The poison apple of “easy” skills data – are you ready to give up that sweet taste?

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This is the third in a series of posts about skills. If you haven’t already, read the other posts first:
Cutting through the BS and Sorry folks, but “Microsoft Office” is NOT a skill.

In the second post of this series, we discussed skills and the issues around defining and specifying them. Assuming we can reach some kind of common understanding of this valuable new currency, the next step is to find a way to generate meaningful skills and job data.

 

Shaky data – shaky results

Big data from online job platforms or professional networking sites can yield a wealth of information with a much higher granularity than the usual data gathered by national statistics offices in surveys – especially regarding skills. One reason is that, unlike printed advertisements, employers do not have to pay by space for online job postings and thus can provide more detailed information on the knowledge and skills they require. This online data also allows for a much larger sample to be monitored in real time, which can be highly valuable for analysts and policy makers to develop a timely and more detailed understanding of conditions and trends on the labor market.

However, when working with the data that is available online, such as online job advertisements (OJA) or professional profiles (e.g., LinkedIn profiles), we need to be clear on the fact that this data is neither complete nor representative and therefore any results must always be interpreted with caution. Not only because of the obvious fact that the results will be distorted, but more importantly because of the implications. Promoting certain skills based on distorted data can be harmful to the labor market: if workers focus on obtaining these skills – which by nature tend to be derived from data biased towards high-skilled professionals in sectors such as IT and other areas involving higher education – they are less likely to opt for career paths involving other skills that actually are in high demand, e.g., vocational careers in skilled trades, construction, healthcare, manufacturing, etc. Despite the fact that digitalization will primarily affect better educated workers with high wages in industrialized countries, simply because it is much easier to digitalize or automate at least some of the tasks in these jobs than those in many blue-collar and vocational occupations such as carpentry, care work, etc. The last thing any labor market policymaker would want is to accentuate the already critical skill gap in this area. Or create an even tighter labor market for certain professions, say, IT professionals [1]. Similarly, education providers seeking to align their curricula with market demand need reliable data so as not to amplify skill gaps instead of alleviating them. And yet, a growing number of PES are relying on this often shaky data for decision making and ALMP design.

For instance, there are several projects that aim to gather and analyze all available OJA from all possible sources in a given labor market and use these aggregated data to make recommendations including forecasts of future employability and skills demand. But the skills are typically processed and presented without any semantic context, which can be extremely misleading.

Challenges of OJA data

In 2018, the European statistical system’s ESSnet Big Data project issued a report [2] on the feasibility of using OJA data for official statistics. Their conclusion was: «the quality issues are such that it is not clear if these data could be integrated in a way that would enable them to meet the standards expected of official statistics.»

Let us take a look at some of the basic challenges of OJA data.

  1. Incomplete and biased: Not all job vacancies are advertised online. A significant proportion of positions are filled without being advertised at all (some say around 20%, others claim up to 85% of vacancies). Of those that are advertised, not all are posted online. CEDEFOP reported that in 2017 the share of vacancies published online in EU countries varies substantially, ranging from almost 100% in Estonia, Finland and Sweden down to under 50% in Denmark, Greece, and Romania. [3] In addition, some types of jobs are more likely to be advertised online than others. And large companies or those with a duty to publish vacancies are typically statistically overrepresented while small businesses, who often prefer other channels such as print media, word of mouth, or signs in shop windows, are underrepresented. Another relevant point is that certain markets are so dried out that advertising vacancies is just not worthwhile, and specialized headhunters are used instead. In summary, this means that OJA data not only fail to capture many job vacancies, but are also not representative of the overall job market. [4]
  2. Duplicates: In most countries, there is no single source of OJA data. Each country has numerous online job portals, some of which publish only original ads, others that republish ads from other sources, hybrid versions, specialized sites for certain sectors or career levels, etc. So, to ensure adequate coverage, OJA data generally need to be obtained from multiple sources. This inevitably leads to many duplicates, which must be dealt with effectively in order to reliably measure labor market trends in the real world. For instance, in a 2016 project the UK national statistics institute (NSI) reported duplicate percentages of 8-22% depending on the portal, and an overall duplication rate of 10%. [5] In the ESSnet Big Data project, the Swedish NSI identified 4-38% duplicates per portal and 10% in the merged data set [6].
  3. Inconsistent level of detail: Certain job postings provide much more explicit information on required skills than others, for instance depending on the sector (e.g., technical/IT) or country (e.g., due to legislation or cultural habits). Moreover, implicit information is recorded only to a limited extent and is statistically underrepresented, despite its high relevance. One reason for this is that US data providers often fail to recognize how uniquely detailed OJA are in the US, thus assuming that this is true everywhere and basing their methods on this assumption. However, this is far from correct. For instance, a job description like the one below, which is fairly typical in the US, will often be condensed to «carry out all painting work in the areas of maintenance, conversions and renovations; compliance with safety and quality regulations; minimum three years of experience or apprenticeship» in European countries. Moreover, in job ads like this, many of the required skills must be derived from the listed tasks or responsibilities. This shows just how important it is to extract implicit information.

 

The poison apple of “easy” skills data – are you ready to give up that sweet taste?

 

So, the question is, can these issues be dealt with in a way that can nonetheless generate meaningful data?

The answer: sort of. Limitations on representativeness can be addressed using various approaches. There is no one-size-fits-all solution, but depending on the available data and the specific labor market, statistically weighting the data according to the industry structure derived from labor force surveys could be promising; as could comparing findings from several data sources to perform robustness checks, or simply focusing on those segments of the market with less problematic coverage bias. [7]

Deduplication issues can be solved technically to a certain extent, and there is a lot of ongoing research in this area. Essentially, most methods entail matching common fields, comparing text content and then calculating a similarity metric to determine the likelihood that two job postings are duplicates. Some job search aggregators also attempt to remove duplicates themselves – with variable success. Identifying duplicates is fairly straightforward when OJAs contain backlinks to an original ad as these links will be identical. On the other hand, job ads that have been posted on multiple job boards pose more of a challenge. Thus, ideally, multiple robust quality assurance checks should be put in place, such as manual validation over smaller data sets.

Seriously underestimated: the challenge of skills extraction

The third challenge, the level of detail, seems to be the most underestimated. OJA from the US are typically much more detailed than elsewhere. A lot of information is set out explicitly that is only implicitly available in OJA data from the UK and other countries (e.g., covered by training requirements or work experience) – or not given at all. But even within the US, this can vary greatly.

 

The poison apple of “easy” skills data – are you ready to give up that sweet taste?

 

Clearly, even if we can resolve the issues concerning representativeness and duplicates, simply recording the explicit data will still result in highly unreliable nowcasts or forecasts. Instead, both the explicit and implicit data need to be extracted – together with their context. To reduce the distortions in the collected data, we need to map them accurately and semantically. This can be done with an extensive knowledge representation that includes not only skills or jobs but also education, work experiences, certifications, and more, as well as required levels and the complex relations between the various entities. In this way, we can capture more implicit skills hidden in stipulations about education, qualifications, and experience. In addition, the higher granularity of OJA data is only truly useful if the extracted skills are not clustered or generalized too much in subsequent processing, e.g., into terms like “project management”, “digital skills” or “healthcare” (see our previous post), due to working with overly simplified classifications or taxonomies instead of leveraging comprehensive ontologies with a high level of detail.

And then of course, there is the question of how to analyze the data. We will delve deeper into this in the next post, but for now, this much can be said: Even if we are able to set up the perfect system for extracting all relevant data from OJAs (and candidate profiles for that matter), we are still faced with the challenge of interpreting results – or even just asking the right questions. When it comes to labor market analyses, nowcasting and forecasting, e.g., of skills demand, combining OJA data with external data such as from surveys by NSI promises more robust results as the OJA data can be cross-checked and thus better calibrated, weighted and stratified. However, relevant and timely external data is extremely rare. And we might possibly be facing another issue. It is much easier and cheaper to up- or reskill jobseekers with, say, an online SEO course than with vocational or technical training in MIG/MAG welding. So maybe, just maybe, some of us are not that interested in the true skills demand…

 

[1] According to the 2020 Manpower Group survey, IT positions are high on the list of hardest-to-fill positions in the US, but not everywhere else. In some countries, including developed ones such as UK and Switzerland, IT professionals are not on the top 10 list at all.
[2] https://ec.europa.eu/eurostat/cros/sites/crosportal/files/SGA2_WP1_Deliverable_2_2_main_report_with_annexes_final.pdf
[3] The feasibility of using big data in anticipating and matching skills needs, Section 1.1, ILO, 2020 https://www.ilo.org/wcmsp5/groups/public/—ed_emp/—emp_ent/documents/publication/wcms_759330.pdf
[4] The ESSnet Big Data project also investigated coverage, for the detailed results see Annexes C and G in the 2018 report.
[5] https://ec.europa.eu/eurostat/cros/content/WP1_Sprint_2016_07_28-29_Virtual_Notes_en
[6] https://ec.europa.eu/eurostat/cros/sites/crosportal/files/WP1_Deliverable_1.3_Final_technical_report.pdf
[7] See for example Kureková et al.: Using online vacancies and web surveys to analyse the labour market: a methodological inquiry, IZA Journal of Labor Economics, 2015, https://izajole.springeropen.com/track/pdf/10.1186/s40172-015-0034-4.pdf

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

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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, Beautiful.ai, 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.
[3] https://www.onetcenter.org/content.html
[4] https://www.indeed.com/career-advice/career-development/what-are-skills
[5] http://www3.weforum.org/docs/WEF_Future_of_Jobs_2020.pdf
[6] Read the previous post for our view on Excel.
[7] http://data.europa.eu/esco/skill/f683ae1d-cb7c-4aa1-b9fe-205e1bd23535
[8] http://data.europa.eu/esco/skill/1973c966-f236-40c9-b2d4-5d71a89019be

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

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We are proud to announce that JANZZ.technology 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 JANZZ.technology, 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 info@janzz.technology to find out how we can transform your experience with our cutting-edge ontology-based solutions.

[1] Gartner Methodologies, “Gartner Hype Cycle,” 2020. https://www.gartner.com/en/research/methodologies/gartner-hype-cycle
[2] For a better understanding of the fundamental difference between ontologies and taxonomies, read our post: https://janzz.technology/ontology-and-taxonomy-stop-comparing-things-that-are-incomparable/
[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.

JANZZ’s job matching platform ParaEmpleo singled out by the IDB as an AI success story

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The IDB is an important driver of artificial intelligence as a tool to address challenges in the labor markets in Latin America and the Caribbean (LAC). As part of the IDB’s fAIr LAC initiative studies, they recently published an interesting report on how to utilize AI for labor intermediation in public employment services (PES). This technical paper provides an overview of key aspects considered by PES when deciding to adopt AI for their operations, as well as discussing the benefits and risks of implementing AI-based solutions for PES.

JANZZ is excited to share that its project ParaEmpleo – a semantic job matching platform realized in collaboration with Paraguay’s Ministry of Labor, Employment and Social Security (MTESS) – is described as a “success story in incorporating AI”. It is so far the only project of its kind in LAC, and JANZZ.technology is eager to continue implementing solutions for PES that use AI for social good, generating better social services and giving people more perspective.

You can read the IDB report Artificial Intelligence for Job Seeking : How to Enhance Labor Intermediation in Public Employment Services here.

JANZZ.technology listed as one of the best tech startups in Zurich

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We are proud to share that Seedtable has selected JANZZ as one of Zurich’s 96 best tech startups to watch in 2020.

JANZZ has been busy developing and implementing semantic technology for job matching and CV parsing, making progress in leaps and bounds this year. Our products and solutions are now deployed by several public employment services around the world, as well as by large private corporations, and are endorsed by organizations such as World Bank, IDB and ILO. We have also been increasing our team size and can now offer our solutions in over 40 languages.

Seedtable is a weekly newsletter on European tech read by over 12,000 founders, investors and operators every week. It ranks startups across Europe based on their founders’ qualities, growth rate, funding and scalability potential.

Thank you to Seedtable for an inspiring start to the new year!

JANZZ.technology in a recent ILO report: From big data to smart data

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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, JANZZ.technology 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

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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, JANZZ.technology 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 sales@janzz.technology

 

[1] WEF. 2018. Global Gender Gap Report 2018. URL: http://reports.weforum.org/global-gender-gap-report-2018/data-explorer/

[2] WEF. 2020. Global Gender Gap Report 2020. URL: https://reports.weforum.org/global-gender-gap-report-2020/the-global-gender-gap-index-2020/results-and-analysis/

[3] Kazuhiko Hori. 2020. Japan gov’t to push back 30% target for women in leadership positions by up to 10 years. URL: https://mainichi.jp/english/articles/20200626/p2a/00m/0fp/014000c

[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: https://www.carf.e.u-tokyo.ac.jp/admin/wp-content/uploads/2020/07/F490.pdf

[5] OECD. 2019. Directorate of Employment, Labour and Social Affairs. URL: https://www.oecd.org/els/soc/LMF_1_6_Gender_differences_in_employment_outcomes.pdf

 

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

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Anyone using online job-matching services has undoubtedly encountered seemingly obvious or even ridiculous mismatches. Many of these mismatches are based on inadequate processing of the information related to education. For instance, when a job matching algorithm focuses only on the level of education (e.g. Bachelor or Master or high school), a travel agent may be suggested a job as an IT specialist. They both have a degree, but – crucially – in a different context. In addition, recently completed lower level courses or certificates become more significant as they complement work experience, in many cases gradually rendering the highest level of education obsolete. But these matching algorithms still match jobseekers with 20 years of work experience primarily based on a university degree dating back 20 years instead of their more recent and more relevant further training.

To address these challenges, JANZZ.technology has created the concept of Education Zones, which are clusters of educations related to a given professional field such as “Tourism” or “Computer Science”. This way of recombining degrees, training and other education at various levels and from different fields of education provides a much more realistic representation of professional fields, and when used in a matching algorithm, can generate significantly better matching results between a candidate’s education and a job’s requirements.

Education Zones are most beneficial when a job description contains popular generic phrases like “has an educational background in …”. At the same time they are well-suited to help matching algorithms more accurately capture the growing number of candidates with non-linear education paths by generating a more precise profile of the candidate’s specific knowledge, competences and skills. JANZZ’s Education Zones provide a more effective categorization of educations according to professional fields, creating the basis for sounder, more accurate matches. To find out more about Education Zones, read our white paper:

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

Creative Associates International reported on UbicaNica.jobs

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Creative Associates International reported on UbicaNica.jobs – our AI powered and un-biased job placement platform in Nicaragua. Ayan Kishore, Director at Creative’s Development Lab said in the article, “Nicaragua doesn’t really have many opportunities for somebody to go online and find a job, so if you’re in Nicaragua, you’re looking at newspaper or depending on word of mouth. That’s just not how people are finding opportunities in the rest of the world.” Thanks to the technology behind UbicaNica.jobs delivered by JANZZ and the other key leads on the project, it will be a real game-changer for the country’s job seekers, especially the youth. JANZZ.technology is keen to continue contributing to projects that use AI for social good and adopt ethical and responsible principles, thus generating better social services in more regions.

Click here to read the article.

New AI job platform to bring together all the skills of Nicaragua and boost the economy

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Zurich, Switzerland/Washington, DC/Managua, Nicaragua, October 2020 – JANZZ.technology, Creative Associates International, the SDC (Swiss Agency for Development and Cooperation) and Fundación COSEP (Superior Council of Private Enterprise) will soon launch Ubicanica.jobs, an AI-driven platform for Nicaragua designed to strengthen the search for jobs and talents, improve employability and boost the country’s economy.

In the adverse economic environment Nicaragua is currently faced with, and which has become even more precarious in recent months, many Nicaraguans lack information, perspective and visibility—especially the youth of Nicaragua, many of whom are denied opportunities to develop critical skills needed for employment, and access to quality training, job placement services and jobs.

The development organization Creative is tackling these issues through the Aprendo y Emprendo project financed by USAID and the SDC. This initiative aims at strengthening the private Technical Vocational Education and Training (TVET) system in order to provide the youth of Nicaragua with vocational skills, life skills, work readiness skills, and soft skills training that will help them become capable employees and entrepreneurs. As part of this project, Creative partnered up with Fundación COSEP, a foundation of the leading business chamber in Nicaragua, and Swiss tech firm JANZZ.technology to develop Ubicanica.jobs, which contributes by providing visibility, career counseling and job placement services.

“Our objective was to create a unique platform that will bring together all the skills and work opportunities of Nicaragua, matching precisely the right people to the right jobs,” says Stefan Winzenried, CEO of JANZZ.technology. “We really hope that as many people and companies as possible use the platform. The potential of having all job offers, job seekers and everything that is involved in the labor market on a single platform is massive: it will improve employment opportunities for everyone on every level.”

Unlike Nicaragua’s existing platforms, job searches on Ubicanica.jobs are not based on keywords. Instead, the user posts a profile of their individual skills and competences, which the system then analyzes using artificial intelligence and compares with the requirements of potential job offers. The technology behind it processes job-related data through deep learning algorithms and knowledge graphs to identify the best possible matches between jobs and candidates and make accurate suggestions to both job seekers and recruiters. The platform can also show users how to improve their chances in the labor market. The system analyzes which jobs and skills are currently in demand and based on the user’s profile, it recommends courses and training to improve employability.

To help mitigate the effects of the current situation in Nicaragua, the platform needed to be up and running as quickly and cost-effectively as possible. It was thus implemented as a white label product—in just 90 days—and is now operated by Fundación COSEP as a SaaS solution together with JANZZ.technology. COSEP is convinced that utilizing the innovative approach of Ubicanica.jobs will drive the social and economic development of the country. “Employment and entrepreneurship are key to a society’s wellbeing and we all have something to offer,” says one of their leading officials. “Through Ubicanica.jobs we can boost Nicaragua’s economy and strengthen our society by better matching the country’s talents and needs. If we all sign up and use the platform, it’ll be a real game-changer.”