Sorry folks, but “Microsoft Office” is NOT a skill.

One of the most prominent buzzwords around employment, employability and workforce management is skills. There is a lot of noise surrounding this concept and its fellow buzzwords like reskilling, upskilling, skills matching, skills alignment, skill gaps, skills anticipation, skills prediction, and so on. One can find myriad publications and posts explaining why skills are so important, how to analyze skills supply and demand, how to develop active labor market policies based on skills, how to manage and develop employee skills – as well as the many sites listing the “most in-demand skills” of the year. We certainly agree that skills are increasingly important, or as stated in one of the Gartner Hype Cycles 2020,

Skills are […] the new currency for talent. They are a foundational element for managing the workforce within any industry. Improved and automated skills detection and assessment allows for significantly greater organizational agility. In times of uncertainty, or when competition is fierce, organizations with better skills data can adapt more quickly […]. This improves productivity and avoids costs through improved planning cycles. [1]  

This applies not only to HCM in businesses, but also to labor market management by government institutions. Considering how globally important these concepts are, there should be a clear or at least common idea of what this valuable currency is. However, in the much of the skills-related content posted online, there is a pervasive pattern of conceptual ambiguity, lack of specificity and lack of concision. So, in the last post, where we discussed a few examples of the noise surrounding jobs and skills, we called for a more fact-based discussion. In this post, we want to lay the groundwork for such a discussion.

Statistics 101

As a reminder from the last post: whenever you try to generalize, you run the risk of losing relevance. Despite all the globalism going on, the world is divided into regions. And each region has its own distinct economic landscape and its individual skills demand. Some regions are more focused on certain industries than others, and even when comparing regions with similar industries, skills demand and gaps can vary significantly, as has been shown in various studies and reports (for example here and here). So there will never be a meaningful list of top skills on a global level. Problem solving skills, blockchain, app development and other “top skills” propagated on various websites are simply not relevant for all activities across the globe. On top of this, it is extremely challenging to generate meaningful, representative data from online profiles and job postings. In general, the data collected online is biased, certain groups are underrepresented, others massively overrepresented. For instance, despite all the noise about apparently all-important, accelerating “digital skills”, most representative surveys highlight that EU and US labor markets require a generally low to moderate level of digital skills, with about 55 to 60 percent of jobs doing simple word processing or data entry and emailing. 10–15 percent need no ICT skills. Only about 10–15 percent need an advanced ICT level. [2] This alone shows that all these publications about the most important skills of the future etc. are at best very misleading.

To perform sound analyses and anticipate the skills that will be required in the future, to predict how these requirements will change (which skills will gain in importance and which skills will become obsolete or to perform target-oriented skills matching, we first need to be able to correctly recognize, understand, assign, and classify today’s skills. We will discuss the challenges (and strengths!) of skills and job data more in detail in the next post. First, we need to focus on an even more basic, but absolutely crucial aspect: we need to clarify what we mean by skills. Or abilities and competencies.

Truth be told, there are so many different definitions floating around, it is quite hard to keep up, and this is one of the key reasons why most approaches and big data evaluations fail miserably. It is therefore all the more important that we agree on a common understanding of this new currency.

What exactly is a skill?

O*NET defines skills as developed capacities that facilitate learning, the more rapid acquisition of knowledge or performance of activities that occur across jobs, [3] and distinguishes skills from abilities, knowledge and technology skills and tools, referring only to directly job-related or transferable skills and knowledge. ESCO, on the other hand, defines a skill as the ability to apply knowledge and use know-how to complete tasks and solve problems. Moreover, ESCO only knows the two main categories skills and competences, which – unlike O*NET – also include attitudes and values. In both classification systems, there is significant overlap between the categories. Indeed, on the other hand, just summarizes all these concepts under the term skill:

Skill is a term that encompasses the knowledge, competencies and abilities to perform operational tasks. Skills are developed through life and work experiences and they can also be learned through study. [4]

Clearly, these discrepancies in the definition of a skill will cause discrepancies in data collection and analysis, which in turn will affect the robustness of any extrapolation based on these data. But, for sake of argument, let us assume there is a universal definition of a skill. In a nutshell, we shall think of a skill as some kind of ability that is useful in a job.

Analyzing generic skills yields generic answers

Just having a written definition of a skill is far from enough. Apart from the fact that it still leaves a lot of room for interpretation, we also have many issues at the level of individual skills. One issue is the granularity, which differs extremely among the various collections. For instance, the ESCO taxonomy currently includes around 13,500 skills concepts, O*NET under 9,000 (in fact, only 121 of these are not skills of the type “can use a certain tool/machine/software/technology”) and our ontology JANZZon! over 1,000,000. Of course, the desired level of detail depends on the context. But for many modern applications of skills analysis, such as skill-based job matching, career guidance, etc., a certain level of detail is crucial to achieve meaningful results. Take the list of “top 10 skills for 2025” published by the World Economic Forum [5]:

  1. Analytical thinking and innovation
  2. Active learning and learning strategies
  3. Complex problem-solving
  4. Critical thinking and analysis
  5. Creativity, originality and initiative
  6. Leadership and social influence
  7. Technology use, monitoring and control
  8. Technology design and programming
  9. Resilience, stress tolerance and flexibility
  10. Reasoning, problem-solving and ideation

Depending on the context, e.g., industry or activity, these skills are understood very differently. They are thus too generic or unspecific to be of any use in matching or for meaningful statistics. In fact, for many occupations they are barely relevant at all. Or how often do you see these skills in job postings? Other generic skills we often see in predictive top 10 lists and recommendations have similar issues, for instance:

Digital skills: What exactly are these skills? Does this include operating digital devices such as smartphones or computers or dealing with the Internet? Do we expect someone with these skills to be able to post on social media, or really know how to handle social media accounts professionally? Is there any sense in summarizing skills such as knowledge of complex building information modelling applications in real estate drafting and planning under digital skills?

Project management skills: This too is almost completely useless when taken out of context like this. A large proportion of workers has project management skills on some level, but it is very difficult to compare or categorize this knowledge across roles or industries. For example, the individual project management knowledge differs substantially between a foreperson on a large tunnel construction site, a project manager for a small-scale IT application, a campaign manager in the public sector and a process engineer or event manager. Clearly, if the event industry comes to a halt, a project manager cannot just switch to the construction industry. So, it is nonsensical to comprise all these variations into a single “matchable” skill.

JANZZsme! Semantic Precision for Skills/Competence Matching

Think multidimensional

Being precise about skills does not just entail clearly identifying the skill and its context, the level of capability is equally relevant. The level of English required of a laborer on a construction site is certainly not the same as that required of a translator. However, construing a robust definition of levels also poses challenges: What does “good” or “very good” knowledge mean, and what distinguishes an “expert” in a certain skill? Is it theoretically acquired knowledge, for example, or is it knowledge already applied in a real professional environment? In contrast to other areas of big data, scales and validations – if they exist – are not necessarily binding. Thus many providers of this type of data just resort to disregarding levels entirely. In doing so, we lose a huge amount of information which would be highly relevant, not only for job matching and career guidance, but also in analyzing skills demand, say, as a basis for workforce or labor market management. Do we have a shortage of highly skilled experts or of employees with a basic working knowledge? Clearly, appropriate measures will differ strongly depending on the answer.

Say what you mean

Granularity in terms of identifying context and level of a skill are certainly important. The main issue, however, is clarity. One of the recurring top 10 skills required in job postings almost anywhere on the planet is almost always listed as Microsoft Office, which at a first glance may seem fairly specific. But what does this really mean? Technically, MS Office is a family of software, available in various packages comprising a varying selection of applications, which evolve over time. Currently, it consists of 9 applications: Word, Excel [6], PowerPoint, OneNote, Outlook, Publisher, Access, InfoPath and Skype for Business. So, if someone “has MS Office skills”, does this mean they can use all those apps? Hardly. And what does it mean to be able to use an app? According to ESCO, someone who can “use Microsoft Office” can

work with the standard programs contained in Microsoft Office at a capable level. Create a document and do basic formatting, insert page breaks, create headers or footers, and insert graphics, create automatically generated tables of contents and merge form letters from a database of addresses (usually in Excel). Create auto-calculating spreadsheets, create images, and sort and filter data tables. [7]

Many people may think they can use MS Office – until they read that definition. It seems that the less one knows about the full potential of an application, the more likely one is to identify as a capable user. This becomes even more apparent when we consider PowerPoint, which, surprisingly, is not included in ESCO’s “use Microsoft Office” skill. Instead, this is called ‘use presentation software’. There are thousands of applications to create presentations, many of which work quite differently to PowerPoint and thus require different knowledge or additional skills: Prezi, Perspective, Powtoon, Zoho Show, Apple Keynote, Slidebean,, just to name a few. And yet, the skill of “using presentation software” is just vaguely described in ESCO as:

“Use software tools to create digital presentations which combine various elements, such as graphs, images, text and other multimedia.” [8]

Putting aside the fact that there are many instances of presentation software, if this is a skill, in the sense of an ability that is useful in a job, then one should expect “creating presentations” imply es that the person can create usable or even good presentations. Amongst many skills, this includes the ability to distill information into key points, as well as a sense of aesthetics and storytelling skills. Yet, with enough self-confidence, a person lacking these implicit skills may still think that they are capable of creating great presentations.

And apart from this, what an employer means when they ask for these skills varies substantially. Someone looking for an office help in an old-school micro business may have a very different idea of MS Office skills than a large corporation looking for a marketing specialist. When it comes down to it, trying to interpret the expression “Microsoft Office” as a skill results in so much guesswork, that the informative value of “Microsoft Office skills” becomes comparable to that of “hammering skills”. Everyone can use a hammer, but does that mean anyone can work in any profession that involves hammers? Of course not.

JANZZsme! Occupations That Involve Hammers

My Math teacher used to say: If you mean something else, say something else. That could be a good place to start.

(Self-)assessment vs. reality

As mentioned above, many people’s self-image deviates from reality, resulting in under- or overestimating their skills (hammering, creating presentations or any other skill). In addition, there is the issue that completing a course or education that should teach a set of skills does not automatically mean that we have that skill set, i.e. that we can apply it productively in a job. Also, many unused skills have an expiration date. And yet, once we get used to listing a certain skill on our resume, we rarely take it off again, no matter how long we haven’t used it. Just asking ourselves the question can I apply this productively in my job? could go a long way in moving our projected image closer to reality. If we wanted to. Just as agreeing on a definition of a skill, standardizing skill designations and levels or just being plain more specific and accurate could give us a clearer common understanding of this valuable currency. If we wanted to. And then we can turn to the challenges of generating smart data – which we will investigate in the next post.


[1] Poitevin, H., “Hype Cycle for Human Capital Management Technology, 2020”, Gartner. 2020.
[2] Thanks to Konstantinos Pouliakas at Cedefop for pointing this out.

[6] Read the previous post for our view on Excel.


Cutting through the BS

Adaptability and flexibility, digital skills, creativity and innovation, emotional intelligence… Since the pandemic went global, everyone has been talking about the top post-COVID skills employees will need. Going through numerous posts from Forbes over Randstad to EURES, it seems that the key point they have in common is that they are untransparent if not completely unfounded. Despite all the noise they generate, none of these posts give any insight into what data their claims are based on – or whether they have any data at all. Here at JANZZ, we have been analyzing over 500,000 job postings from the last few months for a project in Australia and New Zealand. In this data, just as in any of our other data from similar projects in completely different markets and regions of the world, there is no indication of increased demand for creativity and innovation or for digital skills – which, by the way, should not include the ability to participate in a video call, just as Excel usage does not turn an economist into a STEM profession. The skills that were most in demand across all professions from waiters to senior policy officers were in fact ambition, self-motivation, and ability to work under pressure, independently and in fast changing environments.

But it is not just about skills analysis. When it comes to… well, anything related to jobs and skills, there is an unbelievable amount of BS out there. Here are just a few examples.

Future jobs. According to the WEF’s Future of Jobs Survey 2020, among the top 20 job roles in increasing and decreasing demand across industries, Mechanics and Machinery Repairers are listed as both increasing (#18) and decreasing (#9). The same is true for Business services and administration managers (up #12, down #6). This apparent contradiction is simply stated with no explanation in the text. And yet, this information is just reproduced blindly in numerous blogs and posts. [1]

LinkedIn skills reports. The same is true for all the buzz generated by LinkedIn reports on in-demand skills. Countless articles and posts just reproduce these lists, all completely disregarding the fact that they are based on the data captured in LinkedIn profiles [2], which is strongly biased. For instance, blue-collar professions and industries are massively underrepresented in their data. By contrast, according to the ManpowerGroup Talent Shortage surveys, for 7 consecutive years, skilled trades have been hardest to fill, globally and nationally in almost all countries, along with drivers (especially truck/heavy goods, delivery/courier and construction drivers), manufacturers (production & machine operators), construction laborers and healthcare professionals on this year’s list. Shouldn’t the skills associated with these professions be in higher demand than blockchain or cloud computing?

Skills demand. A Canadian institute created a report based on data and skills taxonomies from a large labor market analytics provider. They introduce the report with the statement “Telling Canadians they need digital skills is not enough; we must be specific.” The report then goes on to identify the top 10 digital skills by number of job postings. Among the top 10 skills are Microsoft Excel and Spreadsheets. There is nothing specific about these “skills”. First off, the term “Microsoft Excel” says absolutely nothing about the skills that are actually needed. Is the candidate expected to just be able to open the application and enter data? Or should they be capable of creating formulas? How complex are the formulas supposed to be? What about charts? Also, what exactly is the difference between the two skills Microsoft Excel and Spreadsheets?

Upskilling. Within a business, upskilling can be very useful. An individual company is fairly fixed in its position and should have a clear strategy which will also largely determine the skill needs of the company and thus, the upskilling strategies. However, developing sustainable upskilling strategies as part of an active labor market policy (ALMP) is a very different challenge. Contrary to what many posts and tech providers say, just upskilling all the unemployed will not lower unemployment numbers sustainably and does not necessarily meet market demands. For instance, in a country where a lot of low-skilled work is on offer, upskilling a jobseeker who is already overqualified will not be of any use. Or if the training offers are of poor quality or not aligned to market needs. This is the reality in many countries.

Job matching. A Dutch tech provider for Employment Services claims that its software solutions can help PES “reduce unemployment figures”. As if that could happen by just using the right job matching tool. For instance, this article (in Italian) in Italy’s renowned newspaper Corriere della Sera illustrates just a few of the issues that need to be resolved before, or at least while, implementing a software solutions for the PES: there are currently 730K job vacancies in Italy, compared with 2.5M active jobseekers plus 13.5M inactive and discouraged. The skills of jobseekers in Italy are not aligned with labor market demand. Training, particularly for the unemployed, is inadequate, of poor quality and disconnected from market needs. PES have insufficient and not adequately trained staff. Italy invests extremely little in ALMPs. They have made plans to increase this budget but have no strategy on how to spend the additional funds. A change of direction would require a vision that does not expire after the next elections, which is an extremely high ask given Italy’s political landscape and history. And yet, the Dutch tech provider still argues that their job portal solutions will make the crucial difference.

Most of what is out there is basically gut feelings and creative marketing. So how about cutting through the BS and finding our way back to an honest, fact-based discussion? Well, to do that, we need to find out what the facts are. But for that, we first need to agree on the basics (for instance, define what constitutes a skill) and then generate reliable data based on these definitions. More about this in the next post…


[1] If you are interested in learning more about the issues surrounding predictive analyses based on occupational data (e.g. skills anticipation), read our CEOs talk featured in a recent ILO report.

[2] According to LinkedIn: The most in-demand skills were determined by looking at skills that are in high demand relative to their supply. Demand is measured by identifying the skills listed on the LinkedIn profiles of people who are getting hired at the highest rates.

JANZZ named as a Sample Vendor for Skills Ontologies in Gartner Hype Cycle for HCM Tech 2020

We are proud to announce that has been identified by Gartner as a Sample Vendor of Skills Ontologies in the Hype Cycle for Human Capital Management Technology 2020. This recognition validates the innovative approach of our solutions for businesses and public employment services based on our unique multilingual job and skills ontology.

What is the Gartner Hype Cycle?

“Gartner Hype Cycles provide a graphic representation of the maturity and adoption of technologies and applications, and how they are potentially relevant to solving real business problems and exploiting new opportunities. Gartner Hype Cycle methodology gives you a view of how a technology or application will evolve over time, providing a sound source of insight to manage its deployment within the context of your specific business goals.[1]

JANZZ named as a Sample Vendor for Skills Ontologies in Gartner Hype Cycle for HCM Tech 2020

Skills Ontologies, rated as highly beneficial for HCM, are currently in the first of five stages in the Gartner Hype Cycle: the Innovation Trigger. Gartner describes this stage is the one where “a potential technology breakthrough kicks things off. Early proof-of-concept stories and media interest trigger significant publicity. Often no usable products exist and commercial viability is unproven.” We started developing our skills ontology over a decade ago, in 2009. It is now the most comprehensive multilingual skills ontology worldwide and has proven itself repeatedly over the past few years, being successfully deployed in multiple systems of any scale.

What is a Skills Ontology?

A skills ontology organizes large collections of concepts concerning capabilities, competencies, knowledge, and experience, as well as the relations between them in a data structure. It provides a basis for AI applications in areas such as talent acquisition, talent development and workforce planning. Numerous providers claim that they have an ontology when in reality, they only have a taxonomy or a library.[2] At JANZZ, we have a true ontology, JANZZon!. But it does not just include skills, it is a job and skills ontology. This means that it also encompasses occupations, job titles, work experience, training and qualifications, industries and much more. Matching skills alone without taking other information like occupations/roles into account can result in extremely inaccurate results. A retail cashier and a retail pharmacist will have skills in common, e.g., customer service skills, but their key skills, namely their specialist knowledge and their qualifications, differ dramatically. So even if all other listed skills are a match, it would be completely nonsensical to suggest a cashier for a pharmacist position. Context is essential, and one of the key types of information generated by our job and skills ontology.

Moreover, unlike other skills ontologies on the market, JANZZon! distinguishes levels of skills and their context. For instance, the level of skills required in a junior position are not the same as for a senior specialist, and the skill set of a project manager in application development is not identical to that of a project manager in interior design. These differences are represented in our job and skills ontology JANZZon! and are one of the driving factors in the astonishing accuracy of our job-candidate matching and career pathing tools.

Watch our video about the JANZZ ontology

Why not just stick with skills libraries and taxonomies?

Skills or job libraries, which many technology providers still rely on, are primarily built by experts (often psychologists) analyzing and classifying skills and skills levels related to job categories or functions. These methods are labor intensive and limited, often focusing on cross-functional skills or on a limited number of job-specific technical skills. Moreover, in the fast-changing world of work, these libraries are almost always outdated as soon as they are finalized.

The key issue with these libraries, however, is that there is no such thing as a standard skill profile for a given occupation. This means that search and matching results based on skills libraries are mostly disappointing at best. On the other hand, with the right skills ontology, you get a continuously updated, comprehensive database that provides the basis for technology that “transforms user expectations for relevance of job searches, matching of candidates to job roles, and recommendation of learning content.”[3]

The crucial advantage of a skills ontology compared with skills libraries or taxonomies is that it links synonyms as well as similar and related skills. This dramatically improves search and matching by translating the diverse vocabularies of different stakeholders, job postings and candidate/employee profiles into a common language and giving search terms context. As a result, classical keyword-based approaches can be replaced by semantic search where the system understands the meaning of search terms as opposed to stubbornly comparing strings of characters.[4] For instance, when entering the term CEO, the ontology-based system will exclude results like Assistant to the CEO. Or, upon entering the term Mechanic, it will suggest more precise terms like Auto Mechanic or Boat Mechanic. And the best people for the job can be identified much more accurately – without the noise of unsuitable candidates or the risk of top candidates slipping through the cracks.

Moreover, our ontology-based systems can recognize implicit skills in occupations ranging from Sign Painter to Cybersecurity Project Manager and use these skills to provide more satisfactory results – not only of jobs and candidates, but also in profiling, gap analyses and career pathing. The contextual knowledge stored in our skills ontology is also key to our highly performant job and CV parser.

Pioneering solutions in HCM tech

Most of the current ontology-based HCM applications on the market are still quite rough around the edges and there is no one-size-fits-all solution. Instead, a combination of models and approaches is needed. Here at, we already have a well-established skills ontology as well as highly accurate technology for semantic search and match, gap analyses, profiling, and job and CV parsing. However, we are driven to continuously improve and extend our solutions and thus very actively engaged in R&D, ceaselessly developing pioneering technology to tackle new challenges. Our mission is to help improve the HCM experience by providing efficient and highly performant solutions without compromise.

And why are we so far ahead of the Gartner Hype Cycle? Because we started in 2008, long before anyone was talking about AI and knowledge representations, long before Google and the markets realized that advanced AI solutions will simply be impossible without ontologies. That is why we have a head start of several years today.

Take advantage of this and integrate our job and skills ontology into your applications via our simple APIs. Contact us at to find out how we can transform your experience with our cutting-edge ontology-based solutions.

[1] Gartner Methodologies, “Gartner Hype Cycle,” 2020.
[2] For a better understanding of the fundamental difference between ontologies and taxonomies, read our post:
[3] Poitevin, H., “Hype Cycle for Human Capital Management Technology, 2020”, Gartner. 2020.
[4] For more information on this topic, request a copy of our white paper «Keyword vs. ontology based, semantic matching» via email or contact form. in a recent ILO report: From big data to smart data

Big data and AI still have a hard time today in gaining traction in the field of HR and employment services due to the poor quality and lack of explanatory power in the data. As JANZZ explains in a recent ILO report, any predictive analysis based on big data and determined by a large number of variables is rather inaccurate. The longer the time horizon and more variables included, the less likely such prediction is going to be completely or even partially close to reality.

Hence, any recommendations for market participants such as forecasts of the future employability and required skills of job seekers will generate little or no significant results if based on approaches that simply compile and evaluate all available job advertisements from all available sources in a market over a period of years. Because the skills are often presented and processed without any relevant semantic context, for example, the typical forecasts of general “top skills” as published regularly by LinkedIn and the World Economic Forum. One will find the skills listed are too generic or general to be used in matching, indeed, they are barely relevant for many occupations.

From the very beginning, has determined to form big data into smart data using a structured and fully semantic ontological approach and over the years, it has repeatedly proven to be the only game-changer. To learn more, please find the full article in the ILO report:

The feasibility of using big data in anticipating and matching skills needs

Global Labor Market Insights: More quality jobs are needed for female part-timers

In previous articles (The silver workforce, The world’s most homogeneous society is opening new doors) we have talked about the aging and shrinking working-age population in Japan and how Japan is taking measures to deal with the implications of this demographic issue, including extending retirement ages and welcoming migrant workers. This article focuses on another of Japan’s options to boost workforce: the large number of women that are held back or excluded from the labor market.

The idea that women should stay at home as primary caregivers is deeply seated in Japan. A 2016 poll revealed that this view is still held by 45% of men surveyed. When Shinzo Abe came to power in 2012, he and his government unveiled a comprehensive policy package, known as “Abenomics”, to revive the Japanese economy, of which “womenomics” – the plan to create a “Japan in which women can shine” – has been a key element. “Womenomics” aims to redress Japan’s ingrained gender inequality and to solve the labor shortages by encouraging more women to participate in the job market.

The Abe government passed legislation to extend parental leave and eliminate a tax deduction for dependent spouses. They also ensured rapid expansion of childcare facilities for working mothers including free and affordable childcare for low-income families. They have worked intensively with Japan’s business associations to increase hiring, promoting and empowering women, targeting 30% women in leadership positions by 2020.

How effectively has the program been carried out so far? According to last year’s report by the International Labor Organization, the proportion of Japanese women in management and other leadership positions was 12% in 2018, falling far short of the 30% target and well below the 27.1% global percentage. In the World Economic Forum’s annual Global Gender Gap Index from the same year [1], Japan ranked 110 out of 149 countries, barely moving up from the year before. In the 2020 report, Japan slid down to rank 121 [2]. Faced with disappointing numbers, the Japanese government has had to push its target date 2020 as far back as 2030. [3]

Indeed, although the female labor participation rate reached 71% following the initiative of “womenomics”, outperforming the EU and US, critics claim this policy approach has been no more than surface shine. Multiple sources indicate the disproportional representation of Japanese women in part-time and non-regular positions. The Global Gender Gap report 2020 shows that more than a third of female employees hold these positions, compared with just 11.5% of male employees.

When part-time work began to emerge and expand in the 1970s, it was regarded as a manifestation of a more flexible and non-standard labor market. Compared to full-time jobs, they are ideal for working parents to combine work with family responsibilities. They can enable older people to prolong their work life and people with health issues to remain in the labor market. However, on average, many part-time jobs are of poorer quality: they are disproportionately concentrated in the lower-paid professions with poorer working conditions and less job security. In the case of Japan, economists at MIT and University of Tokyo found that 69% of female Japanese workers are active in sectors such as retail or food and accommodation, where traditional female-dominated service jobs are offered [4]. The activities they perform are strongly associated with the informal sector and have the least regulatory protection; for many higher-paid and managerial positions, one can hardly find part-time opportunities.

Not only in Japan, but around the globe, part-time work is largely performed by women with family responsibilities. According to data from the OECD [5], the Netherlands have the highest rate of female part-time employees, with 58% in 2018. Switzerland, Australia, Ireland, UK and Germany are also among the top. However, even in these countries there are still many barriers that hinder the development of part-time employment into an option that truly ensures equal opportunities. This is not only the case in Japan, it is a phenomenon encountered across the globe.

Part-time employment is a proven means to increase the female participation rate in the labor market, contributing to a more flexible and productive workforce. For policy makers, it is important to ensure that wider measures are put in place to enhance the quality of this work, promoting part-time positions and job sharing in areas with better pay, better working conditions and higher job security, as well as actual career opportunities in part-time; and more generally, to design policies in a way that promotes gender equality.

For almost a decade, has been observing and working with many labor markets worldwide. We offer our know-how and the right data on skills and specializations to tackle general challenges in job market. If you are interested in leveraging our data and experience, please write now to


[1] WEF. 2018. Global Gender Gap Report 2018. URL:

[2] WEF. 2020. Global Gender Gap Report 2020. URL:

[3] Kazuhiko Hori. 2020. Japan gov’t to push back 30% target for women in leadership positions by up to 10 years. URL:

[4] Shinnosuke Kikuchi, Sagiri Kitao and Minamo Mikoshiba. 2020. Who Suffers from the COVID-19 Shocks? Labor Market Heterogeneity and Welfare Consequences in Japan. URL:

[5] OECD. 2019. Directorate of Employment, Labour and Social Affairs. URL:


Education Zones – Bridging the Gap Between Candidate Education and Employer Requirements in Online Job Matching

 » Read more about: Education Zones – Bridging the Gap Between Candidate Education and Employer Requirements in Online Job Matching  »

Equal employment opportunity, starting with anonymous application procedures

To many tourists, Switzerland is often considered as one of the most beautiful countries they have ever visited. However, if they decided to move to this country, would they still feel the same? Would that love at first sight last when they try to land a job?

High salary is one of the major incentives for foreign workers to seek opportunities in Switzerland. Statistics from the Federal Statistical Office (FSO) show that the number of foreign workers commuting daily to Switzerland from neighboring countries is on the rise, reaching 332,177 in 2020 [1]. The FSO also reported that by the end of 2019, the number of foreign nationals actively participating in the Swiss labor market has increased to 1.6 million [2], leaving roughly 1.3 million migrant workers—around a quarter of the nation’s total employed population.

However, compared to Swiss citizens, employment rates among migrant workers are much lower. A recent study indicates that, during their arrival year, the employment rate of migrant men is around 16% lower than that of comparable men born in Switzerland, and for migrant women, it is 37% below the employment rate of women born in Switzerland. Of course, the gap will gradually narrow, but after five years, their employment rates are still below those of workers born in Switzerland: by 4% for migrant men and by a full 13% for migrant women [3].

There are many reasons why migrant workers experience a disadvantage in a host country’s labor market. One major aspect is compatible skills, including both human and social capital. Compared with native-born residents, new migrant workers are often at a disadvantage in terms of these skills in the host country. They are generally less familiar with local customs and less likely to have recognized occupational training or certification. They also lack information about labor market opportunities, for instance local networks that can be useful in a job search, or employer expectations. Fortunately, these disadvantages decrease the longer they stay in the host country [4].

Local language is another country-specific skill and fluency in the main language of the host country is an important determinant of success in the labor market. Switzerland is a multilingual country with four official languages: German, French, Italian and Romansh. According to our estimates based on over 10 years of experience in occupation data analysis, the average number of languages asked by employers in Switzerland is 2–2.5: one or two local languages combined with English. For native speakers of one of the local languages, this constitutes knowledge of one or two foreign languages—both of which are part of the standard curriculum at Swiss schools. For migrant workers, at least for the around 60% who are not native speakers of one of these four languages, these requirements are slightly different.

When comparing the skills and qualification levels of workers between Swiss and foreign nationals, the proportion of foreign nationals with higher education is comparable to that of Swiss nationals. At the other end of the spectrum, the proportion of low-skilled workers is much higher than for Swiss citizens. For workers in these two groups, language requirements can be quite low. For example, English is mostly irrelevant for blue-collar workers such as builders and janitors, and non-native speakers are often only required to have basic command of a local language. On the other hand, local languages are not necessarily essential for workers in international corporations, universities, and other international institutions and organizations, where English is commonplace.

However, migrant workers with intermediate skills and qualification levels, compared with local workers at the same level, are at a disadvantage in terms of language competitiveness when applying for jobs, because many positions at this level require proficiency of both a local language and several other languages. There is a much higher proportion of Swiss workers in this range, and it also happens to be where most vacancies are found in Switzerland.

Apart from potentially lower human and social capital, discrimination is still a reasonable explanation for the differences in labor market outcomes between locals and migrants. In a recent meta-analysis of 43 experimental studies across 25 years on discrimination in hiring decisions, researchers determined that “discrimination of ethnic and racial minority groups in hiring decisions is still commonplace.” [5]

In an experiment which tested HR managers’ discrimination against candidates with non-Swiss background, researchers found that candidates with certain foreign-sounding names who “whitened” their CVs and indicated fluency in only the local language were better received than those who convey a cultural attachment to their country of origin. They concluded that “CVs that convey multiple signals of attachment to one’s culture of origin are heavily sanctioned by assessments of lower productivity [6].” Children of immigrants who have Swiss qualifications and dual nationality must send out 30% more applications to receive a call-back for an interview when applying for apprenticeship level positions [7].

Ensuring equal employment opportunities for migrant and immigrant workers in the labor market is beneficial both to these workers as well as the host society. Access to the labor market increases their social participation, which is essential to integration. Meanwhile, paid work reduces their dependence on social welfare. At, we believe that anonymized procedures at the very beginning of the job application process can largely reduce discrimination and improve equal opportunities. To learn more about’ anonymized procedures, please contact




[1] FSO. 2020. Foreign cross-border commuters by gender, canton of work and age class. URL:

[2] FSO. 2020. Employed persons (domestic concept) total number and in full-time equivalents by gender and nationality, gross and seasonally adjusted values. Quarterly and yearly averages. URL:

[3] Favre, S.; Föllmi, R:; Zweimüller, J.: Immigration, return migration and integration from a labour market perspective. In: A Panorama of Swiss Society 2020 Migration-Integration-Participation, Federal Statistics Office, Neuchãtel, 2020

[4] Friedberg, R.: You can’t take it with you? Immigrant assimilation and the portability of human capital, Journal of Labor Economics 18:2: 221–252, 2000

[5] Zschirnt, E.; Ruedin, D.: Ethnic discrimination in hiring decisions: A meta-analysis of correspondence tests 1990–2015, Journal of Ethnic and Migration Studies, Taylor & Francis, Milton Park, Abingdon, Vol. 42, Iss. 7, pp. 1–19, 2016

[6] Auer, D.: Drivers of immigrant employment in Switzerland, University of Lausanne, 2018

[7] Fossati, F.; Liechti, F.; Auer, D.; Bonoli, G.: Discrimination Multipliers, How immigrants’ integration affects labour market disadvantage, MIM Working Paper Series 17:2, Malmö Institute for Studies of Migration, Diversity and Welfare (MIM) Malmö University, Malmö, 2017, a white-label solution with anonymized application procedures

Reports [1],[2] have shown that an anonymized procedure at the very beginning of the application process can largely reduce bias and significantly improve equal opportunities. Here at, we have been building the architecture of our solutions around the anonymized process since 2010. is a white-label solution designed to avoid bias during the first steps of the application process by concealing users’ personal data. Two profiles are created to separate personal information and job-related information. The job-related profile contains all information that is relevant to the matching process such as occupation, skills, soft skills, education, experience, availability, salary and so on. This profile is accessible from the start. The personal profile containing information such as name, gender, nationality, date of birth, marital status, portrait picture etc. is only shared upon user approval and is not used for job matching.

In addition to this anonymized procedure,’ unique semantic matching engine, powered by JANZZ’ key technologies, searches and matches jobs and candidates based on the similarity of terms, i.e. synonyms and other relations – as opposed to mere keyword comparison. It also compares the degree of skills, identifies cryptic occupational terms through context, performs gap analyses and more. (To learn about semantic matching please check out our previous article – providing semantic technologies powered by ontology )

This white-label solution is currently serving several Public Employment Services (PES) across the globe. Our customers choose because

  • the platform is scalable with state-of-the-art modular components which can meet the varied requirements of PES of all sizes,
  • the process to establish such a platform is fast, easy and cost-effective, and it is especially an ideal solution for PES that need to build from scratch,
  • the solution has been tested and built with years of experience from many other customer PES around the world, and it has proved to be stable, reliable and efficient,
  • the SaaS solution saves PES from running a designated IT department. Instead, they can trust the professional team at JANZZ to manage the databases and can automatically benefit from the updates and upgrades.


User story: How a country in Central America built its job-search system in no time

Due to adverse economic environment our customer, a country in Central America, is struggling with increasing unemployment rates – especially among the youth. We collaborated on a local project designed to strengthen the country’s private Technical Vocational Education and Training (TVET) system and to equip the local youth with the skills they need to successfully enter the labor market. Our mission was to create a modern platform that pools all the talent and work opportunities of the country and successfully matches people and jobs.

In just 90 days, the platform was implemented as a white-label product and is now operated as a SaaS solution to help our customer

  • implement a job-matching platform from scratch in a fast and cost-effective way,
  • boost its economy and strengthen its society by providing higher visibility and better matching for the country’s talents and jobs,
  • assist local educational institutions investigating and comparing labor market demand with graduate profiles to better align their curricula with market needs,
  • provide access to information on local TVET centers, training and scholarships, tips for interviews and CVs etc. to improve users’ chances in the labor market,
  • introduce an anonymized application matching system to improve equal opportunities for its citizens when applying for jobs.


Like PES, many small to medium sized recruiting agencies are faced with tight resources and the need for an automated solution to perform superior candidate search and matching in order to focus more on a small pool of candidates qualified for the vacancies. Therefore, we also receive requests from recruiting agencies. These agencies are the leading recruiting firms in specific branches, specialized in C-level positions for global clients. With JANZZ’s innovative technology and fully customizable solutions can be tailored to the specific needs of small to medium sized recruiting agencies and help them take their business to the next level. They will benefit from

  • being fast to market by launching a modern and powerful platform to boost the business,
  • fully personalized brand experience with business logos, colors and email templates,
  • traffic-based cost generator to maximize revenue,
  • real-time job matching in over 40 languages, and much more.


User Story: Recruiting agency in healthcare, medtech and pharmaceuticals looks for solutions to better support its clients

One of our customers is a recruiting agency specialized in the life science and healthcare sector in Europe. It consists of a small team with less than 10 people. Typically, the talent acquisition managers spend 50% of their already limited time on searching and analyzing CVs. They feel they could deliver more suitable candidates to their clients by reallocating some of this time to conducting in-depth interviews with a smaller number of qualified candidates. For this reason, our customer was interested in a recruiting platform to locate appropriate skilled personnel quickly and efficiently.

By using, our customer has sustainably reduced the processing time of their acquisition managers. Meanwhile, thanks to the anonymized procedure, which largely protects the privacy of their clients, the quality of suitable applications has increased through

  • significantly more precise, multilingual skills matching between applicants and vacancies in comparison with traditional job platforms,
  • increased transparency for all applicants as to why an application has been rejected,
  • improved skills-based active sourcing; vacancies are only visible and open to contact for suitable candidates.


To learn more about JANZZ’s anonymized procedures and building your branded hiring solutions, please contact



[1] Ines Böschen, Dr.Ramona Alt, Annabelle Krause, Dr. Ulf Rinne and Prof. Dr. Klaus F. Zimmermann. 2012. Pilot project ‘Depersonalised application procedures’. URL:

[2] Eva Heinimann and Ralf Margreiter. 2008. Anonyme Bewerbung: Ein Zürcher Pilotprojekt für mehr Chancengleichheit und innovative Lehrlingsselektion. URL:

Free movement of skilled workers in the EU and beyond are more important than ever

Do you know how to build a house? If not, maybe you should consider learning it right now. In 2030 there is a high chance that we will have a critical shortage of construction workers. This is your opportunity to pick up the skills such as installing electrical wiring, carpentry, blockwork, waterproofing and plumbing.

According to the 2020 report by the Schweizerischer Baumeisterverband (Swiss Building Association)[1], the Swiss construction industry is facing massive problems: fewer and fewer young people are willing to learn a trade in construction, and many of the current skilled workers are about to retire. Compared to 2010, 40% less young people started a bricklayer apprenticeship in 2019. This will have a critical impact on the entire industry because the majority of construction foremen, supervisors, and construction managers are recruited from the bricklayer pool. On the other hand, the proportion of people aged over 50 in the main construction industry is up to 36%. The expected outcome of this combination is alarming.

In recent years, the level of professional qualifications on construction sites has risen sharply due to digitalization, leaving the typical unskilled worker now barely in demand and creating a skill gap between supply and demand of workforce in this sector. However, the drivers of skills shortages are much more complex.

Demographic trends

Lower birth rates and increasing life expectancy are the two key demographic trends in Switzerland and the European Union. An ageing population paired with a shrinking working-age population have intensified the situation in the labor market. According to data from Eurostat [2], the share of working-age population in the total EU population decreased by more than 2% from 2010 to 2018 and the median age increased by almost three years to 43.1 during the same period.

On top of this, the impact of baby boomers heading into retirement has yet to fully unfold. A study by Credit Suisse [3] states that a total number of around 1.1m people will reach retirement age in the next ten years and the younger generation will not be able to fill the vast number of highly skilled jobs vacated by this post-war baby boom generation. Studies from other Central European countries reveal similar expectancies concerning the impact of baby boomers on their labor markets.

However, multiple reports have indicated a shortage of skilled workers in the construction sector for countries that do not have this demographic shape, such as South Africa and India. Apparently, a more general factor is causing the skill shortage in construction on an international level.

Negative image

Many studies from around the globe show that the construction industry has a negative image, especially among the youth. For instance, in a study conducted in a high school by the national business employment weekly in South Africa, careers in construction came in at number 247 out of 250 potentially attractive career options [4]. CITB data in 2013 [5] found that in the UK, the overall appeal of the construction industry as a career option had fallen to only 4.2 out of 10 among adolescents aged 14 to 19, and the Union of Construction, Allied Trades and Technicians (UCATT) also reported a 14.6% fall in construction apprenticeship numbers in 2013. This was confirmed again in a 2017 poll. In the US, only 3% of youth aged 18-25 wanted to work in the construction trades in a 2017 survey [6].

Despite the bright prospects of a career in an in-demand field, the youth just can’t seem to get past the industry’s bad image: low wages and job security, health and safety concerns, low-quality work, and tough working conditions are the aspects that the youth most associate with jobs in construction and deter them from pursuing a career in this sector. Moreover, the rise of digitalization feeds a false fear that jobs in construction will fall victim to automation in the future.

Indeed, some occupations in construction have a higher potential for automation, but technically, it is not feasible to perform certain tasks with robots. Even physical work, which many assume most likely to be automated, can be too challenging for robots – especially physical tasks in unpredictable and changing environments.

Moreover, when referring to professions in construction, people mostly think only of on-site activities, often overlooking the professions related to sub-sectors such as real estate, procurement, architectural or engineering activities, which enjoy a higher social status and could be very attractive to both genders.

 Growing interest in the academic route

In the dual education system practiced most notably in Germany, Austria, and Switzerland (DACH), most career paths in the construction sector start with vocational training and apprenticeship. A long-standing tradition in these countries, the dual system was well received by parents, adolescents, and society at large. Lately, an increasing interest in university degrees has posed a direct challenge for the professions in the construction sector and for other skilled trades.

By the time university graduates are faced with making a career choice, it is far too late to attract them to the construction site. Research shows that timing is key for sparking interest in construction professions, and the highest success rates are reported when engaging school children at the primary level. Accomplishing this will require joint efforts to bring about fundamental changes to the education system and foster deeper collaboration between industry and educational institutions.

Indispensable free movement of skilled workers

This is certainly a promising strategy for the long term and future workforce generations. In the short and midterm, however, national economies – particularly in industrialized and industrializing countries – will highly depend on flows of skilled workers among countries all over the world to alleviate the workforce shortages. Indeed, as noted by the ILO [7], migrants are a vital source of skills and labor for industries such as construction. By nature, construction requires flexible teams with a variety of skills, and is thus often deeply dependent upon migrant workers due to their mobility and flexibility. Therefore, solutions contributing to the free movement of persons will rapidly grow in demand. However, to ensure true free movement of workers, an important issue needs to be addressed from the start: the recognition of qualifications among different countries and even regions.

Challenges in skills recognition

In the EU, the Professional Qualifications Directive (PQD), the European Qualifications Framework (EQF), and the European Credit System for Vocational Education and Training (ECVET) serve as the main instruments for the recognition of professional and academic qualifications to support workers’ mobility across Europe. However, despite significant efforts such as the launch of the European Skills, Competences, Qualifications and Occupations (ESCO) classification system, skills recognition and matching among the member countries are still very disappointing, not to mention internationally. Comparing skills across borders poses two significant challenges: first, many countries have their own classification systems or taxonomies, which each need to be mapped into a transnational system. But not all have already effectuated this. Second, not all skills and qualifications translate easily. For instance, a carpenter who completed an 18-month apprenticeship in the UK will have a different skill set than a carpenter with a three-year apprenticeship in Austria, even though both completed standardized training for the same skilled trade.

For this reason, more and more investments and expectations are placed on ontologies and semantic technologies as the solution to overcome these challenges. Currently, JANZZ’s ontology is the largest multilingual encyclopedic knowledge representation in the field of occupation data. Not only can it translate the many linguistic variations in occupational jargon into a common vocabulary, but more powerfully, by human expert curating, it can truly compare differences and similarities in education and qualifications across borders and language divides – thus delivering a meaningful contribution towards true free movement of skilled workers.

Contact us now to understand how JANZZ’s ontology can assist you in education and qualification recognition for your workforce.



[1] Zahlen und Fakten 2020, Schweizerischer Baumeisterverband SBV

[2] European Commission, Improving the human capital basis, Analytical Report (March 2020)

[3] Credit Suisse, Fear of recession exaggerated (September 2019)

[4] Makhene, D., Thwala, W.: Skilled labour shortages in construction contractors: a literature review

[5] UK Construction: An Economic Analysis of the Sector (July 2013)

[6] Young Adults & the Construction Trades, NAHB Economics and Housing Policy Group

[7] Buckley et al.: Migrant work and employment in the construction sector, Geneva: ILO, 2016

Hiring and managing a changing workforce

Remote working, temporary contracts and project-based employment have been shaping the traditional jobs, causing governments and corporations to rethink job offerings in short-term arrangements. has been following the trends, shaping the future of work and collaborating with many labour markets worldwide. We are active in semantic parsing, searching and matching within occupation domain. Our technology can realize data-driven HR decision-making, speed up people analytics and use workforce data for business performance prediction. If you are looking for the right skilled employees or want to identify learning gaps to meet future business needs, contact us and we will explain to you how our products, consulting services and innovative solutions can help you.