Perhaps, this time, “working from anywhere” is here to stay

When people were still debating the future of flexible working and how technology and digitalization are going to change how we work, after COVID-19, that future has arrived much sooner than anticipated. Gartner stated in its report that 48% of employees will likely work remotely compared to 30% before the pandemic. [1]

How has the workplace changed over time?

Back in the days, the workplace was much less tech-oriented, and business conversations were conducted through landlines or in person. Documents and files were all hard copies. Employees were expected to work independently, and cubicles were present in the office. Since the 1950’s, major changes took place and the emergence of teamwork, computers, the internet, and business-oriented software has had a tremendous impact on how we work.

Over the past 20 years, one of the most significant workplace changes has been the technical transformation to a digital workplace. The workplace has undergone even greater revolutions such as telecommuting, zoom, co-working space, and flexible working. The elimination of fixed workplaces first appeared in Silicon Valley technology companies known as flexible working or the upgrade of activity-based working. Basically, employees want more freedom during work and their homes or even a café are gradually becoming their second “office”.

Remote work, who is ready or, more precisely, who is capable?

In an article published by Harvard Business Review, the robustness of digital services, internet infrastructure, and digital payment options were used to evaluate 42 significant global economies in terms of the readiness for remote work. [2] In the survey we can see that countries such as Singapore, UK, US, Netherlands, Norway, Canada, and Germany are placed in a better position while countries like India, Indonesia, Thailand, Chile, Philippines, and China are shown less prepared. However, the readiness of technology is only the external factor triggering the practice of remote work, and it can be influenced with the effort of country leaders by investing in infrastructure and technology.

The internal factor, thus the structure of different industries within a country is the key here. In countries where the main economic activities fall into the agriculture and manufacturing sectors, it is unlikely for its workforce to work remotely because farmers and blue-collar workers simply cannot work from home. On the other hand, countries whose service sectors produce a majority of their economic value, have a workforce, or more precisely a knowledge workforce, that is not attached to the workplace anymore. According to the knowledge economic index (KEI) from the World Bank Institute, countries such as Denmark, Sweden, Finland, Netherlands, Norway, Canada, and Switzerland, are among the top. [3]

The downsides of remote work

COVID-19 forced companies around the globe to practice full-time flexible working and some countries, where flexible working was not common, needed to adapt to the new situation quickly. For such countries, it is also important to understand the negative sides of flexible working when handled unproperly.

When the time spent in the office is no longer the key factor for being paid, flexible working must have a new pay-related appraisal criterion, hence performance is taken as the new measurement. In order to justify their efficiency and be seen as high-achieving and trustworthy, employees often agree to higher target agreements. This can lead to so-called “interested self-endangerment” causing the detriment of health. Therefore, companies should not ask too much of their employees or else they might risk the utility of flexible working. [4]

Digitalization shifts the boundaries between free time and work allowing people to spread their work across the whole day so they can better combine work and life. However, in a survey conducted in Germany, half of those surveyed said that digitalization increases the intensity of their work. They feel more stressed and their workload has piled up, and they also feel subjected to stricter supervision. [4]

The challenge in the gig economy era

Flexibility in jobs is no doubt one of the key features in the gig economy which is transforming our labor market drastically. The work today is changing towards being more cognitively complex, technology-dependent, collaborative, mobile, and border-crossing. This presents a huge challenge for governments and corporations today to match the right skills and qualified people with jobs and to identify the education and learning gaps to meet future business needs.

For almost a decade, JANZZ.technology has been following the trends shaping the future of work and working with many labor markets worldwide. We are active in semantic parsing, searching, and matching within occupation domains. Our technology can realize data-driven HR decision-making, speed up people analytics, and use workforce data for business performance prediction. To find out more about what we can do please contact sales@janzz.technology

 

 

 

[1] Gartner. 2020. 9 Future of Work Trends Post-COVID-19. URL: https://www.gartner.com/smarterwithgartner/9-future-of-work-trends-post-covid-19/

[2] Bhaskar Chakravorti and Ravi Shankar Chaturvedi. 2020. Which Countries Were (And Weren’t) Ready for Remote Work? URL: https://hbr.org/2020/04/which-countries-were-and-werent-ready-for-remote-work

[3] Wikipedia. Knowledge Economic Index. URL: https://en.wikipedia.org/wiki/Knowledge_Economic_Index

[4] UZH Magazin. 2018. Interview: “Working flat out”. URL: https://www.magazin.uzh.ch/en/issues/magazin-18-4/gesundarbeiten.html

Workers age 50+: ready for the scrap heap or worth their weight in gold?

Systematic discrimination against workers age 50+ in candidate selection – or why this is not the issue in the vast majority of cases.

Read on, the following article will not just repeat the same arguments as always on this particularly important topic, which are usually based on assumptions and politically deadlocked positions. We will provide you with new, statistically relevant and number-based arguments that allow you to take a different view on the challenges of older workers and, to the same extent, on our education policy. But let us start at the beginning.

The statistics bring it home…

Suppose we want to fill a new position. We already have 80 suitable applications and CVs, including ones from young professionals, experienced professionals and applicants age 50+. Let the selection process begin. We sort by relevant skills and competences, professional experience, education and training, language skills, specializations, industry knowledge and so on. We reduce first to five, then to three candidates, who we invite to an interview. Importantly, we hide all personal data during selection, or rather, we make a non-discriminatory first selection using XAI.

At the end of this not so fictitious example and after many long, personal interviews and assessments, we choose a 27-year-old, multilingual university graduate with almost three years of experience in the right industry and the best matching scores in the areas of hard skills/competences and soft skills, communication skills, appearance, etc. A surprising choice? Hardly. It is rather the logical result of a structured, transparent and above all fair selection procedure. Remember, the hiring HR professionals were not aware of age, gender or salary expectations for the first steps of the selection. It would have been more of a surprise if one of the 50+ candidates had won the race – for statistical reasons alone: in the total of 80 applications there were only seven more or less suitable 50+ candidates, i.e., less than 10%.

Imagine that a 54-year-old, less qualified candidate had been chosen, contrary to the robust findings of the structured selection process and results of the interviews, primarily because of his age. This would have been just as discriminatory as an inherent preference of male candidates or favoring the candidate with the necessary connections.

Let us explain in more detail why this choice is logical and fair, and why other, similar selection processes usually have just as little to do with age discrimination or with the argument that companies avoid recruiting 50+ candidates for financial reasons.

There are always better qualified candidates out there. No matter how good yours are.

Well-trained, enthusiastic and experienced engineer, 50 plus, seeking – this is a scenario that has become bitter reality for many older workers in recent years. In our example, by the way, six of the 80 applicants were ahead of the 50+ candidate, having even better qualifications, mostly just recently acquired or refreshed, and higher degrees. There was only one criterion, ‘relevant experience’, on which he came in fourth, just narrowly missing the interviews for the final selection round. In short, the candidate was not unsuitable or rejected just because he was over 50, there were simply better-suited and objectively better qualified candidates for the position.

By the way, according to some of the more serious statistics, jobseekers in many industries in Switzerland already start facing more difficulties when they reach their mid-40s. At this age, the chances of finding a suitable job fall significantly in more and more cases. Despite an exceedingly positive economic environment and stable labor market with an exceptionally low unemployment rate before Covid-19, even highly qualified older workers were concerned about potential, longer-lasting unemployment. The fact is that once the older generation have lost their job, it is difficult for them to find a new, equivalent position. This is primarily because, usually for the first time in many years, they will have to face up to the ever increasing, ever better educated, multilingual competition and keep pace with younger, highly motivated and equally ambitious applicants.

To be very clear at this point: The sad exceptions do exist, companies with a real prevailing ‘anti-50+ policy’. Such a policy makes no sense at all, economically or otherwise, but there have always been companies that could neither calculate nor had a reasonable and fair personnel strategy. However, the real reasons why people in their 50s are increasingly faced with unemployment are complex and found on both the employer and employee side.

Last relevant vocational training: commercial apprenticeship 1981

The current labor market is becoming more and more specialized and is exposed to ever-faster technological change in many sectors, not only because of advancing digitalization. There are several reliable surveys that consistently show that, by the age of 30, more than 60% of knowledge acquired up to that point is already outdated or no longer relevant for professional progress.

In recent years, digital technologies, channels and thus evolved processes have come to the fore, rendering tasks more demanding and complex, especially for older workers. For example, compare the top 20 required skills from 2008 and 2018, say, on LinkedIn or in similar surveys. The ongoing transformations and the digitalization of competitive skills are quite dramatic.

This is just one of the reasons why training is being invested in everywhere and more than ever. That is a good thing, we all fought for this privilege for a long time and have repeatedly stressed the importance of good, modern education for every economy. Access to education as affordable as possible for all. A whole variety of tailormade educational models, dual education, vocational baccalaureate, semester abroad, MBA, CAS and much more. Comparing the manifold possibilities of today’s educational landscape, not only in Switzerland, with the options that were available at the time of our 50-year-old engineer, there have been huge, mostly positive developments – consistently and in all areas and aspects that are key to a successful professional life. Moreover, ambitious young professionals will gladly pay, or rather invest, 60000 US dollars and a few months of their lives for an MBA or tens of thousands of francs for challenging advanced training, certifications or postgraduate studies in order to have a better chance in the competition, which is becoming tougher for younger workers as well. We must all be continuously willing to make such investments, including the time commitment and renunciation of family life and leisure time ensued. Lifelong learning and continuous training are more than just buzzwords.

To keep up with a constantly evolving labor market, it is absolutely necessary to continuously train and extend our skills and competences, on average every 5 to 7 years. Work experience is certainly valuable, but that value is diminishing in more and more areas because the businesses they are based on are often outdated after a few years or have disappeared completely from the market. The ever-accelerating cycles of innovation in basic processes, tools and market and production mechanisms render the by far largest asset of experienced workers increasingly obsolete in comparison with younger, often better trained co-applicants.

The problem for the 50+ generation is that their good education was completed many years ago. Their knowledge, should they need to transition from a familiar and well-known environment to a new field of work, is thus no longer up to date.

Also, many 50+ applicants list only a few, if any, current training courses on their CV. For instance, a TOEFL test from 1993 may be the last entry under ‘languages and communication’, hidden among an abundance of in-house courses and trainings with lavish certificates of little meaning or relevance to a new position. This can be confirmed statistically by parsing and carefully evaluating large quantities (several million) of anonymized CVs: on average, the last relevant qualified formal training was completed 11.2 years earlier for 50+ candidates in Switzerland. In cases of successful professional reorientation or re-entry into a profession, it was several years less. As a reminder, the iPhone as the first actual ‘smart phone’ was launched almost exactly 11 years ago. Several other, significant digital processes and tools have followed since – at ever-shorter intervals.

In such cases, companies cannot be blamed for not considering a 50+ applicant for the simple reason that younger applicants are statistically in the majority and are moreover better qualified or have more up-to-date competency profiles. It would therefore be particularly important for older jobseekers to continuously adapt their strengths and qualities to technological change (whether we like it, want it or not…) and to consider continuous, targeted further training or even reorientation. Self-commitment is called for and this is not the responsibility of employers.

Know-how and relevant competence profiles beat experience.

Another reason why older applicants’ dossiers often end up on the rejection pile is the number of years of service. Applicants who have worked in the same department, company and industry for 20 years have specific work experience but often lose touch with the rapidly changing professional world outside the company. However, this long-term, one-dimensional experience is not the main obstacle in itself: it is often more likely the fact that the applicants’ profile is strongly tailored to their former employer and their qualifications are too limited or they are too specialized, having spent many years in the same function with similar tasks. As a result, flexibility and new professional opportunities are often deemed difficult. A new employer would need to invest in thorough onboarding and possibly in retraining. Of course, this may be necessary for a younger applicant as well. However, it may greatly devalue the importance of acquired professional experience in the competition with other candidates. Even if relevant work experience is still very important in general, its importance has diminished in a fast-moving and even faster changing economy. Ten years of experience are no longer twice as good and meaningful as five. Or rather, only if the competence profile has been developed in parallel with experience gained and according to the latest requirements. Unfortunately, this is exceedingly rare, as the data from the many parsed CVs clearly show.

Protect a lack of qualifications?

Lately, there have been repeated discussions about special protection against termination or special quotas for over 50s, in the hope that this constantly growing problem will be mitigated in the long term. But are these ideas not extremely unfair and discriminatory towards younger and usually better qualified workers? Workers who are already severely disadvantaged when it comes to major topics such as retirement provision, and thus are already proving more than enough solidarity with older workers.

Such an approach leads to unacceptable discrimination against younger generations by protecting less-qualified applicants. Not only that, such a rule would also mean that current 50+ jobseekers may no longer be recruited because employers fear that they will not be able to dismiss them. This type of reaction can be widely observed in countries with rigid employee protection laws such as Germany and France, where, as a result, many employers strongly favor fixed-term over permanent contracts. Special protection against termination is thus not a solution, it is a fallacy.

Another idea aimed primarily at mitigating the consequences of systematic age discrimination is the bridging pension (rente-pont). But if that is not the driving factor behind long-term unemployment of older workers, then this will just amount to yet another instance of discrimination against younger jobseekers. Instead, older jobseekers should be trained – many of them barely know how to apply for an opening. Looking at their CVs, you immediately encounter the showcase syndrome: instead of listing relevant skills, the document is adorned with information of no relevance whatsoever such as obsolete programming languages learnt 20 years ago. As a consequence, such an applicant will often seem desperate and insecure, not like a proud, promising new employee who will support the department and enhance it.

So why hire over 50s at all?

Too expensive, too little professional expertise, too inflexible – these are classic stereotypes older employees are branded with. True, the younger generation is usually more flexible and mobile in terms of time and place of work. Sell my beloved house after twenty years and move far away to another city or canton? No thanks. The reality that wages automatically rise with increasing work experience and age is another fact that is never questioned and rarely discussed publicly. And yet, this is another point where performance should be assessed rather than age. Why not earn the most when we are at our most performant and our expertise is its most comprehensive and up to date?

And finally, young jobseekers often also boast more extensive language skills and are, for the most part, much more IT-savvy. However, a few arguments speak in favor of the older generation: they have a high sense of duty and responsibility, very often have a positive attitude to work and are usually regarded as balanced and notably more consistent.

Anyone who now thinks that these issues can be reduced with anonymized AI-based application procedures is completely wrong unfortunately. These procedures do not focus on the person, but on relevant skills, current education and training, language skills, industry knowledge and specializations. An evaluation of various applicant selection processes in a wide range of occupational groups and industries has shown that, in fact, (with the exception of select management positions) the pool for the next round usually contains a significantly smaller proportion of 50+ candidates than in conventional selection procedures. This in turn proves that it cannot be due to the age of the candidates because all personal characteristics such as age, gender, origin, etc., were fully disregarded in the selection process and thus played absolutely no role in the matching and ranking, which formed the basis for the interview invitations.

We must therefore find other strategies. The key is ‘to be found’ instead of ‘to search’. Positions tailored to over 50s are often not found in job postings. However, there are technological tools that ignore these common prejudices against older employees. Machines decide on the basis of matching data points. They do not know discrimination against age, gender, ethnicity, etc. Older applicants should utilize this opportunity, especially to find out what they can draw on to increase their chances. These tools also give very objective and sober answers to many questions such as how many matches do I really get with my current qualifications? Where are my personal skill gaps? In the mind of the machine, there is no ‘I didn’t get the job, wasn’t even shortlisted just because I’m over 50. Sure, that figures…’

Using these tools, employment services, recruiting companies, job portals and others can make attractive employment proposals to 50+ talents, but also pinpoint individual placement challenges. For a gap analysis, feel free to ask for help at info@janzz.technology

Welcoming Jimena Renée Luna as our new VP of Customer Integration, Emerging Markets

We are proud to announce that Jimena Renée Luna will be joining JANZZ.technology as our new VP of Customer Integration, Emerging Markets. She will be responsible for all accounts in LATAM, EMEA and Southeast Asia.

Jimena is well-established and highly experienced in advising client governments and international organizations on tech policy, job creation, and economic development. Throughout her career, she has worked 10+ years designing and implementing related projects with teams across Latin America, Europe and Africa. At the World Bank, she performed research on labor markets and launched innovative solutions for job creation. In addition, she has worked for the U.S. CIO at the White House on digital policies to improve how citizens and businesses interact with government – helping to close the gap between the public and private sector on technology and innovation. More recently, she has worked on projects in Africa to promote the digital economy and digital development.

Jimena is enthusiastic about the job matching products and digital solutions offered by Swiss-based JANZZ.technology to clients around the world. She is confident that digital platforms, big data, and AI will drive the economy of the future. At a time when the world is facing a digital transformation and changes to the labor market, she is excited by the opportunity to work directly with global clients to provide them with digital solutions for job creation.

Jimena will be joining us on May 15. She will start working from Washington, DC, and then transfer to our headquarters in Zurich at a later date. We look forward to seeing Jimena applying her experience, enthusiasm and professionalism to our mission to better serve our clients.

Feel free to reach out to Jimena via email at j.luna@janzz.technology . She is fluent in English, French, and Spanish and will be happy to answer any questions you might have.

JANZZ.technology – providing semantic technologies powered by ontology

If we ask a computer to translate the English sentence “the box is in the pen” into other languages, it will most likely interpret the word “pen” as the object we use to write with, this being the more frequently used meaning. But then the sentence will be nonsensical because, as we know, a larger object cannot be inside a smaller one.

Language processing or natural language processing is a much bigger challenge in AI than, for instance, image processing. We humans realize that, for this sentence to make sense, the word “pen” must mean a small area surrounded by a fence. A computer, on the other hand, lacks contextual knowledge and thus the logical reasoning needed to translate the sentence correctly. Another example would be “John is flying to the Big Apple on Tuesday.” You can probably guess what the result would be.

This is where semantic technologies come in. Among the many available methods, semantic techniques aim to improve computers’ understanding in processing natural/conversational languages through knowledge representation. Semantic technology is powered by ontology: it relies on semantic information encoded in ontology to identify nodes (e.g., words) that are semantically related.

At JANZZ.technology, we offer superior semantic technologies including semantic extraction, searching and matching powered by our comprehensive ontology in the domain of occupation data. To illustrate, JANZZ.technology’s semantic solutions can realize the following smart applications:

-Job searching and matching on related concepts

Related concepts are not (necessarily) synonyms but concepts which share similarities, sometimes given in completely different words or even languages. For example, “Neonatology” and “Pediatrics” are related concepts. With the information stored in ontology, semantic technology can identify how closely these two terms/professions are related to each other and, importantly, what kind of training/certifications one of these professionals needs in order to perform the other one’s job. This can be extremely helpful when transforming workforce skills on a large scale such as public employment services.

As another example, “Creative Director” and “Web Designer” are also related concepts but to a much lower degree compared to “Neonatology” and “Pediatrics”. If you are looking for a “Web Designer”, our semantic technologies would also recommend someone with job title “Creative Director” combined with skills in CSS, HTML and UX, or suggest such skills. Of course, “Concepteur Web”, “Nettdesigner”, “مصمم على شبكة الإنترنت” or “网页设计师” will also be matched. Related concepts can also be skills or education. For example, if you are looking for someone experienced with ERP systems, our semantic technologies know that candidates whose CVs list SAP, JD Edwards and MS Dynamics are all good matches because these are all ERP systems.

– Job searching and matching on degrees of skills

Semantic technology is not only able to match job postings and CVs containing the same skills, but it can also compare the degree of skills. For instance, “MS office skills” is a broad term and listed in many CVs. If you are looking for a Spreadsheet pro, you don’t want to be matched with a myriad of CVs listing basic MS office or beginner’s level Excel skills.

Similarly, if you are searching for professional CAD software skills, our semantic technologies would match CVs with CATIA, OpenSCAD or Rhino rather than TinkerCAD or BlocksCAD because the different specificities of CAD software are also stored in our ontology. Moreover, our semantic techniques not only identify levels of skills, but also report any training necessary for candidates to transform skills from one CAD software to another.

– Concept identification through interpretation of the context

Semantic technologies help identify cryptic concepts through context. Job titles can be very challenging for computers to identify. In the sentence “Company X is looking for an RF System Engineer, Building 8, Menlo Park, CA,” our software is able to decode each part of the sentence with the information stored in our ontology, such as industry codes, company names and places of work. In this case, “Building 8” is not an address but instead a mysterious department for hardware development at Facebook, and the “RF System Engineer” refers to “Senior Radio Frequency Engineer”.

– Job matching on overall dimension of occupation data

Some job titles, such as dentist, pilot, carpenter and Android app developer, already contain a lot of information about the specific position. When matching these jobs, it is possible to match almost exclusively on job titles. However, other titles like teacher, consultant, assistant, engineer and coordinator are much less specific. In such cases, one needs to include other criteria such as industry, skills, education, experience, etc., in order to conduct an accurate and meaningful matching. Semantic solutions from JANZZ.technology can perform such tasks with the data linked in ontology.

– Identifying gaps in the information

In contrast to machine learning, which is proficient in pattern recognition and classification, an ontology models meaning. It helps a system to understand CVs and job postings and perform gap analyses, thus creating a more user-friendly experience. For example, when matching candidates and jobs, semantic technologies can recommend skills, education or training which a given candidate lacks and thus help candidates optimize their CV.

Are you a large international corporation, organization or public employment service? Do you want to have the right technology to prepare and accompany your labor force throughout the digital transformation? Do you want to improve user experience during the application process? Do you want to build a more powerful system which makes your products stand out from the HR tech crowd? To integrate the latest semantic extraction, searching and matching technologies powered by JANZZ’s ontology, please write now to  sales@janzz.technology and let JANZZ.technology assist you.

Is reskilling and upskilling the real cure for today’s skills shortage?

Digitalization, automation and AI pose a great threat to today’s job market that requires constantly changing skills. However, some of the skills are not missing due to the evolution of technology, but rather due to a loss of attractiveness. This is especially the case for positions with an unusually high number of vacancies or such that remain vacant for a long time.

 

According to the Swiss Skills Shortage Index, “a skills shortage exists if there are more vacancies than job seekers in an occupation.” Last year, the Adecco Group compared in its Swiss Job Market Index job advertisements with the number of job seekers registered by the Vacancies and Job Market Statistics Information System (AVAM), which yielded the 2019 Swiss Skills Shortage Ranking.

As in previous years, in 2019 engineering occupations such as structural and electronics engineers are most wanted by Swiss employers. They are followed by technical occupations, fiduciary and IT professions. The ranking further indicates that compared to 2016, when the measurement was conducted for the first time, the skills shortage in 2019 is 22% higher across Switzerland. [1]

There are many reasons for the skills shortage. The rapidly changing skills requirements caused by technological innovation are believed to have the most profound impact on the risks of skills mismatch and shortage. Similarly, Hay’s Global Skills Index 2019/20 reported the highest talent mismatch since the index’ launch in 2012 and they, too, believe that technological development is one of the main contributing factors [2].

On the part of businesses, many companies facing the threat of talent shortages, which might damage their commercial success, prepare themselves for new technologies by upskilling their existing workforce, investing in training, encouraging lifelong learning and raising the retirement age.

There is no doubt that continuous upskilling throughout a career will become the new normal, but is this really the key to overcoming skills shortage? If it were, how come that the situation looks as if things are going in the opposite direction?

Another report published by a Swiss online job portal and Zurich University of Applied Sciences (ZHAW) provides further insight into the Swiss job market. The report compared more than 100,000 job advertisements with the number of clicks on Swiss job portals and, thus, reveals people’s interests in specific jobs in a more direct fashion.

In the German-speaking part of Switzerland, professions in administration, HR, consulting, sales and customer services, marketing, communication, and executive boards received more interest (clicks) than the job advertisements posted. However, jobs in areas like production, telecommunications, construction or nursing received less interest (clicks) compared to the job advertisements posted. [3] This suggests that economic incentives as well as social recognition are becoming increasingly important for people when it comes to choosing a profession.

Last year, there were over 6000 professional care vacancies in Switzerland. This number has doubled compared to five years ago.[4] Reporting on the healthcare workforce supply and demand in Switzerland shows that care workers graduating in the near future will only cover 56% of the demand until 2025.[5]

In the case described above, the problem doesn’t have to do with up-or reskilling. It is rather about the ways in which more people – especially younger ones – can be encouraged to pursue a career in jobs that are considered less attractive. What is even worse, evidence shows that due to bad working conditions (e.g. little income, long working hours, too much stress) a large share of young people has switched their working field either right after their apprenticeship or after a mere few years of professional experience. This includes professions in childcare, hospitality, catering services and handcrafts.

Today everyone is talking about automation, digitalization, AI, upskilling and reskilling. We must remember that there are still many jobs that are unlikely to be automated but essential to our daily lives. And these jobs are losing in popularity. It is important for governments and education systems to take action on increasing awareness and to promote such professions. As written in the OECD Employment Outlook 2019, the “future of work is in our hands and will largely depend on the policy decisions countries make.”

For almost a decade, JANZZ.technology has been observing and working with many labor markets worldwide. Our latest product JANZZdashboard! creates transparent and easy to understand gap analyses of the labor market. This will give governments a clear idea of which skills are available and which ones should be expanded or redeveloped. To learn more about our solutions please write now to sales@janzz.technology

 

 

 

[1] Spring. 2019. Swiss skills shortage index 2019. URL: file://srvgiga-adart/JANZZ.technology/JANZZ.technology/JANZZ.technology%20Company/JANZZ%20Business%20Development/JANZZ%20Social%20Media&Blogs/JANZZ%20Posts/2020/Swiss%20skills%20shortage%20index%202019/adecco-study-data.pdf [21.01.2020]

[2] Rachel Muller-Heyndyk. 2019. New technology causing skills gaps and stagnant wages. URL : https://hrmagazine.co.uk/article-details/new-technology-causing-skills-gaps-and-stagnant-wages [21.01.2020]

[3] Robert Mayer. 2019. Die meisten Stelleninserate, die geringste Nachfrage. URL : https://www.tagesanzeiger.ch/wirtschaft/in-diesen-berufen-herrscht-ein-mangel-an-fachkraeften/story/18953945 [21.01.2020]

[4] Albert Steck. 2019. Offene Stellen auf Höchststand. URL: http://jobs.nzz.ch/news/6/arbeitswelt/artikel/421/offene-stellen-auf-hochststand [21.01.2020]

[5] Veronica DeVore. 2016. When caring for patients gets competitive. URL : https://www.swissinfo.ch/eng/showing-off-skills_when-caring-for-patients-gets-competitive/42524090 [21.01.2020]

 

 

 

 

 

 

JANZZ ontology – empowering your data and realizing smart applications

Ontologies have been around in artificial intelligence (AI) research for the last 40 years.[1] Just as trends come and go, ontologies too have had their ups and downs. Introduced in the 80s, ontologies became popular in the mid-90s. After machine learning (ML) came on the scene in 2000, the widespread opinion was that in the future every task performed with a computer (by means of AI and ML) could be solved with a smart algorithm. A lot of companies invested heavily in these algorithms hoping to have the next breakthrough in AI.

With the rapid development of AI and ML, especially after the emergence of the convolutional neural networks (CNN), the core technology – deep learning (DL) – has been growing rampantly in terms of parameter size and computing complexity. Today some of the most complex models have reached a scale of billions of parameters. [2]

Still, concerns have been raised regarding the current mainstream DL. Let us take supervised learning in image recognition as an example: the images used to train the AI models need to be manually identified in terms of the position and contour of the target objects, for the models to be able to find implicit pattern features between the data after comparing different labeling results. If we recall how we learn things as infants, us human beings can easily identify and classify different objects without needing this kind of instruction. [2]

DL methods have made tremendous progress and are now able to extract knowledge from the training data. However, this knowledge is not explicitly explainable, because the so-called “black box” training cannot reveal the complex relations hidden within the models. When facing new problems, the current DL models are unable to apply their acquired knowledge to solve new challenges in an effective way. [2]

There is another big concern regarding big data and the privacy issue related to it. What is more, current DL methods are based on big data which is not applicable to industries that generate small amounts of data such as certain fields in medicine and human resources. This case requires AI systems to have the ability to reason and judge, which can only be successful in specific domain areas. [3]

Many powerful ontologies already exist for specific domains, examples include the Financial Industry Business Ontology (FIBO) as well as numerous ontologies for healthcare, geography or occupations. It is widely believed that knowledge integration and DL are the important ideas for further amplifying the effectiveness of DL. For this reason, ontologies have made it back into the spotlight, along with many equivalents such as knowledge graphs or knowledge representations.

At JANZZ.technology, we started to build our ontology – JANZZon! – in 2008, before the tech giant Google invented and popularized the term “knowledge graph”. JANZZ.technology has been building its ontology using domain experts with various backgrounds (e.g. intellectual property law, fluid dynamics, car repair, open-heart surgery, or educational and vocational systems).

Today, JANZZon! is the largest multilingual encyclopedic knowledge representation in the field of occupation data. The main focus lies on jobs, job classifications, hard and soft skills, training/qualifications, etc. The number of stored nodes and relations comes to more than 350 million!

Integrated with both data-driven and expert consultation taxonomies, JANZZon! covers ESCO, O*Net, ISCO-08, GB/T 6556-2015, DISCO II and the UK skills taxonomy from Nesta just to name a few. Currently, 9 languages (German, English, French, Italian, Spanish, Portuguese, Dutch, Arabic and Norwegian) fully cover occupation, skills, specializations, function, education, etc., and we are working on achieving the same level in a total of 40 languages in 2020.

Being the backbone of our job and skills matching technology, JANZZon! represents the knowledge at the deepest level where all the entities have been encoded and vectorized in the semantic space. Therefore, when searching and matching, our technology can truly understand a concept and its semantic meaning and thus guarantee meaningful results.

If you are wondering how ontologies can help you empower your data in the fields of human resources and labor markets and aid you in realizing smart applications? Please write to sales@janzz.technology

 

[1] ODSC. 2018. Where Ontologies End and Knowledge Graphs Begin. URL: https://medium.com/predict/where-ontologies-end-and-knowledge-graphs-begin-6fe0cdede1ed [2019.11.20]

[2] Li Jun. 2019. Shen Du Xue Xi: Xin Shi Dai De Lian Jin Shu. URL : https://www.ftchinese.com/story/001084827?page=1&archive [2019.11.20]

[3] Cai Fangfang. 2019. Qin Hua Zi Ran Yu Yan Chu Li Ke Xue Jia Sun Maosong: Shen Du Xue Xi Peng Pi Zhi Hou, Wo Men Hai Neng Zuo Shen Me? URL: https://www.infoq.cn/article/OvhfhpPChTLpsMgrf43N [2019.11.20]

The potential of AI in human resource management

Artificial intelligence (AI) is unquestionably a powerful tool. Its economic value is increasing tremendously and transforming numerous industries such as manufacturing, fintech, healthcare and automobile. Workers in finance and marketing have much success using AI technologies, whereas HR practitioners find it rather hard to integrate these into their daily practices.

Prasanna Tambe, Peter Cappelli and Valery Yakubovich state in their research: “there are systemic and structural differences for HR that do make it harder, when you are building an AI-based system.” [1]. Due to the fact that the quality and explanatory power of big data and AI are limited they are still considered unconventional in the fields of HR and employment. To have a better understanding of this matter, we need to consider the AI problems in terms of data science in human resource management (HRM).

There are three main challenges in HR practices when it comes to data science. The first issue is the lack of consistency in HR process measurement throughout the employee life cycle. For example, when determining which candidate to hire or choosing whom to promote, it is crucial to consistently record and analyze which criteria and skills were the decisive factors in the previous hiring process.

The second problem with HR practices is the limitation of data sets produced in HRM. Unlike some fields such as marketing and finance, where a lot of data are generated and easily gathered, data collection in HRM faces big challenges in terms of quantity and quality. Moreover, data in HRM is oftentimes unstructured (on paper, in excel or PDF) and consequently, difficult for a computer to process.

The last difficulty concerns ethical issues related to data processing. The results of HR decisions can have a significant impact on someone’s career. Therefore, it is imperative to think about how fairness and transparency can be achieved. Furthermore, it is also crucial to know how employees react to the results that are solely based on data-driven algorithms. As Morgan Hampton from Tesla declared, “recruitment should be automated as much as possible, hiring should remain human.”

Taking these three issues into consideration when searching for AI solutions, HR managers should focus on the following aspects in order to utilize AI more effectively. Firstly, HR managers need to create the right HR process that is ready both for the digital age and AI technology.

Currently, AI technologies are separately, for example in recruiting and talent acquisition, payroll management and self-service transactions. However, they lack a mechanism to generate data that can assist the whole AI process in HR practices.

HR managers often merely keep the applications that they are interested in and don’t retain those that are screened out. This leads to a one-dimensional analysis and conclusion [1]. All these criteria should be gathered in data collection and eventually, be evaluated to facilitate the development of big data models and AI processes.

Furthermore, it is also imperative to generate data in a sustainable way. For instance, there are AI applications that can predict which workers are about to quit their jobs, and some even track data points from employees’ social media or emails [2]. If employees were aware of such a system, they would probably change their behavior and deliberately produce misleading data.

Last year, the story about the Amazon AI recruiting tool being biased against women was proof that machine learning can mimic human attitudes. Gender, nonetheless, is not the only aspect that is reason for discrimination. Others such as age, nationality or ethnicity could also have a negative impact, keeping companies from inclusive and diverse hiring. HR managers should carefully collect data samples that are representative and look for explainable AI solutions. The complex neural networks in deep learning is far from self-explanatory.

Up until today, the standard data criteria that HR managers should respect throughout the HR practice cycle does not exist yet. This means that HR managers have to team up with their company’s internal IT department or with external AI vendors to determine what data to trace and how to measure those data, in order to establish the best practices for AI within their companies.

At JANZZ.technology we believe that collecting and structuring data is fundamental for creating smart data. Our parsing tool extracts the right entities from paper, Excel or PDF, ensuring a fair end-to-end data processing from the very beginning. Do you want to know more about our parser and how we can assist you in your AI transformation journey? Please write now to sales@janzz.technology

 

 

[1]Prasanna Tambe, Peter Cappellli and Valery Yakubovich. 2019. Artificial intelligence in human resources management: Challenges and a path forward. URL:https://www.researchgate.net/profile/Peter_Cappelli/publication/328798021_Artificial_Intelligence_in_Human_Resources_Management_Challenges_and_a_Path_Forward/links/5c5edc7f299bf1d14cb7dc5f/Artificial-Intelligence-in-Human-Resources-Management-Challenges-and-a-Path-Forward.pdf [2019.10.20]

[2] Samantha Mclaren. 2019. Here’s how IBM predicts 95% of its turnover using data. URL:https://business.linkedin.com/talent-solutions/blog/artificial-intelligence/2019/IBM-predicts-95-percent-of-turnover-using-AI-and-data[2019.10.20]

Sex segregation in the workplace

The segregation of people in the workplace according to their biological sex is partly due to different preferences and aptitude for specific occupations. Traditionally, jobs with the highest concentration of women are to be found in teaching, nursing and other care-related service work. The majority of male workers, conversely, holds blue-collar jobs, for instance in construction, equipment operation or repairing.

Furthermore, since occupation fields dominated by female workers have a lower compensation in comparison, it is more common for women to change into a profession that is male dominated than vice-versa. This is accompanied by recent developments such as the #MeToo movement and the introduction of women’s quota. However, this change is one-sided: occupations with a disproportional amount of men such as childcare remain largely unchanged.

In 1996, the European Commission Network on Childcare set the strategic goal to increase male employment in childcare to 20% by 2020. With a few months to go before the deadline, the employment rate of men in this domain is still far below the target number.

In Germany currently 6 in 100 pre-school childcare workers are male. In the UK and Ireland, the numbers are even lower than 2 out of 100. Norway, which is considered the global leader when it comes to gender equality, has the highest rate – with 9 in 100 men working in early childcare. This score is nevertheless far below the target of 20%. Outside of Europe the situation remains fairly unchanged. In the United States and Australia, the number of men working in early childcare amount to 4% and 2%, respectively.

Interestingly, things are quite different in a childcare center situated in Stuttgart, Germany. This center employs 12 people, half of which are male; moreover, it keeps receiving applications from more men. What is the secret of this center that appears to be so attractive for male childcare workers?

Mr. Nöth, the center’s founder and a former childcare worker explains that, unlike in traditional childcare centers with fixed groups, in his center children are presented with seven different activity groups, including ones for painting, handcraft and gymnastics. Each group activity takes place in a separate, specially equipped room and the children can attend a different group every day. Nöth notes that this way of operating offers opportunities for the childcare employees to work creatively, which includes individually designing and developing the activities. Such liberties, in turn, are said to be highly valued by many men. [1]

Gender stereotyping in the workplace like the one pertaining to men in early childcare is still present and it can create a hostile environment for male employees. In addition, work life can also be isolating when most co-workers are female. If there is little camaraderie and social activities among male coworkers, this might impel the few men to quit. At least, this is suggested by the fact that several of the male childcare workers from the Stuttgart center report to feel more comfortable among the relatively high number of male coworkers.

Such special cases can give important indications for changing the workplace situation where sex segregation poses a significant challenge. One point is concerned with creating new functions for roles that are dominated by one particular sex.

This is already happening in some blue-collar professions. With the advent of new technologies some of the jobs are shifting their focus from intense physical tasks towards sophisticated machine-involving activities. This in turn attracts an increasing number of women to the field.

Another point involves the achievement of gender balance in the workplace. If one sex is in the minority it is likelier to have unpleasant experiences at work. Statistics indicate that for women such unpleasantness increases tremendously in occupations with over 90% male employees.

In Norway, the education system actively directs men towards certain teacher colleges in order to prevent unbalanced concentration. This way, it encourages more men to apply and contributes to higher shares of male workers in early childcare. [2]

For almost a decade, JANZZ.technology has been observing and working with many labor markets worldwide. Our unique matching solution only uses parameters that truly matter for job matching. This includes functions, skills, specialization, experience etc. but avoids biases with regard to age, gender or origin. To learn more about our solutions please write now to sales@janzz.technology

 

 

 

[1] Philipp Awounou. 2019. Kann Mann machen. URL: https://www.spiegel.de/karriere/kita-in-stuttgart-wo-das-halbe-personal-maennlich-ist-a-1281251.html [2019.10.03]

[2] Jack Graham. 2018. Men don’t feel welcome in early childhood. Here’s how to change that. URL: https://apolitical.co/solution_article/men-not-welcome-gender-inequality-in-the-early-childhood-profession/ [2019.10.03]

JANZZ.technology offers explainable AI (XAI)

Over the past decade, thanks to the availability of large datasets and more advanced computing power, machine learning (ML), especially deep learning systems, have experienced a significant improvement. However, the dramatic success of ML has forced us to tolerate the process of Artificial Intelligence (AI) applications. Due to their increasingly more autonomous systems, current machines are unable to inform their users about their actions.

Nowadays, most AI technologies are made by private companies that make sure to keep their data processing a secret. Furthermore, many companies employ complex neural networks in AI technologies that cannot provide an explanation on how they come up with certain results.

If this kind of system, for example, wrongly affects customers’ traveling, this might not be of great consequence. However, what if it falsely impacts autonomous vehicles, medical diagnoses, policy-making or someone’s job? In this case it would be hard to blindly agree with a system’s decision-making process.

At the beginning of this year, the Organization for Economic Cooperation and Development (OECD) put forward its principles on AI with the purpose of promoting innovation and trustworthiness. One of the five complementary value-based principles for the responsible stewardship of a trustworthy AI is that “there should be transparency and responsible disclosure around AI systems to ensure that people understand AI-based outcomes and can challenge them.” [1]

Explainable AI (XAI) has recently emerged in the field of ML as a means to address the issue of “black box” decisions in AI systems. As mentioned above, most of the current algorithms used for ML cannot be understood by humans in terms of how and why a decision is made. It is hence hard to diagnose these decisions for errors and biases. This is especially the case for most of the popular algorithms in deep learning neural network approaches. [2]

Consequently, numerous regulatory parties, including the OECD, have urged companies for more XAI. The General Data Protection Regulation, which took effect in Europe, provided the people in the European Union with a “right to a human review” of any algorithmic decision. In the United States, insurance laws force companies to elaborate on their decisions such as why they reject the coverage of a certain group of people or charge only a few with a higher premium. [3]

There are two main problems associated with XAI. Firstly, it is challenging to correctly define the concept of XAI. Furthermore, users should be aware of what the limitations of their knowledge are. If companies had no choice but to provide detailed explanations for everything, intellectual property as a unique selling proposition (USP) would disappear. [4]

The second problematic factor is assessing the trade-off between performance and explainability. Do we need to standardize certain tasks and regulate industries to force them to search for transparently integrated AI solutions? Even if that means putting a very high burden on the potential of those industries.

At JANZZ.technology, we try our best to explain to our users how we match candidates and positions. Our unique matching software excludes secondary parameters such as gender, age, or nationality and only compares skills, education/training, specializations, experiences, etc. It only uses aspects that truly matter in order to find the perfect candidates.

Instead of giving one matching score, our unique matching system breaks down all criteria such as functions, skills, languages and availability. This allows users to have a better understanding of the results and sets the foundation for reskilling and upskilling the workforce to be analyzed. Do you want to know more about the ways in which JANZZ.technology applies explainable AI solutions? Please write now to  sales@janzz.technology

 

 

[1] OECD. 2019. OECD Principles on AI. URL :https://www.oecd.org/going-digital/ai/principles/ [2019.9.17].

[2] Ron Schmelzer. 2019. Understanding Explainable AI. URL: https://www.forbes.com/sites/cognitiveworld/2019/07/23/understanding-explainable-ai/#6b4882fa7c9e[2019.9.17].

[3] Jeremy Kahn. 2018. Artificial Intelligence Has Some Explaining to Do. URL: https://www.bloomberg.com/news/articles/2018-12-12/artificial-intelligence-has-some-explaining-to-do[2019.9.17].

[4] Rudina Seseri. 2018. The problem with ‘explainable AI’. URL: https://techcrunch.com/2018/06/14/the-problem-with-explainable-ai/[2019.9.17].

 

 

 

 

In China’s industrial heartland city of Chongqing, a smart technology upgrading is on full display

The second Smart China Expo (SCE) was successfully held during 26-29 August in Chongqing China. Some of the most newsworthy Chinese business leaders, including Jack Ma Yun (Founder of Alibaba Group), Pony Ma Huateng (Chairman and CEO of Tencent), Robin Li Yanghong (Co-founder, Chairman and CEO of Baidu), Yang Yuanqing (Chairman and CEO of Lenovo Group), gathered in Chongqing  and shared their views on intelligent technology, offering their suggestions for the smart industry.

What is more, President of the People’s Republic of China, Xi Jinping, sent a congratulatory letter for the opening ceremony of the SCE and Chongqing was announced to be the permanent meeting place of this high standard event. But what makes this city the pioneer of the intelligent and smart industry in China? Perhaps the following aspects could give you some insights.

 China’s strategic development    

At the end of the 20th century and the beginning of the 21st century, the Central Committee of the Communist Party of China and the State Council carried out Deng Xiaoping’s thought of “two overall situations” in China’s modernization drive and made a strategy for the development of the western region. As the only municipality directly under the Central Government in Western China, Chongqing has ushered in historic opportunities and accelerated the pace of municipality development.

In 2013, the Chinese government took the “One Belt One Road” initiative as a global strategy, and Chongqing once again welcomed the golden opportunity for development. As the intersection of “Silk Road Economic Belt” and “Yangtze River Economic Belt”, the strategy of “One Belt One Road” accelerated Chongqing’s opening to the East and West.

After stepping on the express train to achieve high economic growth, Chongqing’s economy encounters downward pressure. In order to achieve a new round of growth, since 2018, Chongqing has implemented an innovation-driven development strategic action plan led by the intellectualization of big data.

Complete industrial chain   

Chongqing’s choice was clearly well thought out. Chongqing plays an important role in the integration of informatization and industrialization in China. It is one of the country’s oldest industrial bases in the West and an important industrial manufacturing base for the country. Additionally, the industry in Chongqing has got a relatively complete chain. Besides the traditional manufacturing industry, it also includes the electronic information and communication industry.

It is China’s largest manufacturer of cars and is home to some of the largest car manufacturers such as “Changan Ford” and “LIFAN”. It also produces one in every three laptops in the world. Having a good industrial foundation makes it feasible to transform and upgrade the industrial economy with smart technology.

 Well established digital platforms   

After several years of efforts, Chongqing has built a unified information sharing platform for the entire city. A three-level (country-city-district) government information resource sharing system links the national platform and 38 district platforms together.

Chongqing has taken the lead in completing data sharing at the provincial/municipality level, through which the isolated information islands of the departments can be bridged, and the data aggregation can be accelerated. More than 200 applications for government services, urban planning, urban governance, etc. within 80 departments have been realized.

Well established network infrastructure

Chongqing has become one of the 10 first-level nodes in the national communication network architecture, which have greatly enhanced Chongqing’s position as an internet hub in the West and the supporting capacity of Chongqing’s network infrastructure.

Chongqing has also become one of the two big cities in the Western Region where three operators (Mobile, Unicom and Telecom) have launched 5G pilot projects together. Thus far, Chongqing Mobile has opened two 5G base stations. Within this year, it plans to build and open 50 5G base stations in the main urban area.

 During the opening, the newest technologies, products and applications in the fields of artificial intelligence, big data, self-driving vehicles, drone and virtual technology, and so on, were presented and released. The exhibitors included several technology heavyweights such as Alibaba, Google, Inspure, Qualicomm, Huawei and Baidu.

Among the 843 companies from 28 countries and regions, JANZZ.technology was honored to take part in the SCE in Chongqing. We believe that China will be the leading country in smart technology in the future and we hope to seek potential Chinese partners that can help us open the door to the Chinese market. We could still recall the hundreds of youngsters we met during the expo. Through their excited eyes, we see the bright future of the city.