “Company-specific know-how will become even more important in the future. However, employees can only acquire these skills on the job in their company.” [1]

Many companies have realized this and adopted effective skills development programs.  However, as we have written before on this site, workforce reskilling requires an ecosystem approach that involves individuals, companies, industries, as well as governments. Based on calculations by the World Economic Forum, in the US 45% of workers at risk could be collectively reskilled through businesses working together. If combined with governmental efforts, this number could increase to even 77%. [4]

For almost a decade, JANZZ.technology has been watching and collaborating in many labor markets worldwide. We offer our know-how and the right data on skills and specializations to tackle general challenges in the job market. Our latest product, Labor Market Dashboard uses real time data in order to establish important labor market indexes such as the most required skills, the most searched for positions or the female/male ratio. Should we have caught your interest and should you wish to learn more about JANZZ.technology’s offers, please write now to sales@janzz.technology

 

 

[1] Albert Steck. 2019.Digitalisierung gefährdet Jobs von Mittelqualifizierten am stärksten. URL: https://nzzas.nzz.ch/wirtschaft/digitalisierung-gefaehrdet-jobs-von-mittelqualifizierten-am-staerksten-ld.1492570#swglogin [2019.07.02]

[2] OECD. 2015. OECD skills outlook 2015: youth, skills and employability, OECD Publishing, Paris, URL: https://doi.org/10.1787/9789264234178-en [2019.07.02]

[3] OECD. 2019. OECD skills outlook 2019: thriving in a digital world, OECD Publishing, Paris, URL: https://doi.org/10.1787/df80bc12-en [2019.07.02]

[4] Borge Brende. 2019. We need a reskilling revolution. Here’s how to make it happen. URL: https://www.weforum.org/agenda/2019/04/skills-jobs-investing-in-people-inclusive-growth/ [2019.07.02]

Occupational classification systems in the digital age

People have long been monitoring the economic activities of our society. It is said that during the Chinese Tang Dynasty (618-907) there were 36 different job types. Fittingly, the period marks the origin of the famous Chinese saying that ‘every trade has its master’ (san shi liu hang, hang hang chu zhuang yuan).

Today, jobs are changing at such a speed that it is almost impossible to give an exact number of the occupations that affect our daily life. Compiling statistical records of occupations is also becoming complicated since jobs are changing, disappearing and emerging. There used to be only ‘the’ manager, but now there is a PI manager, an IT manager, a project manager, an intergenerational engagement manager, you name it.

Thus, other than listing simply all occupations for statistical purposes, job descriptions, skill and experience requirements, education levels and more aspects are integrated, too, in occupation-related databases. That way, we can not only better understand the jobs of today but also develop more sophisticated systems that are able to perform more complex services with occupation data. For example, this enables performing the tasks of career planning, job searching, identifying trends or guiding policy design.

US-based classification systems

The United States Department of Commerce released the Standard Occupational Classification (SOC) in 1977. Back then, many programs by the US government began collecting statistics which is why the federal government needed a unified occupational classification system. SOC entails a short description and illustrative examples for each job. It is classified based on the type of work performed, but rarely on the level of skills and education needed for a specific position [1]. The latest version of SOC was published in 2018.

 The online database O*Net is an expansion of SOC and was created during the mid-1990s by the US Department of Labor’s Employment and Training Administration. O*Net can be freely accessed and downloaded by job seekers, students, businesses researchers and workforce development professionals alike. Compared to SOC, it is a much more sophisticated system with more detailed information such as tasks, technology skills, knowledge, abilities, education level and work style.

 Europe-based classification systems

The international Standard Classification of Occupations (ISCO) is maintained and managed by the International Labour Office (ILO). ISCO is the main international classification of occupation-related data and used for international exchange, reporting and comparison. It also serves countries and regions that want to either further develop their own occupational classifications or directly adapt one from ISCO-08. Examples include Ö-ISCO in Austria, Styrk-08 in Norway, COCR-2011 in Costa Rica, NOC 2016 in Canada and most national occupational classifications in Asia.

In July 2017, the European Union launched the first version of a European multilingual classification of skills, competencies, qualifications and occupations (ESCO) that is also based on ISCO-08. ESCO aims to create a common understanding of occupations, skills, knowledge and qualifications across the EU’s official 24 languages that enables employers, employees and educational institutions to better understand needs and requirements. Under freedom of movement ESCO could aid in making up for skill gaps and unemployment in the different member states, as the President of the European Commission Jean-Claude Juncker states [2].

Industry classifications

Industry classifications or industry taxonomies group companies by industry and in terms of production processes, products or job positions. They serve national and international statistical agencies for the analysis, comparison and summarization of economic conditions. Well-known industry taxonomies include NAICS, ISIC, GICS, NAF 2015 and MUPCS.

Furthermore, a shift from occupational classifications towards skills classification has been observed. This shift is linked to an attempt of improving classifications’ ability to aid in career guidance and the conduction of upskilling and reskilling. The United Kingdom and the innovation foundation Nesta have built the UK’s first data-driven skills taxonomy. It allows for measuring the country’s supply and demand of skills and for preventing skill shortages. The social media platform LinkedIn has also built a skills taxonomy for its users.

Chinese classification systems

China started to create its occupational classification in 1995. After four years, the country released its first version. Currently in use is a version from 2015 that aims to keep pace with the fast-changing employment sector. The Chinese classification has 4 digits with 1838 professions in total.

Compared to O*Net, which was created during about the same time period, there is still much room for improvement in the Chinese occupational classification. Specifically, it could be improved with regard to accessibility, continuous data updating and the provision of guidance for students and job seekers [3]. However, the problem of lacking in updated data is not unique to the Chinese occupational classification. This issue is shared with many other classifications, including O*Net.

A new concept for occupational classification systems

The creation of a traditional expert consultation taxonomy is time-consuming, costly and, most importantly, will lack the ability to continuously adapt to the world’s fast-changing working environment.  Therefore, a new solution is needed. One that can inform the labor market constantly and make job seekers, students, education providers, employers and policy-makers alert for change and empowered to react.

With digitalization, a data-based information collection methodology can revolutionize the way classification systems are created. At JANZZ.technology, we have mapped all international occupational classification systems and others in our ontology. (If you would like to learn about the difference between taxonomy and ontology, please check https://janzz.technology/ontology-and-taxonomy-stop-comparing-things-that-are-incomparable/).

This mapping allows us to analyze complex sets of occupational data and to annotate it with intelligent and standardized meta-data, which makes the data comparable in further processes like benchmarking, matching or statistical analyses. Our JANZZclassifier! is a product for everyone who has large volumes of (unstandardized) occupation-related data such as job titles, hard and soft skills and, particularly, training/qualifications. It enables you to simply run your data through our API and will return more meaningful data and, if desired, one of the standard classifications.

Above all, we are using real-time data, both from our users, our partners and the labor market in order to constantly update our database. It is the new way to develop classification systems in the digital age. Please write now to sales@janzz.technology if you wish to learn how our ontology may assist you.

 

[1] Jeffrey H. Greenhaus and Gerard A. Encyclopedia of career development.

[2] ESCO (2015). ESCO strategic framework. Vision, mission, position, added value and guiding principles. Brüssel.

[3] LI Wen – Dong and SHI Kan. 2006. A brief introduction to the development of the U.S. national standard occupational classification system and its implications to China. URL: https://www.docin.com/p-1479318301.html [2019.06.24]

 

 

Examples of NLP applications in talent acquisition

JANZZ’s ontology-JANZZon! fits the description of a natural-language processing application for talent acquisition and can be placed alongside Google’s Cloud Jobs API. Gartner’s article Impacts of Artificial Intelligence and Machine Learning on Human Capital Management summarizes that both AI and machine learning applications actively transform the ways in which HR processes are done today. To read the full report please go to: https://www.gartner.com/en/documents/3778864/impacts-of-artificial-intelligence-and-machine-learning-