Effective Data Curation for Occupation Related Data: How We Are Dealing with NAICS and ISIC.

The North American Industrial Classification System (NAICS) and the International Standard Industrial Classification (ISIC) are two landmarks on our way to master occupation data. The way we are curating the data from these two classifications is exemplary of our approach to put a deep understanding of jobs, skills and industries at the center of our recruitment/employment solutions. Hence, we felt it would be about time to give you a little more insight into how we deal with occupation related data, showing you the inherent complexity of the labor market and the difficulty in preparing occupation related data in a way that it can go on to drive some of today’s most powerful applications. For example public employment services, applicant tracking systems, statistical tools or job boards. Solutions that help alleviate some of today’s hardest problems on the global labor market.

NAICS and ISIC

The two industrial classifications are fairly complex structures in themselves. They also show a different approach to the classification of industries. When looking at an industry like street construction for example, NAICS lists a total of 38 different activities under “Highway, street and bridge construction”, among which you will find airport runway construction, highway line painting, pothole filling and guardrail construction. ISIC on the other hand is less detailed; it sums up the same industry in only three bullet points: asphalt paving of roads, road painting and installation of crash barriers and traffic signs. While ISIC contains less detailed information about activities, the underlying structure of the two classifications is the same. The International Standard Industrial Classification has provided guidance to countries in developing national activity classifications, hence most national taxonomies took over its general structure and filled it with country specific activities.

How JANZZ.technology enriches data from standard classifications

Now, what do we do with the thousands of activities and industries in these classifications? We connect each of the terms within the classifications with terms that are already in our ontology JANZZon!: not only related industries, for example other types of civil engineering in the case of “street construction”, but also occupations, skills, specializations and educations that belong within the realm of a particular industry. Also SSIC, the Singapore Standard Industrial Classification, adopts the basic framework and principles of ISIC. Including each of these industrial classifications into our ontology means having a greater level of detail and comprehensiveness at our fingertips than any of the taxonomies could provide on their own.

NAICS and ISIC street construction

Not only industries and activities are curated like that but also skills, educations, job titles etc. All these “data trees” are again interconnected. “Street construction” is related for example with the “road construction engineer”, the “roller driver”, “infrastructure planning” and “road surface marking”.

Sometimes, the denomination of skills, industries and specializations can be the same: for instance, “street construction” could also be a skill or specialization of a construction worker. In these cases, NAICS, ISIC and SSIC intersect with taxonomies of skills and competencies such as ESCO. Our ontology curation team adds these intersections and thereby creates yet more cross-relations and thus makes the ontology even smarter.
On the one hand, the ontology enriches the data from the standard classifications by establishing meaningful connections between occupations, skills, industries and so on. In multiple languages at that. On the other hand, another layer of detail is added to the taxonomies by including also real life data: data from job boards for instance. For taxonomies like NAICS and ISIC have become important tools for comparing statistical data on economic activities but the denominations used are not necessarily the ones used in CVs or jobs postings. By adding a wealth of synonyms, we make the data harvested from the taxonomies fit to be used not only for statistical purposes but also for job matching.
Finally, the effective curation of occupation related data is not only ensured by the breadth and detail of data that is entered into our ontology JANZZon! but also by the industry specific expertise of our team. Establishing meaningful relations between occupations, skills and education requires human experts in order to guarantee the high quality of the knowledge base. In a time when machine learning, smart algorithms and predictive analytics are often held as ubiquitous solutions to everything, we put a deep understanding of occupations, skills and industries back at the center of solving some of today’s hardest labor market issues.

JANZZ Mindsetter – Interview with Dr. Chia-Jung Tsay

JANZZ Mindsetter is about critical mindsets. It provides space for critical voices to offer insights into HR, recruiting, digital transformation, labor market issues such as gender and minority discrimination and many more topical issues.

Dr. Chia-Jung Tsay on biases against strivers

Dr. Chia-Jung Tsay (UCL School of Management) studies the psychological influences on decision making and interpersonal perception, and how expertise and biases affect professional selection and advancement. Dr. Tsay’s work has been published in leading academic journals and featured in media outlets including the BBC, Economist, Harvard Business Review, Nature, and NPR, and in television programs, radio stations, and newspapers across 48 countries. For us, she answered three questions regarding her latest work titled “Naturals and strivers: Preferences and beliefs about sources of achievement“.

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How do you position your argument against the idea that hard work and perseverance are key to achieve success?

There’s a lot of great research out there that suggests that differences in achievement likely reflect deliberate effort and persistence, rather than only innate talent. So it’s interesting that we may have little awareness that we actually have a preference for the natural, and we even sacrifice objective qualifications to hire the natural – and yet it may well be the consistent and persevering individual who achieves more in the long run.

Why are we willing to give up better-qualified candidates in order to hire those believed to be naturals?

Delving into how/why the naturalness bias develops is of great interest for future research. One possibility is that we have a preference for potential over even demonstrated achievement. It is also possible that natural talent is attributed more to stable internal characteristics, and thus be perceived as an immutable, more authentic, and more certain path to success.

Your research suggests that our bias for natural talent is unconscious. How do you think this bias could be circumvented then, e.g. in recruiting?

Further work would be necessary to reveal more specific levers through which we may attenuate the effects of the naturalness bias. If the way in which this bias functions overlaps with those of more established biases, we may consider several possible solutions at the point of performance evaluation. These solutions might include ensuring more precise and tangible metrics of assessment, confronting evaluators with highly achieving exemplars of both naturalness and striving, allowing evaluators to have the time and cognitive resources to fully consider the metrics that are important and valued for actual performance, or simply filtering out any candidate application materials that reference sources of achievement.

JANZZ Mindsetter – Interview with Dr. Wen Hua

JANZZ Mindsetter is about critical mindsets. It offers space for critical voices to offer insights into HR, recruiting, digital transformation, labor market issues such as gender and minority discrimination and many more topical issues.

Dr. Wen Hua on gender issues in the Chinese Job market

Dr. Wen Hua has rich experience in research and international development in the field of gender. She obtained the M.Phil. Degree in Social Anthropology at University of Bergen of Norway in 2005 and received the Ph.D. in Anthropology at the Chinese University of Hong Kong in 2010. She was a visiting fellow of Gender Research Programme at Utrecht University of Netherlands in 2007. She has published several papers on gender issues in English and Chinese journals. She is the author of Buying Beauty: Cosmetic surgery in China, published by Hong Kong University Press 2013.

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Why do more and more Chinese women undergo cosmetic surgeries despite a plethora of reports on the possible side effects?

Since the reforms in the early 1980s, Chinas has been one of the fastest growing economies in the world. The uncertainty and instability created by the drastic and dramatic economic, socio-cultural and political changes in China have produced immense anxiety that is experienced by women both mentally and corporeally. The economic reform has resulted in fierce competition in the job market and produced much pressure on young women to get an edge to stand out in the fierce job market. Meanwhile, despite dramatic social changes, some traditional gender norms that prize women’s beauty over ability remain remarkably unchanged, which leads people to value women’s physical appearance in the workplace. The rapid social transitions lead people to grasp every opportunity presented, and cosmetic surgery is therefore viewed by some women as an investment to gain “beauty capital” for one’s future life in a rapidly changing and fiercely competitive society.

How does beauty matter in job recruitment in China?

In my book, I argues that some women view “Being good-looking is capital,” that is, an attractive appearance as a set of tangible and portable personal assets that are convertible into financial or social capital that can give them an edge in the fierce job market, where occupational segregation of female labor in the service industry and employment discrimination based on gender, appearance, height and age widely exist. In the past decade, it was not unusual that we saw that besides education background and work experience, job advertisements specified gender, age, marriage status, and even height and appearance such as “above-average looking,” “good-looking,” or “height over 1.65 meters.” Female job applicants, especially young graduates who already have fewer opportunities than their male counterparts, have to face more prejudice and discrimination based on appearance during their job-hunting. Within these fewer opportunities, when age and appearance matter, it is not surprising why some Chinese women regard beauty as a capital in the brutal competition for jobs.

What could be done in order to reduce the pressure on graduates to undergo cosmetic surgery?

Over the years, I saw that job advertisements, which require specific gender, age, marriage status, height and physical appearance, are less and less to be seen openly in job adverts. But I think that discrimination in employment still exists in China’s workplace. The discrimination has changed from overt to recessive, while the situation might be even worse because hidden prejudice and discrimination against women is harder to avoid and punish. According to the Third Survey of Chinese Women’s Social Status in 2010, more than 72 percent of women had a perception of “not being hired or promoted because of gender” discrimination. I think that to safeguard women’s rights and interests, the authorities should put more effort and effectively punish gender discrimination in employment, which can also reduce the pressure of graduates to undergo cosmetic surgery.

 

The World Economic Forum on the Future of Jobs

Are you prepared to meet the challenges in the global labor market that lay ahead? Is your company? The World Economic Forum’s Future of Jobs report highlights the widespread disruptions in the labor markets that will be caused by the developments in fields such as artificial intelligence, machine-learning, genetics and nanotechnology. The report found that “technological disruption is interacting with socio-economic, geopolitical and demographic factors to create a perfect storm in labor markets in the next five years”.

The technological innovations over the coming years will lead to an automation of tasks that are highly repetitive such as administrative and manufacturing tasks. At the same time, new jobs will be created by these innovations: most notably roles such as the data analyst, which companies expect will help them make sense and derive insights from the torrent of data generated by technological disruptions, and the specialized sales representative, as industries will have to get more skilled at explaining the value of their new products to outsiders. However, the jobs gained over the next five years will not be able to outweigh the expected losses. The report estimates that a total of 5.1 million jobs will be lost within the period of 2015-2020. What is worse is that the impact is not distributed evenly as routine white collar office functions as well as manufacturing and production roles are expected to be hit hardest – with a total loss of 7.1 million jobs. In contrast, 2 million jobs will be gained in highly skilled professions, predominantly in computer and mathematical, and architecture and engineering related fields.

Importantly, the report’s claim as to the net loss of jobs due to automation and technology is a highly contested one among economists. For instance, David Dorn, Professor of International Trade and Labor Markets at the University of Zurich comes to the conclusion that the two effects – the losses and the gains of jobs due to technological progress – will more or less balance each other out. However, also he argues that the jobs that are being created are not in the same pay bracket as the ones that are lost. Hence, Dorn perceives a divide that is widening more and more.

In order to be prepared to meet these challenges, companies need to build a new approach to workforce planning and talent management, where better forecasting data and planning metrics can anticipate the skills that will be needed to persist. According to the World Economic Forum, “HR has the opportunity to add significant strategic value in predicting the skills that will be needed, and plan for changes in demand and supply”. This means that companies will need expert tools that can generate actionable insights into the development of the labor market. Such tools could help companies make job training investments based on skills deemed seminal and job seekers could get customized suggestions to follow the best opportunities for advancement.

It is not surprising that evaluating which skills will be promising in the future or not is the hardest part. The Forum’s own in-depth analysis of industries, occupations and skills of the future proves that this is not as easy as it seems.

skills

The WEF’s list of top 10 skills for 2020 does hardly seem to reflect the scope of the proclaimed disruptions. The skill sets in 2015 and 2020 contain eight identical skills. Only the ranking has changed. For instance, creativity becomes much more important whereas negotiation loses relevance. More generally though, the skills are formulated so general that they could be assigned to almost any occupation. Indeed, the Word Economic Forum’s prediction reads more like a prophecy of the Delphi Oracle that is so pliable that it will come true in any case. Apart from listing seminal skills, the list does thus highlight the need for better analytical tools that can capture the complexity of the global labor market.

In order to efficiently analyze occupation data and to produce actionable insights that can prepare companies and governments for the disruption ahead, we need semantic tools that can provide context for skills and occupations on a global level. Tools that can make sense of different cultural understandings of a job. Tools that can make meaningful connections between different jobs and skills. Simply tools that bring the same or even a better understanding to occupations than we do.

JANZZ Highlights: How we started off 2016 successfully

2015 was an exciting and busy year for us, with projects in Europe, South East Asia and the Middle East. The complexity due to the many different languages, cultures and labor markets demanded a lot from our database maintenance team. Therefore, we are all the more proud, to have successfully mastered these projects and to have gained so much knowhow on occupation data. Our team and our central asset, our ontology JANZZon!, have learnt so much.

Occupational classifications

  • We have integrated a major part of the Indian occupational classification NCO-2004. That includes not only occupations in English but also in Hindi.
  • The entry of JSOC 2011 (Japan) and NOC 2011 (Canada) is soon completed
  • We are collecting over 14’000 jobs in Dutch, from the national Dutch classification BO&C. We are also enhancing this data with information from real life job postings.

LinkedIn Skills

As the search for the perfect matching talent or job on LinkedIn becomes more and more important, the significance of the skills you display on your LinkedIn profile increases. The network even advertises that members who register their skills will get four times more profile views. The skills users include on their profile also offer an opportunity to personalize job suggestions, adverts and search results more accurately. On the other hand, companies can search for job candidates according to their job title or skills.

Our ontology already included about 70% of all global LinkedIn skills. In order to achieve our goal “to master occupation data”, we have started to teach our ontology also the remaining 30% of these skills. For we are serious about really knowing all the skills in the world (The same is obviously also true for jobs).

Semantic Technology

Why is it so important, to include all these classifications and skills in our ontology? Why does it, for instance, not suffice that LinkedIn knows all the skills its users register? Our ontology not only registers these terms but it also interlinks them logically. In case of the LinkedIn skills, JANZZ provides significant added value through the interlinking of different languages, which makes LinkedIn’s skills comparable on a global basis. Hence, our ontology JANZZon! offers essential context and intelligent evaluation options for applications such as information systems, matching engines, job portals, CV parsers, statistical analysis and modelling tools and much more. The ontology becomes the means to utilize an enormous amount of data intelligently. Big data becomes smart data.

JANZZ Mindsetter – Interview with Kamal Karanth

JANZZ Mindsetter is about critical mindsets. It offers space for critical voices to offer insights into HR, recruiting, digital transformation, labor market issues such as gender and minority discrimination and many more topical issues.

Kamal Karanth on Issues in the Indian Job Market

Kamal Karanth, Managing Director of Kelly Services and KellyOCG India, is the critical voice that opens our blog series JANZZ Mindsetter. Kamal has over 20 years of experience in the recruitment industry. Kelly Services is a leader in providing workforce solutions, providing employment to more than 550,000 employees annually. In this short interview, he reports on current challenges in the Indian job market.

JANZZ Mindsetter: Kamal Karanth

What are the biggest challenges you are facing in the IT staffing market in India at the moment?

Skills: One of the challenges that we continuously face is the demand and supply gap in India. In the IT sector, we see an abundance of opportunities but do not see talent that is skilled enough to fill these roles as the technology change and the skill upgrade pace are not matching. Companies always find it difficult to find good candidates with skills, especially in PHP, Ruby-on-Rails, Python, Android and iOS.

Mobility: Also, India being a vast nation, mobilizing talent to the required location is a challenge at times. IT talent demand is primarily in NCR (Northern Capital region which consists of Delhi, Noida & Gurgaon), Pune (West) and South (Bangalore, Hyderabad & Chennai). Vast IT Talent is concentrated in the South of India and recently have become less mobile as more opportunities are available down south.

Expectations mismatch: We also see that IT talent tend to be ambitious in their salary expectations. The standard hikes in IT are typically around 25-30%. However, we see talent negotiating for hikes around 40-50% which upsets the budgets of organizations.

How do you encounter these challenges?

Our experience in staffing for more than 60 years helps us. Our recruiters are seasoned and competent, and they understand the talent dynamics. We continuously map new technologies and the talent who are well versed with that, we build talent communities in advance so that we can offer them to IT Companies in time. We have offices in all the IT Talent hubs and have developed a network with talent, we try to offer employment to talent who are in the same city to avoid mobility issues, on candidates expectations, our seasoned recruiters use a blend of their experience and relationship to set realistic expectations with candidates. Candidates appreciate our experience and candor while making career moves and it brings the desirable win-win between hiring companies and candidates.

How many engineers are jobless in India at the moment and what happens to them?

It’s difficult to estimate this number as we don’t have a formal way of capturing it. But it is substantial. Engineering colleges have been springing up at a fast rate in India in the last few years. Their number has gone up from a not too modest 1,511 colleges in 2006-07 to an astoundingly high 3,345 in 2014-15. The state of Andhra Pradesh alone has more than 700 colleges. 1.5 million engineers graduate from India every year. Out of the 1.5 million, 60% do not find jobs. Some of them opt for higher education, some of them do non related jobs in sales or BPOs to ensure they remain employed to survive.

Can Brainteasers Help Select the Best Job Candidate?

How many Smarties fit into a Boeing 747? Who would win a fight between Spiderman and Batman? Describe the color yellow to a blind person. These and many more tricky questions are increasingly being asked at job interviews. Brainteasers are superseding the same old standard interview questions to which candidates are ready to deliver a perfectly memorized answer. The question is whether they are a helpful tool to get to know candidates better or rather a silly annoyance that distracts from the really important things such as the skills and competencies of the applicant.

“Where do you see yourself in 5 years?” “What are your strengths and weaknesses?” “Why did you apply at our company?” these are questions we expect to be confronted with at a job interview. The questions to which we lack the perfectly ambitious but at the same time sociable sounding response can be googled beforehand and the appropriate answers learnt by heart – slightly modified to make them sound personal of course. Hence, recruiters gain hardly any new and reliable insights about our personality and suitability for the job from these type of questions. In order to catch applicants off guard, recruiters have had to reinvent their questionnaire. The result: brainteasers.

The fiddly mind games that often don’t even have a correct answer, are ideally suited to reinvigorate job interviews. They require applicants to think analytically, logically and creatively, all at the same time. Recruiters don’t actually expect their counterpart to come up with a definite answer to “how many tennis balls are used on average during a Wimbledon tournament” or “how many cows it would require to cater to the daily milk demand of Starbucks” but candidates’ reactions and their ability to deal with the problem at hand give valuable and genuine impressions.

Glassdoor has started to compile brainteasers from all around the world by asking people to send in the most unexpected questions they had been asked in job interviews. Here are some of the most intricate brainteasers they compiled from interviews at Accenture, Google and Stanford University among others.

The 10 most convoluted brainteasers

 

Accenture, Application for Analyst

eFont Financial solutions, Application for Business Analyst

Frauenhofer Gesellschaft, Application as Research AssociateSpirit Airlines, Application for Flight Attendant

 

Bain & Company, Application for InternshipEstée Lauder, Application for Internship

Google, Application for Product Managerbrainteaser (8)

 

Stanford University, Application for Junior ConsultantBose, Application for IT-Support Manager

 

However, is asking candidates about the perfect way to boil an egg or about the scripture they envision on their headstone an efficient way to filter out suitable or even the best future employees? What is the effect of brainteasers on job applicants? Who wins and who loses out? While brainteasers might amuse some, the silly questions could also make one or the other applicant rethink their initiative to apply at a company. As the examples show, brainteasers are hardly related to the skill set needed to perform the job people are applying for. Instead, they demand open-mindedness, stress tolerance and creativity. If these are essential qualities you are looking for in an employee, brainteasers might indeed be a suitable and fun way to test the waters. After all, the idea of incorporating mind games in job interviews stems from consulting, where test cases have been used for a long time to assess applicants’ analytical skills. However, it might be worth to think twice about the impression these questions will leave on the applicants and whether they fit the overall image of your company. Furthermore, as the case of consulting has shown us, it is only partly true that brainteasers provoke spontaneous answers that are an adequate meter for applicants’ skills. The business with model answers and guides to master brainteasers is already flourishing.

Hence we should ask ourselves, if incorporating brainteasers in job interviews is really the way to go. Is there not another, more efficient way to find the right person for the job? For example, taking unsuitable candidates out of the mix before the job interview has even started. With a few clicks, JANZZ.jobs enables you to find the needle(s) in the haystack. The job platform works like a dating site but instead of matching people to one another, it matches people to jobs. JANZZ.jobs brings together all of the world’s knowledge and skills quickly and accurately, irrespective of the language used. Thus, it focuses only on the relevant skills and qualifications for a job and thereby circumvents bias and personal preferences. JANZZ.jobs is the next generation recruiting tool that fosters equal chances for everyone.

Big Corporations: A Quarter of a Million Want to Leave

Career aspirations today are radically changing. Fifteen years ago, big corporations were the ultimate prestigious go-to for graduates but now they are seriously losing appeal for many young talents. People today want to build careers on their own terms.

The widening gap between people’s aspirations and what most professional jobs offer is leading to an epidemic of dissatisfaction, especially among employees in big corporations. The Escape the City initiative that has been working with disillusioned employees for over 5 years tried to identify the underlying issues of professional disaffection and to get behind the drivers of the changing career aspirations. According to the study, a quarter of a million workers in the UK want to leave big corporations in favor of companies that are more aligned with their values. Accenture, Ernst & Young and PWC are the corporates that most respondents want to leave, whereas companies like Airbnb, Uber and Virgin Galactic are at the top of people’s employer wish list.

Top Ten Companies to Escape and to Join

Source: Escape the City

But why is job dissatisfaction so high? And why does it culminate in big corporations like KPMG or Deloitte? The main issues that cause disaffection are the lack of personal freedom, a clear sense of purpose and a sense of positive social impact, worries about mental and physical health and the absence of creativity, innovation and entrepreneurialism at work. Half of the corporate employees that partook in the study feel that they cannot use their strengths or skills in their current jobs. Consequently, 54% cannot see themselves working in their organization in five years’ time.

Big corporations have failed to spot and catch up with the new ways of earning a living that are rapidly emerging. They still operate according to increasingly outdated paradigms that do not correspond to today’s working professionals’ values. The Escape the City survey admonishes corporates that their outdated attitude towards work is taking its toll on their image: “Perhaps, dear Corporates, you might wish to ask yourselves the questions that your employees are already answering: What is my purpose here? What difference am I making in the world? What positive mark will I leave behind after I’m gone? In a world where I could spend my days any way I choose — why should I spend them doing the work that I’m doing right now?”

It seems that big-named corporations have been living off old glory. Four of the top ten companies that workers want to escape are still to be found on the Times Top 100 Graduate Employers list, on which renowned corporations still hold 17 of the top 20 spots. While a host of talented graduates indeed flock to pursue a career within the corporate world, there is also a rapidly increasing movement away from the prestigious graduate schemes towards jobs in small businesses, start-ups and non-profit organizations. The growing importance of alternative careers is also highlighted by George Monbiot’s article in the Guardian warning students of entering a career in banking, finance or consulting rashly (How a Corporate Cult Captures and Destroys Our Best Graduates). There is indeed a trend of students moving away from these traditional career options; only 15% of Oxford graduates and 16% of Cambridge graduates now pursue finance and consultancy jobs when they leave university.

Furthermore, people’s aspirations to start their own business are increasing exponentially. Innovations in technology and communications make starting one’s own business easier and easier. And social media relentlessly celebrate every success, adventure and champagne moment creating ever higher entrepreneurial aspirations.

We find ourselves in a work revolution, in which the alignment of our own values and those of our workplace is becoming more and more important. JANZZ.jobs – the job platform that works like a dating site – provides an efficient means to find our exact match. Using the latest semantic technologies, it precisely matches profiles of job seekers, employers, freelancers and companies.

Mapping Stereotypes: Immigrant Jobs in the US

Immigrants in the US are widely held to be employed in low-wage jobs such as gardening or housekeeping. A map created by Business Insider, showing the most common jobs held by immigrants across the United States, reinforces many stereotypes. However, a closer look reveals some surprising results.

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The map largely offers an image of immigrants’ jobs corresponding to people’s prejudices, with immigrants holding down low-paying jobs in sectors such as agriculture on the West Coast and housekeeping and construction across much of the South. Yet, there are also four states in the East in which immigrants most commonly work as college professors, and in Delaware the predominant occupation among people born outside the US is software developer.
More importantly however, the map reveals that a significant number of immigrants work as health aides, nurses or personal care aides. Jobs in healthcare will be increasingly in demand with the aging of the population and the supply of workers will depend in part on the availability of immigrant workers.

Indeed, the distribution of immigrant jobs shown on map may lead to false conclusions regarding immigrant employment. The map suggests that most immigrants are employed in low-wage jobs, such as agricultural work or housekeeping. However, the Economic Policy Institute found in an extensive survey that, in the United States as a whole, there are almost as many immigrants in white-collar jobs (46%) as in all other occupations combined. Thus, the perception that all immigrants work in low-wage jobs is clearly inaccurate. While immigrants might be overrepresented in some occupations and underrepresented in others, the discrepancy between the US and foreign born population is not as dramatic as is often assumed. While immigrants are overrepresented in low-wage occupations, as the map shows, they also play a significant role in some high-wage and middle-wage jobs. An analysis of the Bureau of Labor Statistics data for example reveals that high-skilled immigrant workers are overrepresented in industries such as information technology, life sciences and high-tech manufacturing.

However, the fact many immigrants work in high-skilled and high-wage jobs offers little consolation for those at the bottom. Low-wage immigrant workers do not enjoy the benefits of employer-provided training programs as these are usually geared to managers or highly skilled employees. They are also outside the reach of government-sponsored job training programs that aim to inject more equality in the labor market. The data from the census that is represented on the map includes both documented and non-documented immigrants. While documented immigrants tend to hold higher skilled jobs, undocumented immigrants are relegated to menial work. The social gap is wide apart between immigrants at the top and at the bottom and it is not due to get any narrower soon.

In any case, foreign workers make up a large portion of the US work force and are vital to the US economy. An infographic that sums up data from the Immigration and Integration Initiative, as well as original AS/COA research shows just how big an impact immigrants both documented and undocumented have on prosperity in the United States. For example, immigrants started 28% of all new businesses in 2011, employing 1 in 10 US workers, while they only make up 13% of society as a whole.  On average, immigrants pay $1,800 more in taxes than they receive in benefits. They also produce significantly more consumer spending, thereby creating new jobs.

Both the insights from the map and from the infographic remain rather superficial because the statistical analyses do not go beyond job titles. For instance, it might be interesting to see, what skills or talents immgrants in the US bring with them exactly and how they could be put to use most efficiently. Also, an international comparison with other countries would offer valuable insights. An indepth investigation of immigrants’ occupations and skills would mean processing a wealth of data. More importantly however, it would require adequate tools that allow drawing significant conclusions. JANZZ.technology offers exactly that. Its semantic matching technology and its expertise in occupation and skills data provide an effective means to analyse the potential and shortages of immigrants’ skills. Furthermore, JANZZ.technology would allow to compare the immigrants within the US workforce to immigrants in other countries, as the ontology JANZZon! connects job titles, skills and qualifications across multiple languages and cultures. The ontology would also allow to assess the skills of immigrants better, showing them what skills exactly they lack in order to climb up the social and economic ladder. The tools by JANZZ.technology therefore offer a starting point in order to allocate immigrants to the best possible occupations and to learn from an international economic comparison.

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The Rise of the Machines: The Disruptive Potential of Cognitive Computing

As cinematic representations of intelligent machines over the past decades have shown, the fascination with and the fear of artificial intelligence always inevitably mix. People enjoy the thrill of watching humans knocked off-balance by AI in Ex Machina, robots trying to take over the world in I Robot, or seeing an entire team of Marvel superheroes fighting Ultron in the latest Avengers movie. But also in real life, intelligent machines are rivalling with humans and many are afraid of automation and digitalization stealing away people’s jobs. Still, the quest for intelligent machines is relentless.

Thinking Robot --- Image by © Blutgruppe/Corbis

Thinking Robot — Image by © Blutgruppe/Corbis

Back in 1997 the Deep Blue computer picked grandmaster Garry Kasparov apart in a chess match. Three years ago, supercomputer Watson competed on Jeopardy! against two champions and defeated them by far. Now, Watson helps doctors make more accurate diagnoses using raw data from medical research and patient histories. In Japan, cuddly robot bears are hailed to be the future of elderly care. The ability to talk to one’s phone or tablet is not relegated to the imaginary space of films such as the science-fiction drama Her but is a reality. These and many more innovations in the field of artificial intelligence have profound implications for the relationship between man and machine.

Indeed, in our increasingly digitalized world with exponentially growing data volumes, complex issues are handled much more effectively by computers than by humans. Computers can process large volumes of data in a speed unattainable for humans. Not only is data increasing in volume but also in speed, variety and uncertainty. Most data is now supplied in unstructured forms such as images, videos, symbols and natural language – hence, computer systems needed to step up to the challenge in order to process this new kind of data. Cognitive computing aims to simulate human thought processes in a computerized model. Self-learning systems that use data mining, pattern recognition and natural language processing are trained to mimic the way the human brain works. Ultimately, cognitive computing strives to solve complex problems independently, without human assistance. According to Gartner, the era of cognitive computing, also called the smart machine era will be the most disruptive in the history of IT.

While AI capabilities such as natural language processing, speech recognition and machine learning algorithms were invented 30 years ago, it is only now that these technologies find significant application in business systems. More than 2’300 startups have been founded and venture capitalists have invested billions of dollars in the field of AI lately (a representation of the AI business landscape can be found here). Furthermore, major players like Amazon, Google, IBM, Microsoft, SAS and Yahoo are investing in the development of smarter applications.

Why now? The exponential growth of unstructured data not only offered a challenge to computer systems but also an effective means to train machines. Big data, along with improvements in the above mentioned disciplines, is what’s making the difference in machine learning. Sophisticated algorithms can only learn to solve problems independently by repeated training using big data. The success of smart applications thus depends largely on the quality of data that they are fed.

In healthcare, the finance industry, e-commerce, customer relationship management and search engines, cognitive computing is employed in order to support human experts in making faster and more accurate decisions. While machines have thus replaced human work in many fields, especially where manual work is concerned, artificial intelligence does not supersede human experts but rather acts as a catalyst. Cognitive computing systems can amplify the possibilities of what either machines or humans could do on their own.

JANZZ.technology also supplies such a system in the field of employment, skills and talent. The ontology JANZZon! and the smart matching engine JANZZsme! make complex problems such as job and skills matching computable and completely change the way we think and go about job searching. As the applications of JANZZ.technology are structured semantically, that is, occupations, skills and qualifications etc. are interlinked logically; they can deliver meaningful results for complex searches for job vacancies, employees, freelancers etc. in real time, across multiple languages. Importantly, the applications are constantly fed with new data and therefore become more accurate over time. With the tools by JANZZ.technology, you don’t search for a job – you are found.

The high quality of JANZZ.technology’s tools stems from its specialization and expertise in occupation data. The ontology JANZZon! has been built with solid industry-specific expertise and years of experience in HR. Every day, a dedicated team of IT-supporters and engineers work on improving the quality and extent of the ontology JANZZon!. A myriad of connections between occupations, skills and other data stored in the knowledge base is established continuously – like synapses in a human brain – turning the unstructured occupation data into structured data. Big data is turned into smart data. The gist: Cognitive computing tools are only ever as good as the expertise of their human creators. Also the success of the supercomputers Deep Blue and Watson may be explained by looking at the specificity and quality of their training. Both were built for one particular purpose, to play chess and to compete in Jeopardy!. Also in a later stage, Watson needed to be fed with a wealth of medical research and patient histories in order to be able to supply doctors with accurate treatments. The assumption that smart applications are superhuman all-rounders is thus vehemently inaccurate.

The only ones to fear the rise of cognitive systems are those who perform menial tasks. Sure, cognitive systems can process volumes of information in real time that we couldn’t even dream of but they need to be nurtured by human experts in order to perform accurately. Hence, HR cracks and doctors need not fear their digital supporters but rather welcome their disruptive and amplifying potential.