Protect the young people: How accidents and illness at work cost lives and money worldwide

Young people are much more often affected by accidents at work and health problems resulting from their work than older employees. According to the European Agency for Safety and Health, they are up to 40% more prone to work-related injuries than their older colleagues. Therefore, young employees need to receive much better protection and training from their employer.

The United Nations defines young workers as workers between the ages of 15 and 24, irrespective of the kind of work they do, whether they are permanently employed, in an apprenticeship or internship or support the family business. There are 541 million young workers worldwide, which represents 15% of the workforce.

Many people work in dangerous conditions. Especially in areas such as agriculture, construction or production, many work-related accidents are reported. According to the International Labor Organization (ILO), over the course of one year 374 million employees worldwide suffer from occupational accidents. Only in 2015, just under 3900 so-called fatal accidents at work were recorded in Europe. The term “fatal accidents” describes thereby accidents resulting in death.

A sound safety training is necessary.

Young people are particularly affected by high risks because they do not yet have the same skills and experience as older employees. They are therefore less able to assess dangers and have not yet received the knowledge that is necessary to prevent or reduce dangers. Additionally, their bodies and brains are not yet fully developed. For example, the frontal cortex, where rationality and judgement are located, only develops completely after reaching the age of 20. Many devices and tools are designed to be used by adults, which makes it likelier for young people to injure themselves when using them. Likewise, their bodies‘ reactions to pollutants are stronger. Furthermore, young employees are often not able to point out workplace grievances, either because they do not recognize them or because they do not dare to accuse their employer.

In general, the probability of an accident at work is four times higher in the first month of a job than during the entire following year. This probability increases enormously for young workers: it is five times higher in their first month at work than for older employees. The European Agency for Safety and Health at Work cites as an example a case of an 18-year-old man who died after four days of apprenticeship from burns he sustained while disposing of petrol and diesel. His employer had not provided basic safety training to him or any other employee.

Based on this and many other experiences, the agency points out the importance of workplace safety and health training, especially for young employees. Thus, it advises for example to provide extensive information about frequent and special dangers, possibilities of self-protection, contacts in case of dangers, as well as actions in an emergency. It also recommends to train supervisors, specifically when they are dealing with young employees, as it cannot be expected that they have the same prudence as older employees. In many countries, it is also stipulated by law that the employer must identify and assess risks in order to take measures to prevent them.

After all, much is lost due to inadequate work safety. The ILO estimates that poor workplace health and safety conditions cost around 4% of global GDP per year. Companies and the economy are severely affected by accidents at work, as potential hazards can already reduce productivity, and employees affected by accidents and illness are unable to work. Furthermore, investments in workplace adjustment might be necessary after a potential disability of the affected employee. Last but not least, one must not forget that accidents at work can destroy career opportunities, social security and the general well-being of (young) people for a lifetime. In the long and short term, training and investments are therefore worthwhile for employers and employees alike.

However, the most important safety instructions should already be shown in a job advertisement. This acquaints the new young employee with the local conditions right from the start and thus ensures greater prudence. With the knowledge graph JANZZon! and the well-developed, multilingual typeahead APIs, job advertisements of professions with a particularly high-risk potential or an above-average proportion of young employees can be semantically enriched. By entering the necessary safety skills and information from the very beginning they can be meaningfully integrated into the hiring and training period processes.

Even more ado about nothing … or why the hype about big data and AI is often more about self-marketing than facts and real progress.

Every two days we produce the amount of data that was produced in total from the beginning of civilization until 2003. This shocking statistic was first presented by former CEO of Google, Eric Schmidt, in 2010. Since then, data production has certainly accelerated. Although mass data processing is nothing new, the hype surrounding the more familiar term “big data” only started in recent years [1]. But many people are quickly getting lost in this ever-growing jungle of data and often quite abstruse data processing methods.


Coincidences cannot be calculated …

… because “more data does not mean more knowledge,” as Gerd Antes proclaims succinctly in an interview with the Tagesanzeiger. The mathematician strongly criticizes the hype about big data usage because the mass of data leads to a higher probability of random correlations. For example, per capita cheese consumption and the number of deaths caused by entanglement in bedsheets in the USA show an identical curve. Machine analysis would possibly draw conclusions from this, whereas a scientist immediately recognizes it as a coincidence. [2]

Nevertheless, according to many big data supporters, coincidences no longer exist. They believe that if the quantities of data available are large enough, all interrelationships can be calculated in advance with the help of machine processing or deep learning and the right type of analyses. Past experience and available training sets are sufficient for this, and there is negligible risk of error ranges due to missing or irrelevant data. However, such a conclusion is fatal. Of course, certain areas, periods of time and interrelationships, etc. can be explored more easily, for which something is more or less likely to happen. However, this certainly does not mean that coincidences or significant deviations are impossible. For example, how can we expect an analysis of data collected from the past to precisely predict traffic accidents in the future? Or diseases, since information on disease progressions – and thus digital patient data – can be incomplete, inconsistent and/or inaccurate. [2]

Big, bigger, big data? Don’t exaggerate on your achievement.


Data analysis can thus be life-threatening …

Especially with regard to the field of medicine, Gerd Antes is not alone in cautioning against the pitfalls of big data and AI. If an incorrect treatment method is selected due to the results of big data analyses and machine learning, the effects can be devastating – for patients, for wallets and for reputations. With such enormous amounts of data available, true correlations and inconsistencies may not even be discovered. Inconsistencies and correlations can threaten or save lives. [2]

IBM made negative headlines again recently when the media company STAT analyzed IBM internal documents for a report which concluded that Watson for Oncology had repeatedly recommended “unsafe and incorrect” cancer treatments. The report also claimed that IBM employees and supervisors were aware of this. Although no deaths have been proven to have occurred as a result of these proposals, many prestigious hospitals have decided to stop using the multi-million-dollar technology. [3]

In this respect, the first signs of a rethink and a somewhat more rational approach in this area are already visible. The two to three years of seemingly boundless hype about IBM’s wonder computer Watson in the field of medicine is finally coming to an end. This will also happen in many other similar fields – at the latest, when people realize the importance of facts, reliable results and relevance rather than self-marketing and grandiose promises by well-known global tech groups with their often still very experimental products. It is certain that the aforementioned developments in the field of medicine can be transferred almost 1:1 to the digital HR market, for example with regard to the matching of jobs and skills.


Trustworthy knowledge comes from experts

Over five years ago Cornel Brücher published his provocative work “Rethink Big Data” in which he described big data supporters as fools. We at JANZZ have held a similar point of view from the beginning. It is simply not possible to acquire knowledge in the field of jobs and CVs, including more complex occupation data, by means of machine learning alone. Anyone who says otherwise is demonstrably wrong. And will remain wrong, no matter how often the same ideas and products are advertised and marketed; and even if much more money is invested in such technologies than before.

For this reason, and despite considerable investment, results that are based on this “big data approach” are still largely inadequate and have barely improved over recent years, regardless of the size of the data records used, e.g. for LinkedIn and IBM & Co. The results from machine learning will become increasingly error-prone as more factors and variables – and thus complicated rules and relations – are added. With the risk being that incidents of erroneous correlations or even assumed causality can occur. Knowledge graphs or ontologies, on the other hand, enable knowledge to be mapped and used in a very deep and structured manner. Knowledge concerning knowledge graphs is highly verifiable and trustworthy because the know-how of various experts is stored and connected in a structured manner – rather than being calculated by computer scientists who are experts in programming, but not, for example, in the fields of medicine, engineering, investment banking, etc. Since knowledge graphs reflect the relationships between many different areas, only they can provide relevant and precise search results and recommendations. For example, in the area of occupation data: A knowledge graph recognizes the difference and the connections between competencies, experiences, functions, specializations and education. They take into consideration, for example, that for job title “J” with apprenticeship “A,” skill “S” is very important. Let’s take a Senior Cloud Architect as an example. A knowledge graph recognizes this job title and knows that, for example, a master’s degree in computer science could one day lead to the applicant securing this job if he/she also has the skill “cloud solution development” and several years of professional experience.


Google also relies on experts and a knowledge graph for occupation data

This was proclaimed by Google when the company launched its knowledge graph “Google Cloud Jobs API,” on which its Google for Jobs search is based (see „Google Launches its Ontology-powered Jobs Search Engine. What Now?“). Google realized then that an ontology-based approach would give better search results. In the case of a semantic search based on the knowledge of a knowledge graph, a search for an “Admin Assistant” would not add results that are only similar to the search term, such as “HR Admin” or “Software Admin.” Or a big data analysis could possibly determine random correlations and thus suggest completely different jobs that only have similar skill requirements (engineers, for example, but also office assistants need knowledge of Microsoft Office).

To know the difference and thus truly know about job search and have a general understanding of professions and their interrelationships is therefore generally only possible with a knowledge graph. Matt Moore, product manager of Google Cloud, stated as the reason for introducing Google Cloud Jobs API: “We want to provide a better job search experience for all employers and candidates. Because, let’s face it: Hiring the right people is one of the most important things your company needs to do.” [4]


Only people have the knowledge necessary to comprehend human nature …

This raises the question of whom you can really trust when it comes to this most important task: the selection of employees. It’s a never-ending story: According to the CV, the applicant was the perfect candidate, but unfortunately he/she did not fit in personally. Drawing such conclusions, which are not suggested by the available (digital) data, is at a level where it is the turn of HR specialists, humans. Technological tools can manage CVs and rank them according to obvious findings such as education, skills, experience, etc. if the data flood is manageable and, above all, is correctly evaluated. Even the best candidate according to the documentation can suddenly disappear into the crowd due to the large number of misinterpreted or misunderstood criteria. And the best CV does not always belong to the best candidate. In the firm belief that even this last remaining human factor will finally be banned from selection processes, more and more tech companies and start-ups are trying to digitalize this dimension and control it with artificial intelligence. This is again done with mostly unsuitable methods and even before the process-enabled, existing digital data would have been correctly used and evaluated. The specialists and leading providers of technologies who have been dealing with serious and resilient processes and products in digital HR for several years now agree on this to a large extent – not only since Google entered this market segment. [5]


Big data limits knowledge development

So, more data really does not mean more knowledge. Knowledge must be structured, stored and validated. And people with the right expertise have to be involved. Caution is therefore called for in combating a flood of data that can no longer be structured and which results in random correlations. Alexander Wissner-Gross, a scientist at Harvard University and the Massachusetts Institute of Technology (MIT), summarized it interestingly, “Perhaps the most important news of our day is that datasets – not algorithms – might be the key limiting factor to development of human-level artificial intelligence.” [6]

So, it is above all the content of knowledge that is promising, not the amount of data from which this knowledge is to be extracted. In the end, it is promising and reassuring that only experts or tools based on real expertise in many important areas, such as medicine or recruitment, can make reliable and correct judgments. All this makes the hype about big data and AI in HR a little more bearable. And our mission at – “We turn big data into smart data” – is more up to date than ever.


[1] Brücher, Cornel. 2013. Rethink Big Data. Frechen: MITP-Verlag.

[2] Straumann, Felix. «Vieles ist blankes Marketing». Big Data. In: Tagesanzeiger (2018), Nr. 168, P. 32.

[3] Spitzer, Julie. 2018. IBM’s Watson recommended “unsafe and incorrect” cancer treatments, STAT report finds. URL: [2018.08.01].

[4] From video: Google Cloud Platform. 2017. Google Cloud Jobs API: How to power your search for the best talent (Google Cloud Next ’17). URL: [2018.08.03].

[5] Watson, Christine. 2018. RecTech is creating more – not less – need for the human touch. URL: [2018.08.09].

[6] Alexander Wissner-Gross. 2016. Datasets Over Algorithms. URL: [2018.07.27].

The lifting of the driving ban for Saudi women (finally) creates new job opportunities

The women of Saudi Arabia have achieved an important new freedom: finally they are allowed to drive a car. Until 24 June this year, they were legally prohibited from doing so. Now that the ban has been lifted, Saudi women can move more freely, which will likely improve their opportunities in the labor market. Only 22% of Saudi women are currently employed, compared to 77% of Saudi men. When comparing the proportion of working Saudi women with those of other Arab countries, it appears strikingly small. For instance, in the United Arab Emirates, 47% of women work.  In Quatar, it is even 58%.

The economy suffered greatly from the driving ban. Firstly, women could not reach many employers, which made their employment often impossible. Secondly, men often stayed away from work to drive their wives. Under the ban, those who could afford it hired a driver, as women were also not allowed to use taxis on their own. However, this luxury exceeds many Saudi citizens’ budget. Thus, driving one’s wife was often a practical reason to stay away from work.  Furthermore, since Saudi Arabia’s cities are very spacious, walking or cycling no real option. At the same time, the country’s public transport network is not yet well developed.

Currently, 32% of Saudi women who are looking for a job are unemployed, and Saudi Arabia’s youth unemployment rate is as high as 40%. Yet, Saudi women are on average better educated than men. Now that women are allowed to be in the driving seat themselves there are a number of new job opportunities. Companies have already discovered new possibilities: car rental companies are advertising training and employment for women and an insurance company has already trained some women as accident inspectors. Since the beginning of this year, Saudi women are also allowed to serve in the military, where the ability to drive facilitates employment considerably. In April 2018, the Ministry of Labor and Social Development further decided to privilege Saudi Arabian citizens in many retail areas of employment. These areas include, for example, watch shops, optician’s, electronics stores, bakeries and furniture shops. Additionally, the regulation applies to trade in car parts. As these trade sectors continue to have a high demand for personnel, women can furthermore be employed to visit customers or deliver goods.

In spite of these developments, there are also some obstacles. In principle, Saudi women and men are allowed to work together, but there are special requirements for common workplaces. They must provide separate washrooms and break rooms, as well as a safety system. Many of Saudi Arabia’s employers are not prepared to remodel their buildings, which have long been intended only for the employment of men.

On statutory level, however, Saudi Arabia has in many ways cleared the path for a higher participation rate of women in the job market. In principle, a Saudi woman’s (male) legal guardian must consent to all her important decisions. The legal guardian is usually the woman’s father or husband, but in certain cases the son, too, can take up this position. In today’s Saudi Arabia, women can still not leave the country, be released from prison or get married on their own. Since last year, however, they no longer need approval to start a business, they can serve in the military, open bank accounts and apply for public services. These decisions are part of “Vision 2030,” a development program for the Saudi Arabian labor market. A large proportion of this plan includes interventions to increase the proportion of women in the labor market. Although it is no longer a legal requirement for a woman to have her guardian’s consent to work, it is often still demanded by the employer.

The fact that so few Saudi women work is increasingly becoming a problem for the country. Its economy is undergoing a major transformation, as it is booming. So far, increasing labor requirements have been satisfied by hiring foreign workers. That is, a large number of available jobs in Saudi Arabia are allocated to foreign citizens: only 5.6 million of the country’s 11.9 million employment positions are held by Saudis. At the same time, its economy is heavily dependent on the oil industry, which generates almost 45% of real GDP in Saudi Arabia. Overall, the labor market is very homogeneous, with 67% of Saudis working for the state.

The Ministry of Labor and Social Development has recognized that the Saudi labor market needs to be fundamentally transformed, as dependencies and untapped potential begin to adversely affect the economy. Saudi women’s potential has now been recognized, particularly their quality education.  The lifting of the driving ban marks thus a first step towards the development of a new Saudi labor environment.

Where self-employment may not always be voluntary

Many people dream of being their own boss, of not always having to say „yes, gladly“, and thus taking on every unpleasant task that comes to the boss’s mind. At the same time, some people fear self-employment because of the unsecured income and pension. Others see in it precisely an opportunity of having more regularly money in their accounts than employees. Various studies by the OECD (The Organisation for Economic Co-operation and Development) explain the background to this as follows.

The OECD defines self-employment as „the employment of employers, workers who work for themselves, members of producers‘ co-operatives, and unpaid family workers.“ Regular surveys of the organization show that the self-employment rate of the total labor force varies greatly from country to country. In the United States, for example, only about 6.4 % are self-employed. – In comparison, Columbia’s (self-employment) rate is at 51.3 % (see chart according to OECD, 2018).

There are several reasons why self-employment has different levels of popularity. In some countries, a large proportion of the labor force still works in the agricultural sector where self-employment is a common practice. For example, about 15 % of the Colombian population still work in this sector. In countries with a small proportion of self-employed people, such as Denmark, Sweden or Canada, it is only around 2%. The United States’ self-employment rate has declined considerably over the last 25 years: in the mid-1990s, it was situated in the statistical midfield. This is due to the enormous reduction of the agricultural sector in the United States that led many people to quit their self-employment.

OECD (2018). Self-employment rate as percentage of total employment.

There are further factors that can lead to a high proportion of self-employment. In addition to crime and non-transparent cash flows, working and income conditions might influence a country’s self-employment rate. It is ever more frequently observed that countries with a high average wage tend to have a lower proportion of self-employment. In that regard, the United States is one of the highest-ranking countries with an annual average wage of more than USD 60,000, while Denmark, Canada and Norway are also among the top with around USD 50,000. This stands in opposition to Colombia, which has an average wage of about USD 6,000.

As an equivalent, countries with a large proportion of citizens living below the poverty line tend to have a higher proportion of self-employed. According to OECD figures, 24 % of the Colombian population are affected by poverty. In 2016, the OECD furthermore examined in which countries many people live below the poverty line in spite of being employed. Here, too, a great number of citizens of the most affected countries are choosing self-employment particularly frequently, for example in Brazil, Greece, Mexico and Turkey.

Another indicator is the number of working hours. Greece, Mexico and Chile show particularly high numbers of working hours (around 2000 to 2250 hours per year). At the same time, Norwegians and Danes work particularly few hours (approximately 1400 hours per year), and a particularly small percentage figure chooses self-employment. Colombia clearly ranks first in the average weekly working time of contract-based work as an employee, with more than 48 hours. Looking at all employees (contract-based, non-contract-based and self-employed), this number is already reduced to 44 hours.

Last but not least, labor market insecurity, meaning the likelihood of losing one’s job, and a high workload might lead to the choice of self-employment. These indicators are particularly low in Norway, Canada and Denmark, which remain countries with a rather low proportion of self-employed people. At the same time, these figures are especially high in Turkey, Greece and Colombia.

Colombia, where self-employment is by far most widespread, is facing severe structural problems in the labor market. Employment relationships here are mainly informal which means that people are employed without a contract or social security, often for their own purpose and without exchange value. Informal work is very difficult to measure and control or to regulate by laws. Many employers are forced to continue employing informally because of the high costs, and at the same time many Colombians are not qualified enough to get a good, formal job. Similarly, many people choose (informal) self-employment in order to avoid costs, such as taxes and other fees, so even 93 % of the self-employed people work informally. Moreover, Colombia has set a very high minimum wage above the OECD average. Many employers are unable to pay this amount which has further strengthened informal work and unemployment.

Is it always about being your own boss? No, because in some countries people may feel compelled to be self-employed. To counteract this, there are constructive actions and laws for the individual labor market. consults Public Employment Services on possible interventions to make formal work attractive and effectively protect employment conditions. Please feel free to contact us by e-mail to

How a modern and customized high-performance solution for PES is ready in 180 days

Public Employment Services (PES) come from time to time to a point where they are forced to renew their outdated job-search systems or to set them up from scratch in countries where no solutions at all were available before. With the rapidly progressing digitalization, the demands and expectations of such solutions have increased greatly: Consistent processes, intelligent matching, comprehensive data and labor market analyses, simple parsing and classification facilities and so on.
Developing and successfully launching such sustainable and high-performance solutions is therefore a complex task. The required processes and the development work can take several years and are costly, especially for smaller PES. With a proven, scalable white label platform with state-of-the-art modular components, this process can be realized quickly, easily and cost-effectively for all PES requirements and sizes. We would like to show you how this works in the following post.

Model patchwork rug and plenty of time…

Such a solution is often composed of existing internal and new acquired components. This type of model can work, but it requires a lot of time, a huge budget and usually even more effort. Many PES (excuse the expression, but unfortunately this is the case) patch up their new solution because they often already use some existing components and skilled staff such as software engineers and project managers is available as well. But how often did they develop such a modern PES platform with all necessary processes and components in recent years? And how well are your own data specialists and taxonomists prepared for the changed requirements? Can they make the data and content available in the desired form and structure for consistent, digital processes, e.g. for matching?
The development of a modern and powerful platform that brings job seekers together with the labor market in a more effective way is an extremely challenging task for job advisers in any case. It contains many equally demanding subprojects. For each component which this platform should contain, suppliers usually even have to be checked and tested via public tender procedures and in-house solutions have to be revised or even completely redeveloped. From matching to parsing tool, interface and UX to support chat. Even the consulting and evaluation for each individual tool and each sub-process create lengthy processes. Who understands this piecemeal solution? Which provider offers the right approach and has the necessary experience?
A lot of research, a lot of conversations, a lot of negotiations. It should also be kept in mind that new technologies and tools alone cannot save the day. All processes must be adapted as well. The job placement, the actual core business of PES, has already changed considerably. Every five to ten years the approaches and processes have to be changed and reviewed again. In the future, with increasing digitalization and with the effects on labor markets, more often than ever.
In this way, several valuable years can pass from the project kickoff until the solution goes live. And sometimes the new solution is already outdated by the time of its launch. In any case, a lot of time usually passes while an efficient white label platform could already be running for a long time.

Often untestable until launch

This is not only a great challenge in terms of time. A patchwork rug approach is also complex and risky. The individual solution components must be developed and integrated into a large entity. Now and then into an old, existing platform or front-end. Or it can even be newly developed and built in-house. Internal and external suppliers have little or no knowledge of each other’s components. This also makes testing more difficult. Usually it is only possible to test how the components work together in depth shortly before going live because there are no references for such a constellation which has not existed in this composition and mode of functionality so far.
The temptation can be great to build a part of the solution (for example the front-end) using your own development resources. What can bring advantages in certain cases, can lead to risks and unforeseen costs for a large number of PES. Customizable off-the-shelf solutions, on the other hand, offer stability, reliability and efficiency for operating procedures and future maintenance.

Stable, reliable, efficient

The stability, further development and the future maintenance of the patchwork model are not fully ensured since changes to the software are more difficult if the core components of the solution mainly run via an external API. In-house developed solutions must implement a software layer for the API. If changes are made to the API (minor or major), these changes must be made in the corresponding software modules as well. This can lead to instability and/or maintenance problems if detected too late or not at all.

Neither can you count on reliable performance. The risk of errors increases due to the combination of several software systems. At the same time, localization and finding the cause of the error, or „root cause“, becomes more complex since it is first of all essential to identify in what part of the solution the error occurred.
In contrast, a white label platform already successfully used by other PES offers significant advantages in terms of user-friendliness, efficiency and customization of the user interface. In addition, this solution already offers a simple-to-use mobile user interface with responsive design and many other functions as part of the standard package.
Finally, many costs are eliminated, such as for maintenance and further development of the self-built part and for integrations. In-house support is hardly necessary since this task mainly belongs to the platform provider who knows all the features of his platform very well. Expensive new hires are therefore also barely or not at all necessary. In addition, it usually takes another 20 years before a new in-house solution is designed and developed. It is difficult to keep the experienced and qualified developers, architects, taxonomists and UX specialists (of whom there are far too few everywhere and who are very much wanted by the industry…) and to provide them with enough interesting work in the meantime.

A single solution is all you need

The ideal solution by has long been ready: A customizable platform in which all required features are integrated and adapted to the individual needs of all PES, even with a smaller budget. The solution has been thoroughly tested and was built with many years of know-how of many other PES around the globe. It is available in many languages, with ISCO-08, ESCO and of course all country-specific classifications, as well as all necessary design, process and color combinations. Whether as a GDPR-compliant cloud service solution (which also releases you from other expensive tasks) or as a high-performance on-premise installation according to your specifications.
The platform is available quickly and easily: The existing, broad and proven know-how of and the easily adaptable structures, UI and the currently most powerful, semantic technical components, etc. can reduce the project duration to around 180 days. This means budget and project security and very, very short 180 days until the first job seeker can be placed with the new system.

When can we introduce your future solution to you?

Let the women calculate: Why Public Employment Services should convince women of STEM subjects

Despite many efforts, young women are still under-represented in science, technology, engineering and mathematics (STEM). Studies show that many advantages can be achieved by completing a STEM degree, both for individuals and for entire countries. Public Employment Services should therefore effectively strengthen the STEM sector. There are various reasons why:

Many employees with a STEM degree earn higher salaries. The US Bureau of Labor Statistics emphasizes that 93 out of 100 STEM occupations have wages above the US average. At the same time, the average income of graduates in STEM occupations was twice as high as the one of non-STEM graduates. STEM graduates also earned more on average if they worked in a profession that was not in the STEM sector. Statistics from other countries confirm these wage forecasts. Last but not least, the probability of becoming unemployed is much lower with a STEM degree. For example, the unemployment rate among STEM employees is overall lower, and in the United States only about half as high as among non-STEM employees.

It is no secret that an increasing number of specialists are being needed in this sector, if only because of the technological developments. For example, headlines recently stated that only in Germany there is a shortage of 100,000 engineers, most of them electrical engineers. In addition, mathematicians are being used more and more in every field. Whether it’s production planning, insurance benefits, salaries or your favorite lunch – they calculate basically everything. For example, it was recently calculated that people may survive a fall into a black hole.

Female share (%) of all tertiary graduates in science, mathematics and computing, 2014 or latest available year. Source: OECD (2017). The Pursuit of Gender Equality: An Uphill Battle, Fig. 1.1 B.

In order to meet this need, a great number of STEM graduates are needed but particularly women often do not dare to study a STEM subject. Studies have shown that girls do not generally perform worse in mathematics than boys but they are more often afraid of scientific subjects and predict themselves to perform worse.

How can girls overcome their fears of mathematics? How can their self-confidence be strengthened? The OECD recommends to start at an early stage, as it has been identified that decisions on the career path are usually taken at the age of 15. For example, STEM summer schools should accept a larger number of girls to alleviate their fears and highlight their strengths. It is currently twice as likely that a 15-year-old boy would like to work as an engineer, scientist or architect as a girl of that age. At the same time, less than one percent of girls want to become IT specialists.

Public Employment Services should combat this trend and invest in the future of their women and thus also of their countries. The digitalization requires many well-educated STEM employees, while at the same time citizens can be better protected from unemployment and a good standard of living can be ensured. We are glad to provide further hints at


Change or die – Four issues for the multifaceted future of PES

It is the dominant topic when it comes to today’s digital HR processes: how to develop better, more efficient, up-to-date tools and technologies for matching, which solve the various tasks and challenges in a more customer-oriented manner. Separating the wheat from the chaff is a very demanding task. If today’s technological choice is not up to the task of shaping the future, then this has a strong impact on matching. In particular, problems will be pushed forward into the future because matching means understanding the challenges a labor market faces. All providers of job-matching technologies believe that they are able to place job seekers and thereby stimulate the labor market. There is no such thing, however, as the labor market. Each individual labor market has its own characteristics, and merely placing as many people as possible in the labor market quickly does not suffice; after all, there are other complex problems demanding our attention. Four issues were selected from within this complex topic, which illustrate why it is not only a question of pure placement. And why prevention in the present is necessary for reducing problems in the future.

1) Full employment today, gap tomorrow

Is unemployment actually a problem in the Western world at the moment?

The latest figures for the US labor market released just after the first week of the year indicated near full employment (defined as three percent unemployment) with an unemployment rate of 4.1 percent, after 250,000 new jobs were counted in the last month of 2017: the lowest figure in 17 years. Mark Zandi, chief economist of the market research institution Moody’s Analytics, referred to the American labor market as “soon as good as it can be.” Many Western countries also currently have similarly low figures, even less than four percent in Germany and Switzerland, Norway only slightly higher, and the EU average is the lowest it has been in ten years. Even the United Kingdom has not yet been impacted by the Brexit in this regard. This begs the question: are the employment offices now planning long holidays?

Hopefully not; because it would be a fallacy to think that these nations need not worry about their labor markets. Each labor ministry is confronted with its own challenges, which is why employment offices always are busy. A simple placement solution does not suffice to provide fundamental support for public employment services. Required first and foremost is in-depth knowledge of the labor markets and the diverse challenges that are currently confronting us in every corner of the world.

One particular challenge today is digitalization. While the European labor market may be approaching full employment in many regions, this trend will make it too easy to replace employees in the future. Who needs a taxi driver when the car itself becomes a chauffeur? And who needs a cleaning assistant when cleaning is carried out by robots that clean even in the tightest corners? It is to be noted that there are major differences between jobs with lower qualification levels. It is much easier for machines to take on cleaning tasks than complicated sewing jobs, for example. Correspondingly, not all jobs with lower qualifications are at risk – but many are. And they are not alone. Employees with higher education levels can also be replaced, as computers become able to more precisely calculate and improve the static structures of buildings, logistics or production processes. Similarly, computers are increasingly considered more reliable and risk-averse than the human financial advisor at the local bank because they decide on the basis of facts and not emotionality.

These complex challenges cannot be solved with simple placement, because even if someone could be placed, this job could disappear in the near future due to digitalization. If the combustion engine will soon become obsolete and replaced by the electric motor, a considerably smaller workforce will be required, because the production of an electric engine will only require four instead of seven employees. The three superfluous ones will become unemployed, and to place them again, we cannot afford to merely watch and wait.


2) The divide is growing

If you look at certain occupations, the opposite occurrence can be identified as a challenge. While some occupations are disappearing, other sectors are now desperately looking for new employees. The numbers reported in the media continue to climb: 7,000 vacancies for nursing staff in Switzerland, 100,000 engineers lacking in Germany. How is placement supposed to meet a demand for which there are no capacities?

Consequently, the professions people would like to be trained in are decreasingly in line with their demands. People have grown accustomed to having great freedom of choice when it comes to choosing a career: almost everyone can decide for themselves which career path they want to pursue. This freedom leads to the situation in which some career paths are frequently chosen, while others are seldom chosen. This ultimately leads to a tremendous gap between these two groups. In many attractive professions it is becoming increasingly difficult to ensure a life span of four to five decades, and as we work for longer periods of time, this aspect is very important. How many marine biologists are really needed in Switzerland? And while the highly skilled marine biologists remain unemployed, engineers sign employment contracts while still studying in the lecture hall. This is a tragedy.

It should be seen as an enticement for politics, society, universities and all other parties involved to take on a new task: we have a demand, so let us increase the attractiveness of the field in demand. It is time to take action in training and career planning, not just to react in case of emergency, but also to prevent. What can be done to make young people choose the training that is essential? We must look into the future. Do we more extensively restrict access to highly frequented degree programs? Do we provide extra support to people who choose unattractive training programs?

Of course, increasing the salaries of professions such as nursing would make them more attractive. However, who will pay for this if consumers are not prepared to pay more? As long as products and services grow increasingly affordable, wages cannot be increased – which means that earnings are insufficient and the job is considered unattractive. Hence, a job cannot be made more attractive in this way.

When it comes to such challenges, it makes no sense to simply consider placement strategies, whether technological or non-technological. After all, this problem is not solved by simple placement. Instead, we should work to ensure that supply and demand are matched. New models must be created to enable a response to current trends and gaps. The gap analysis shows that the shortage is steadily increasing in all markets. Unfortunately, this cannot be solved by migration, although it is currently leading to many opportunities, particularly in Europe.


3) Emigration as the only way out

There are even entire regions in which income is simply insufficient. In these parts of the world, people feel compelled to leave to find work. In Lithuania, for example, in almost every family there is someone who works abroad, because with the rising cost of living, people there can hardly survive from their wages. As a result, the small country has lost more than half a million people in the last 15 years – a large number in view of a total population of less than three million. Especially young people are emigrating from the country either before or immediately after graduation, leaving behind a society that is aging even faster.

Consider the population of Indonesia: over a quarter billion people. People there may find their job market more interesting since the country’s economy is constantly growing. The population, however, is growing even faster – three million additional people each year, equivalent to the population of Berlin, Madrid or Lithuania. More than half of them are under 30 years old. All these young people will need a job at some point. Again, many will view emigration as a necessary solution. New models must be also created for such cases, models that balance supply and demand entirely differently. People cannot be placed where there are simply no jobs available.


4) Having a job is not enough

Even if jobs are available, simple placement strategies are not enough. For instance, some South American labor markets are attempting to combat underemployment, along with other challenges such as crime, drug abuse and the lack of transparency in money flows. Underemployment is not the same as unemployment but means an insufficient number of working hours. No adequate standard of living can be ensured with the resulting low wages. Even after the various and sustained efforts by labor ministries, the employment market situation remains complicated. In Paraguay, for example, the unemployment rate is around nine percent, a level similar to those in highly developed countries such as France or Finland. However, what does this value mean? Due to underemployment and a high level of day laborers, a large number of citizens do not appear in the unemployment rate, because they technically have a job. The unemployment rate does not decisively signify whether a reasonable standard of living is guaranteed in a country or region.


Reaction instead of awaiting

While unemployment rates may be low, a low rate does not save the job market. Each labor market has its own specific problems which need to be handled differently. There are many more challenges to be met: How to place people over 50? How to place highly qualified refugees? In principle, it is foreseeable that if PES do not adapt and thus master as many challenges as possible, major problems will cause that the PES will lose their raison d‘ être. A reaction to these challenges and discussions about them must be started now; discussions that are fact-based and therefore require the right tools and technologies. Nevertheless, success is not guaranteed by provision of the tools and technologies. Profound expertise has been developed over the course of a decade which knows precisely which problem areas should be tackled, at which location and by which method, and which consequently also knows how the tools are to be used correctly. Required is someone who applies this substantial expertise at an early stage. It is only a matter of time before unemployment rises again, especially among young people. If the proper fundamental understanding of these types of problems is applied, the possibilities can be identified at an early stage and coordinated with the right solution strategies. Furthermore, the specific requirements of the labor market must be brought to light, taken into account and acted upon now, in short: we must react immediately. I wonder why politicians, society, educational institutions and others are still standing by and observing. They should discuss these issues now with specialists who have this specific expertise. There are specialists who deal with, reflect on and analyze all the mentioned and unmentioned challenges of labor markets on a daily basis. The knowledge held by these specialists is available to you – at

The MTESS introduced an advanced platform for job matching

The Minister of Labour, Employment and Social Security (MTESS), Dr. Guillermo Sosa, presented today, Wednesday, February 21st, a new platform to strengthen the search for jobs in Paraguay. This platform was developed by, based in Switzerland. will implement its platform to improve employment opportunities for young people looking for a job, this being the company’s first project in Latin America. Besides Paraguay, provides service to more than 150,000 applicants and employers in 5 countries, using 40 different languages. The platform incorporates advances that enable people to find available jobs and job seekers through a variety of multiple dimensions, including soft skills, education, experience, contractual and geographical availability, among other variables that enhance job search. The platform has more than 100,000 man-hours of work development and brings its new version to Paraguay.

Through this new web platform called, the ministry of Labour wants to offer the most advanced technology for job matching. To this day more than 25,000 candidates have registered in the “PARAGUAY PUEDE MÁS” database. Once the admission process is concluded, young people will have access to job opportunities according to their capabilities and skills. This agreement is part of the Labour Inclusion Support Programme (PR-L1066), which is financed by the Inter-American Development Bank under a loan agreement with the Republic of Paraguay.

For more information regarding this topic: (Spanish)

How can man and job be matched for the perfect date?

It is extremely difficult to match one person with another by using technology to send them on a date. There will be numerous factors and expectations that have to be taken into account. Do they have similar interests? Are they living in the same area? What are their goals? And then there are plenty of hidden expectations regarding things such as appearance. Matching has always been a complex task.

The same is true when it comes to bringing together the right person for the right job. Even for specialists with years of experience, matching jobs and skills is a huge challenge. Who works well with what? How can you be sure of making a good decision? Every day, such questions have to be correctly answered in order to be able to successfully match person to job. This requires thorough knowledge and good information. The expectations of employers and potential employees are high. Can a machine or an algorithm more than satisfy these expectations?

How to match this complex data? Source: Getty Images.


Is good matching possible?

Firstly, let’s determine whether good matching is even possible. Matching is the act of combining complementary attributes of two entities, in our case job and person. However, even in this context the word ‘matching’ can have various meanings. In some jobs, whether a candidate is suitable for a given job is merely a question of whether he or she is able to work. If you are physically healthy, for example, you should be able to pick strawberries. There are other jobs, however, that require a variety of certificates, specializations and experience. Try to match a neonatal surgeon to a job in a hospital department and this becomes clear.

Although HR specialists realize that the tiniest details have to be considered during the matching process, their task remains highly complex. This is because the prevailing conditions are constantly changing. Requirements that were commonplace yesterday no longer apply today, and in turn today’s requirements will no longer be valid tomorrow. How we define job, prospective employee and labor market shifts all the time. Who would have needed a Director of Digital Development a few years ago? And who would have cited such a specialization in his or her CV?

Matching becomes far more complex when a machine has to deal with the task. A machine has to apply all the experience and knowledge of the specialist in the same way, pay attention to the smallest details and react to changes in the labor market. Suppliers of such machines focus on different data in order to overcome this highly complex problem. For example, former job titles of applicants or their skills are taken into account. An algorithm then compares job requests and CVs, and a match is made. Successful?


Bricklayer equals bricklayer. Sales consultant equals sales consultant?

Some algorithms, as we have heard, will make a match based on former job titles. If the candidate had position X at company A, he or she can also hold position X at company B, no? This may have held true in the past, yes. We used to be general practitioners, secretaries, lawyers, bricklayers, etc. Today we are sales consultants, data ninjas, facility managers, etc. Is a sales consultant someone who works in a retail shop and advises customers? Or someone who prepares offers, takes up orders and negotiates contracts with customers? Such questions are already being asked by specialists when they look at CVs. And now machines should be able to make such distinctions efficiently.

Job titles are therefore often too generic. Or too specific, as internal company terms influence job titles and therefore tend to describe functions. Nowadays, everyone is some kind of manager. Without a more detailed description of the jobs we would often be lost and would not know whether an applicant is really suitable for a position – or vice versa.


Comparing skills

A job title is not enough for good matching. So other job matching providers solve the matching problem by using other parameters – they look at skills and competences, since these represent the ‘content’ behind descriptions of sometimes cryptic job titles. Skills-based or competence-based matching is more meaningful and promising because it takes into account not only a title previously held by an applicant, but also that person’s knowledge, talents, insights and education. Thus, one considers the candidate’s skills and the skills required for a job, and matches them.

This sounds logical: I want a manager who is open-minded, communicative, strong in leadership and good at solving problems. I find someone who outlines such qualities in his/her résumé and thus corresponds to my criteria. So, are skills now a reliable factor for machines to evaluate the perfect match for my vacancy?

Let’s take a closer look at skills. Skills result from knowledge. Aristotle said that knowledge is the absolute truth. Absolute truth can only be attained if one has experienced and tested the knowledge oneself. Knowledge that I have acquired from others through communication and study must be verified and therefore is not necessarily the absolute truth. If someone tells me something new, how can I be sure it is true?

So, as long as I have not experienced this new knowledge – and applied it accordingly – it remains incomplete. There is no doubt that good education is of great value, but until I know how someone has used the acquired knowledge, it is not proven and does not give me the opportunity to benefit from it. Only when it has been tested does it give me an advantage or a certain scope for action and, to some extent, power.

Let’s return to my manager who is open-minded, communicative, strong in leadership and good at solving problems. Couldn’t it be that our potential candidates are managers in the construction, finance or clothing industry? Without their experience, the vacancy would probably have been matched to all three positions, although each job requires its own industry insights. There is a lack of relevant experience to put the skills into a meaningful context.


Real knowledge needs experience

This has been recognized by other job matching experts. The criterion of skills is not sufficient for good matching. If I want to match a job seeker with a certain profession, I cannot only take into account knowledge of the person’s skills based on his/her CV and cover letter. I will also need to know about experience. Only with experience can relationships and industries be developed.

In addition, no one mentions only the skills he/she has – but very often other relevant information that can contribute to a good match. Similarly, in a job advertisement, a company does not specify all the skills it is looking for – and this is a hindrance to matching. Because if a job advertisement appears for a “Data Scientist,” the employer will probably not mention “IT usage” or “data processing” as he/she will assume that such skills are evident from the job title. Similarly, a data scientist would probably indicate in his or her CV more specific skills than those associated with previous job titles. But if that person is to be matched according to skills, then information relevant to this matching parameter will be missing.

If we only matched on the basis of skills, I’m sure we would get different results than if we just compared job titles. However, such an approach is ultimately not good enough to guide people to jobs, applicants to positions and employees to employers. We need more.


Good education does not mean good manners

Knowledge of skills and experience cannot determine whether the new copywriter will fit into the team well, or whether the new nurse will arrive at the hospital on time, or whether the new procurement officer will negotiate well. Who would confide in a CV nowadays that he/she is a poor team player or is unreliable? Yet it is precisely these soft skills and the personality of the applicant that are incredibly important for a good match. A consultant must be punctual for a customer appointment, whereas a programmer can keep flexible working hours. Likewise, the programmer’s appearance is of less importance than that of the consultant. However, if the consultant is unable to speak openly to customers, his company will soon lose them. Accordingly, a match only becomes truly successful if the applicant’s personality is also considered. My CV details a wide range of things I have done, but how I have done them is also crucial.


Pulling together?

Now, if this CV fits that that vacancy perfectly, it is not yet certain that we will have a perfect match. After all, the skills and personality of a new employee have to complement a network of skills and personalities of colleagues. If I’m the only software engineer in a company, I have to be an all-rounder and take the initiative with ease. If I am hired in a team with two others – one of them is more familiar with field X, the other with field Y – skills complement each other and the collaboration creates something completely new. I can ask for help more often and at the same time I am expected to be able to fit in well with the team. The colleagues involved also influence the perfect match. To be precise, the CVs of the staff have to be matched as well.

Whoever still thinks that you can match an employee to a job by taking into account just one parameter (job title, skills, experience or personality) may realize that this can only work well if you are very lucky. If an algorithm is supposed to solve such a complex problem, the chances of a successful match are like those of finding the proverbial needle in the haystack.

So, are we at the end of the road?

Not yet. Confucius made the statement, “Experience is like a lantern in the background; it always illuminates only the piece of road that we already have behind us.”

We have tested our knowledge, brought us and others advantages, we may be punctual and reliable. We are in possession of the required soft skills. This means that we are sure to secure our ongoing business. All deadlines are met, the customers are treated well, and the employees occupy their seats punctually each morning. Everything should be fixed by now.


What truly strengthens a business?

But if everyone always conforms to the requirements, then business remains “only” secured. We don’t create anything new. Creating something new calls for good knowledge and often a great deal of experience. Above all, however, one needs – literally and semantically – creativity.

The Cambridge dictionary defines creativity as “The ability to produce original and unusual ideas, or to make something new or imaginative.”¹ Basically, by going beyond knowledge and experience, creativity gives us a third way of looking at something, which one could call “thinking outside of the box.” As an approach, creativity is therefore less artistic than rule-breaking: radical reworking, thinking outside the box, or throwing the box out altogether. The creative act produces something new, different and maybe a little frightening.

Albert Einstein said that “Creativity is intelligence having fun,”² so a creator is someone who enjoys turning the business around rather than someone who merely meets a catalogue of requirements. Creativity is of the greatest value in times when a great deal of change is happening. After all, anyone who simply adapts during the course of digitalization will not follow, and certainly will not move forward. We need the people who keep an overview. We need the employees who secure the business. And we also need people who show us new ways of doing thing, especially nowadays. Creativity is the most important skill today.

Creativity, intuition, emotions and anything contrary to logical, analytical and rational thinking (which could be considered analogous to knowledge and experience) are often attributed to the right side of the brain. You may have heard the theory that people think more with either the left or right side of the brain. However, researchers have found out that this is a myth. Even if some functions may be attributed more to one side of the brain, results are greatest when both sides of the brain work together in complex networks.³

If I want to create a new product, knowledge of the production processes and the materials required helps me. My experience in planning a new product also helps me. My organizational talent supports the process. But the idea of creating a new product results from my creativity. So, if you are good at something, then you get the best results because all factors are involved at the same time: knowledge, experience, personality and creativity.


Farewell to the notion of the perfect match

Let’s put it in a nutshell: matching cannot be competence-based, skills-based, or based on an ad hoc approach because the problem is too complex. Matching is driven by expectations and expectations change constantly.

Accordingly, there is simply no such thing as a perfect match, because it is impossible to overcome expectations. Expectations are very subjective and can never be fulfilled equally well for all. So we can only evaluate all the factors as far as possible in order to get as close as possible to the perfect match.

The results of today’s culture of matching with data shreds, such as a few skills or cryptic job titles, will destroy the quality of the machine again and again. Matching with data shreds is a tap in the dark. Those who believe that they can match data fragments with arbitrary keywords will never approach the perfect match. As we have mentioned, such an approach ignores other parameters that are crucial for high-quality allocation.

With complex algorithms you can only create the greatest possible approximation if you distance yourself from data fragments and try to include all factors, as does the brain when creating something new: skills, experience, personality and, if treated appropriated, also former job titles. The machine takes all these criteria into account, evaluates them in turn, and gives each a weighting. If these criteria are represented with an adequate weighting, a good starting point will have been reached to bring person and job together using technology. All determinants, including expectations, are aligned and the chances for the perfect match are thereby optimized.

Even with JANZZ. Technology’s well-designed, developed and improved matching processes, it is difficult to consider all factors to the correct extent. Expectations can be mapped on a large scale, but one part is always kept hidden. For example, if unemployed people are to be secured positions, a large part of the expectation is that they will actually be employed. If engineers are to be matched, there is the expectation that the salary band will correspond with that in previous positions. Further expectations can be mapped if it is clear that they exist. Accordingly, we can only approximate the perfect match. However, we are not fumbling in the dark with data fragments. The process may not end with the perfect date – but maybe with an invitation to another one.



¹ Cambridge Dictionary (2017). Creativity. Accessed from: [2017.11.02].

² Einstein, Albert (1930). Mein Weltbild. Wie ich die Welt sehe.

³ Nielsen JA, Zielinski BA, Ferguson MA, Lainhart JE, Anderson JS (2013). An Evaluation of the Left-Brain vs. Right-Brain Hypothesis with Resting State Functional Connectivity Magnetic Resonance Imaging. PLoS ONE8(8): e71275.

Sahoo, Anadi (2017). Knowledge, Experience & Creativity. Accessed from: [2017.11.03.].


ESCO: We expected an ontology – we got a disappointing term collection

Almost four years had passed. We have waited for a long time – and we were curious to see what the EU has announced grandiosely. Steadily excited to see if it solves well-known problems of classification systems. The classification of the European Union for occupational data is called „ESCO“ (European Skills, Competences, Qualifications and Occupations). Up to now, classifications have been solved by all states on their own, such as ROME in France or KLdB in Germany or CP in Italy. They are usually based on the mother of all classifications, the International Standard Classification of Occupations (ISCO) made by the International Labour Organisation around 1960, but they are not necessarily comparable – different numbers, letters and different taxonomy levels can differentiate the classifications.


Other classification systems were first and foremost developed for statistical reasons. Thus, it was possible to compose occupations with identification numbers into groups and then raise statistics, but these systems did not expand the understanding of the individual occupations. The group arrangements were often far too broad, too generic. For example, all medical specialists are grouped together and this group is described with only one set of skills for all specialists. This means that an oncologist is described as having exactly the same skills as a gastroenterologist, gynecologist or pathologist. So, according to the taxonomies, they have exactly the same knowledge, their specializations can only be recognized by their job title. With such inaccurate descriptions you certainly can’t understand individual job titles any better.

The EU did not want to build ESCO as another far too vague skeleton but rather create a common understanding of occupations, skills, knowledge and qualifications across 26 languages so that employers, employees and educational institutions better understand each other’s needs and requirements. In this way, freedom of movement could make up for skills gaps and unemployment in the different member states, as Juncker says¹.

Almost four years have now been worked since the trial version. All possible stakeholders were involved, such as employment offices, career advisors, statisticians, scientists… to create this classification in 26 languages. Almost four years of testing, extending, modifying, reworking… And now I’m sitting here on my PC, typing in the online database „Word“ as a skill and the database does not recognize the term. The only alternative suggestion: WordPress, not really related. If I type „PowerPoint“, there happens just nothing, the database does not recognize the term, it is not stored².

All right, let’s try Indeed. In Germany alone, I find more than 13000 job advertisements with the search term „PowerPoint“, in France and the United Kingdom about 8000 but PowerPoint is not classified as a skill across Europe. No place among 13485 skills in the ESCO. Should an employee understand a potential employer better in the sense that PowerPoint is not an important skill for employment?

Admittedly, the database does recognize „use microsoft office“ when „Microsoft“ is entered but the semantic understanding of the database does not go any further. After all, „use word processing software“ is even stored as a stand-alone skill with no connection to Microsoft Office, none of the two skills suggest themselves to be synonyms.

ESCO states that it recognizes 2942 occupations. It is interesting that the system identifies a «rail logistics coordinator», and also offers certain alternative spellings but not the logistician. Now and then occupations with similar illnesses are found. In addition, as an alternative term for a „political party agent“, «public relations agent» is suggested. Just to give you an example of a mistaken job title alternative.

ESCO will now be available in 26 languages. Yes and no, I’ll find out. Yes, the job titles are available in 26 languages, yes, the skills are also available. The explanation of a term is always in English, though, which means that a title can be translated into all languages but the job description not. It always remains in English. It is now questionable whether an employer from France understands the profession of his Swedish applicant better without a definition in his native language French. Or whether he understands if the classification really matches his vacancy.

Quite apart from the fact that qualifications are only available in one language: Greek. The detailed descriptions can only be found in this language. In any case, an employer in another member state will not understand his or her applicant better, even if he or she comes from Greece. ESCO itself reports that the qualifications have to be supplied by the member states and will be integrated from time to time. However, 27 Member States allowed quiet a lot of time themselves.

Now I have to sum up, I am more than just marginally disappointed. I have waited almost four years since I have been explaining the manifold possibilities of ontologies along with others at the ESCO Congress. But there was not build any ontology, rather a taxonomy or collection of terms. 2942 professions, 13485 skills and 672 (Greek) qualifications were integrated into ESCO. ESCO has apparently invested a great deal of time and probably a great deal of money in this development. But whether this is the breakthrough to Juncker’s goal is fundamentally questionable.

And the question is: What do we do now? Hope and wait another four years until ESCO might be able to meet the needs of HR and Public Employment Services? Or maybe rather look for an alternative? How about an alternative which represents a true ontology with semantic recognition? Which recognizes that a party employee does not do the same as a PR employee. Which knows that MS Word is the same skill as Microsoft Word or word processing. And which contains many languages completely. Who knows, maybe there is such a solution already. Perhaps an online research could be successful in this respect. For example on


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

[2] For this research only the online database of ESCO was used.