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 info@janzz.technology

 

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 JANZZ.technology.

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.

 

Sources:

¹ Cambridge Dictionary (2017). Creativity. Accessed from: http://dictionary.cambridge.org/dictionary/english/creativity [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. https://doi.org/10.1371/journal.pone.0071275

Sahoo, Anadi (2017). Knowledge, Experience & Creativity. Accessed from: https://www.linkedin.com/pulse/knowledge-experience-creativity-dr-anadi-sahoo/ [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.

janzz_esco_unsatisfied

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 http://janzz.technology.

 

[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.

Our Article on the „Google for Jobs“ Initiative Now Also Available in French, Spanish and Arabic!

Google-for-Jobs

The landscape of online job search has gained a significant addition with wide-ranging implications. In line with its recently announced initiative Google for Jobs, Google launched a new jobs search feature right on its search result pages that lets you search for jobs across virtually all of the major online job boards. Google’s new initiative not only has the potential to disrupt the online job search market, but the initiative’s underlying data model, an occupation ontology, may change the nature of job and candidate searches altogether.

Google’s advance into the domain of job search threatens many of the existing players. Not just because the feature will likely focus more user searches on Google’s own site but also because Google’s search quality will likely surpass other services due to the occupation ontology Google has built. Other online job services will have to carefully consider how to go ahead in face of Google’s move. Some can partner with Google. Others will have to look elsewhere for solutions. For the latter, we can offer an even more extensive ontology of occupations and skills.

Read the full article about Google for Jobs by clicking on one of the links below.

English Version: Google launches its ontology-powered jobs search engine. What now?

French Version: Google lance son moteur de recherche d’emploi par ontologie. Et alors?

Spanish Version: Google lanza su buscador de trabajo apoyado en la ontología. ¿Y ahora qué?

Arabic Version: Google launches its ontology-powered jobs search engine. What now? (Arabic Version)

Google Launches its Ontology-powered Jobs Search Engine. What Now?

google-jobs-api

This week, the landscape of online job search has gained a significant addition with wide-ranging implications. In line with its recently announced initiative “Google for Jobs”, Google launched a new jobs search feature right on its search result pages that lets you search for jobs across virtually all of the major online job boards. Google’s new initiative not only has the potential to disrupt the online job search market, but the initiative’s underlying data model, an occupation ontology, may change the nature of job and candidate searches altogether. We should know, as we have been working with occupation ontologies for the past 8 years.

Google’s advance into the domain of job search threatens many of the existing players. Not just because the feature will likely focus more user searches on Google’s own site but also because Google’s search quality will likely surpass other services due to the occupation ontology Google has built. Other online job services will have to carefully consider how to go ahead in face of Google’s move. Some can partner with Google. Others will have to look elsewhere for solutions. For the latter, we can offer an even more extensive ontology of occupations and skills.

Many people already start their job search on Google. But with the new feature, they will have a very different experience on Google from now on. Previously, Google search queries for jobs, such as “retail jobs” produced a list of links to websites like Indeed and ZipRecruiter. People would click on one of the top links and continue their search on their chosen site. However, Google’s new feature will keep significantly more search traffic on Google’s own site, as Google’s new feature will list single job postings in a box above the traditional web search results. The information will come from the websites of job search specialists like Glassdoor and LinkedIn, and directly from the career sections of many other company websites. Job seekers will click on the new listings and Google will show more information about the position. A „Read More“ button will take them to the job site or mobile app where the listing originated.

Job Board Woes
While Google initially partners with some of the biggest players in job search, including CareerBuilder, Monster, LinkedIn and Glassdoor, Google’s new initiative also injects great uncertainty into the business models of many players in the recruitment market. Most directly affected are job aggregators like Indeed. Chris Russell, a recruiting technology and job site consultant with RecTech Media, said to SHRM that “just like that, Indeed can no longer call itself the ‚Google for jobs‘.” Other recruiting technology experts go even further saying that “it may take another 10 years for Indeed to become an afterthought, but it’s fooling itself if it thinks this isn’t a DEFCON 1 moment” (SHRM). Indeed’s SEO traffic will certainly drop as Google takes over the top spots in search results, prized online real estate Indeed currently holds. Furthermore, companies may be encouraged to list fewer jobs on job boards, where they often have to pay, as Google picks up the listings directly from their career sites.

But that is just to sum up briefly a few ways in which Google’s new initiative may affect the status quo in online job search. There would be a lot of other things to consider and Google is of course also tied to many of the existing players from its other revenue streams, in particular its ad business – relations which Google may not want to upset. In any case, the more significant shift may lie in the underlying data model and approach Google has used to build its solution: the occupational ontology. While ontologies have been around for some time, they have never been used on a large scale. Google’s initiative has put occupation ontologies centre stage, which may finally alter the way companies and technology providers approach the problem of matching people and jobs.

The Problem of Matching the Right People and Jobs
The most challenging problem in business still is matching the right person to the right job. This has many reasons. First, many of the criteria that determine whether a position is right for a person, such as personality and lifestyle, are not embedded in job descriptions. Second, many job descriptions are limited, out of date and often poorly written. Third, employers each use different language to describe the same jobs. For any given job, there are hundreds of different job titles, which makes job or candidate searches often inaccurate and misleading. This has led to a mismatch on the job market: Employers say they still have issues filling open positions. Meanwhile, job seekers often do not know there is a job opening just around the corner from them because search engines have trouble detecting what job postings really mean.

Enter the Occupation Ontology.
This is where the occupation ontology comes in. An occupation ontology functions like a Rosetta Stone between job seeker and employers: it aggregates similar job titles, competences, educations and so on, and thereby helps understand the nuances of CVs and vacancies. An ontology aggregates similar job titles into families of jobs to build a truly useful, searchable, “universe” of jobs, organized by discipline and functional domain. By understanding the relationships between job content, competences, experience, and education, an occupation ontology helps deliver more relevant search results and recommendations.

occupation-ontology

An occupation ontology therefore offers maximal support both for job seekers and employers. When it is integrated into a job platform, it allows users to get the search results they are looking for without having to worry about their search criteria being too broad or too narrow. In contrast to keyword-based / Boolean search, an ontology based search will deliver results for things you did not explicitly search for but are related to your search criteria.

For instance, even a simple job such as “truck driver” appears in myriad of different wordings, depending on the company. FedEx Express calls drivers Couriers, FedEx Office calls them SameDay City Couriers and FedEx Freight calls them City Drivers. They each use different language to describe that very job. In a normal job search for “truck driver”, these jobs would not surface. However, an ontology knows that these jobs are highly similar and can include all of these and more in the results of a job search.

What Now?
Building an ontology is an enormous undertaking but it will benefit our economy and entire society if it is applied right. Google’s initial customers have been psyched with the successes they have achieved by using Google’s job search. Likewise, our customers have been surprised by the extensive improvements they were able to make with our knowledge graph (JANZZon!). Google has built an occupation ontology in English so far. In the past 8 years, we have built an occupation ontology in 8 languages that captures job universes from regions as diverse as the United States, Germany, Norway, and the Middle East. We have learnt that as the dimension of the ontology grows, both its complexity and value multiply.

Now, while Google’s job feature is a great step towards better job search quality, it is not a suitable solution for every player in the job market. Some, like Indeed are notable excluded from working with Google for competitive reasons, others, such as public employment services and HCM system providers may have greater needs for data security and customization, and finally, Google’s feature does only serve the English language so far. So where should companies look for solutions to keep in step with the rapid technological advancement?

Licensing an Ontology of their Own
Some companies have tried to build their own ontology but have failed to maintain it as a result of a shortage of specialist knowledge and insufficient resources or financial means. In recent years, many digital graveyards have emerged in the area of occupation data. The easiest solution is to license an occupation ontology as a cloud service. The occupation ontology JANZZon! offers this possibility and gives companies and public services the chance to connect to a wealth of knowledge about occupations and skills and to use it for their existing applications. JANZZon! is currently available in 9 languages (working on up to 40) and extends over all industries and job families. It is the most comprehensive ontology available today. And why not give someone else but Google a shot 😉?

Building a Job Matching Engine for the Global Labor Market

Understanding resumes and job ads, and finding the best matches among a great number of them is probably one of the most challenging tasks for machines today. The results and the precision of search and matching processes are dependent upon the scope and depth as well as the quality and comprehensiveness of the applied contextual and background knowledge.

Job postings are often worded in industry- and company-specific jargon that job seekers do not search for and would not use when writing their resumes. While resumes are often structured in a similar way, job postings can take pretty much any form and are often filled with information about the company and its culture rather than a specific list of requirements for the post to be filled. Furthermore, the labor market domain is inherently very complex to understand for machines. For example, data scientist is a profession and Hadoop is a technology that most data scientists need to know. Microsoft Excel and Powerpoint are both components of the MS Office Suite. A mechanic can work with cars or with industry machinery and an investigator can be a scientific researcher or someone investigating crime.

To understand the labor market domain thus, and to understand resumes and job ads, a lot of contextual and background is needed. Recruiters and job seekers already have this knowledge. Machines on the other hand need to be fed with this kind of knowledge so they can then apply it when parsing or matching job ads and resumes. The acquisition of such knowledge is beyond the scope of smart algorithms. Instead, semantic knowledge graphs are required, which represent this domain knowledge in digital form so it can be processed by a matching engine.

job matching engine

The idea that data not algorithms are crucial to developing human level artificial intelligence has been gaining momentum for some time. The use of knowledge graphs and ontologies in particular, has been referenced prominently when Google announced at the end of last year that they had built a knowledge graph of occupations and skills for powerful and precise job search and discovery with their Cloud Jobs API.

JANZZ’s semantic matching engine has the most comprehensive, multilingual knowledge graph in the area of occupations and skills at its disposal. Thus, when the matching engine JANZZsme! does a query expansion, searches or matches job ads and resumes, it accesses the ontology concepts, lexical terms and synonyms by which they may appear in CVs and vacancies in up to 40 languages, and the connections to related concepts. Thus, one of the essential parts in building a matching engine for the global labor market is building a knowledge base that covers the contextual knowledge required to understand the specificities of a labor market and its participants.

To find out more about the powerful application possibilities of our matching engine JANZZsme!, contact us for a demo anytime.

How an Ontology Can Help with Content-based Matching

Job and candidate search, job recommendations and automated candidate evaluations have one thing in common. They are a matching problem.

Simply put, given a set of CVs and a set of vacancies, the most similar items should match, that is, these items should come out at the top of the search, recommendation or evaluation. Most applications use either of two high-level approaches to achieve this: behavior-based or content-based. They each have pros and cons, and there are also ways to combine the approaches to take advantage of both techniques.

Matching_problem

Behavior-based approaches leverage user behavior to generate recommendations or suggestions. These approaches are domain agnostic, meaning the same algorithms that work on music or movies can be applied to the jobs domain. Behavior-based approaches do suffer from a cold start problem. If you have little user activity, it is much harder to generate good quality results.

Content-based approaches use data, such as user preferences and features of the items being matched or recommended, to determine the best matches. For recommending jobs, using keywords of the job description to match keywords in a user’s resume is one content-based approach. Using keywords in a job to find other similar jobs is another way to implement content-based recommendations.

However, the issue in this process is really the determination of similarity between two items. How can the similarity between for instance a resume and a vacancy be determined effectively even though they are often structured extremely heterogeneously? All too often, simple keyword-based matching is used for this, which means that many similarities go undetected, as keyword variations, synonyms and alternative phrases are not matched. With a content-based approach, it is important that the semantics (the underlying meaning) of two items be compared rather than the wording. This is where ontologies come into play. They can provide a relational model that can detect the underlying meanings and similarities in CVs and job descriptions. Ontologies enable a digital representation of implicit knowledge: humans usually understand the correct meaning of a term, thanks to their background knowledge and the context in which a specific term is used. A machine on the other hand lacks this ability. It can however, learn about the semantic meaning of a term by means of the concepts and relations stored in an ontology. By using an occupation and skills ontology as an intermediary, content-based approaches for job recommendations, job and candidate search and automated candidate evaluations can achieve much more.

The comprehensive ontology of occupations and skills JANZZon! for example offers a large number of poly-directional concepts pertaining to the global labor market. With its extraodrinary range of concepts, this ontology offers essential context and intelligent evaluation and enhancement options for applications such as information systems, matching engines, job portals, CV parsers, statistical analysis and modelling tools.

Industry Taxonomies Enhanced by JANZZ’s Occupation Ontology

At the heart of JANZZ.technology’s ontology of occupations and skills, there are over 35 taxonomies, among which occupation, skills and industry taxonomies like O*Net, ESCO, NAICS and ISCO-08. They are mapped by the JANZZ curation team to form a single entity that serves as a relational model for a great part of the world’s economic activity. As part of the latest additions to the occupational ontology JANZZon!, the curation team has inserted the two industry classifications GICS and ICB into the ontology, thereby extending the scope of the ontology as well as enhancing the intelligence of the two industry classifications.

GICS refers to the Global Industry Classification Standard and is a standardized classification system for equities developed jointly by Morgan Stanley Capital International (MSCI) and Standard & Poor’s. The GICS methodology is used by the MSCI indexes, which include domestic and international stocks, as well as by a large portion of the professional investment management community. The GICS hierarchy begins with 10 sectors and is followed by 24 industry groups, 67 industries and 147 sub-industries. Each stock that is classified will have a coding at all four of these levels.

ICB (Industry Classification Benchmark) is an industry classification taxonomy launched by Dow Jones and FTSE in 2005. It is used to segregate markets into sectors within the macro economy and categorizes over 290’000 securities worldwide. The ICB uses a system of 10 industries, partitioned into 18 supersectors, which are further divided into 41 sectors, which in turn contain 114 subsectors.

The two industry classifications allocate each company to the subsector that most closely represents the nature of its principal business activity. Thereby, the classifications allow a comparison of companies across national and linguistic boundaries.

Industry taxonomy enhanced by the ontology network

However, mapped to the semantic network of JANZZon!, the intelligence of the two classifications and their potential use multiply exponentially. As part of the semantic database JANZZon! the taxonomies are connected to a dense web of relations between occupations, skills, specializations and industries. The information on individual companies from the two taxonomies and the relational model of occupations, skills, specializations and industries are intertwined to form an even greater knowledge database. With the added knowledge about companies and how they relate to industry sectors, the ontology JANZZon! can serve its purpose even better, namely to provide an accurate relational model for parsing, matching benchmarking and classification.

Why leading employment services and software providers are betting on ontologies.

Algorithms are out, datasets are in. Perhaps one of the crucial findings in data science today is that datasets – not algorithms – might be the key limiting factor to developing human-level artificial intelligence. This contention is especially true in the case of solutions for the labor and recruitment market. Many companies in the recruitment market and public employment services are taking notice and are investing in the ontology-based solutions of JANZZ.technology.

Therefore, we have taken a brief moment to lay out the underlying reasons why datasets have become so important. And what qualities these datasets must have in order for businesses to take full advantage.

Over the past years, machine learning as well as deep learning and evidence based systems have achieved remarkable breakthroughs. These have in turn, driven performance improvements across AI components. Perhaps the two branches of machine learning that have contributed most to this are deep learning, particularly in perception tasks, and reinforcement learning, especially in decision making. Interestingly, these advancements have arguably been driven mostly by the exponential growth of high-quality annotated dataset, rather than algorithms. And the results are staggering: continuously better performance in increasingly complex tasks at often super-human levels.

Machine learning thrives on patterns. Unfortunately, our world is full of an almost limitless number of outliers. The labor market in particular is intrinsically a very tough market for automated solutions. Neither job titles, nor skills or educations are in any way standardized across the world or even in a particular country. There is a lot of company, culture and geography specific language involved in the description of jobs and qualifications. Furthermore, implicit phrases like “relevant education” or “relevant experience” are all too common, making job descriptions hard to decipher for machines. Algorithms – even sophisticated ones – have a hard time dealing with such an amount of heterogeneity, implicitness and inconsistency. When one adds the factor or language, it gets even more complicated. Currently, most algorithms struggle to deal with any languages other than English.

Datasets on the other hand, in particular annotated datasets of high quality, can reflect and understand the full bandwidth of the labor market vocabulary. They can also deal with cultural and geographical particularities. However, not all datasets are annotated and of a high quality. Most datasets that companies or public employment services have at their disposal are legacies of the past and therefore often messy, incomplete or inconsistent. Nevertheless, they want to leverage the power of data to improve their applications. Therefore, they need a way to enhance their data with standardized, intelligent meta-data.

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This is where ontologies come into play. An ontology formally represents knowledge as a hierarchy of concepts within a domain. Concepts are linked to each other through different relations. Through the relations that have been set and the location of the term in the ontology the meaning of a specific term becomes interpretable for a machine. An ontology is a dataset but it is of such high quality that it can also help improve the quality of other datasets.

JANZZ.technology focuses solely on the labor market and its ontology is the largest multilingual encyclopedic knowledge database in the area of occupation data, in particular jobs, job classifications, hard and soft skills and qualifications. The occupation and skills ontology can help companies and public employment services in many respects. It can serve as the basis of matching engines, parsing tools, natural language processing or classification tools, improving the results and learning of these tools significantly. More specifically, it can enhance job and candidate matching processes, CV parsing, benchmarking, statistical analyses and much more.

Positive that high data quality is going to create a competitive advantage for them, many stakeholders in the global labor market are currently investing in the solutions offered by JANZZ.technology. Above all its ontology. Data quality is becoming a focal point of competition in the digital labor and recruitment market.