Tindak Lanjut Kesetaraan Upah, atau Sosok bayangan tersembunyi yang sering kita abaikan

Tulisan ini merupakan kelanjutan dari artikel kami sebelumnya tentang Gender Pay Gap (GPG), di mana kami menyarankan bahwa fokus terhadap kesenjangan upah berdasarkan gender saja tidaklah cukup dan mengalihkan fokus pada konsep kinerja akan lebih bermanfaat. Sebagai kelanjutannya, kami beralih ke topik fast fashion dan mendiskusikan ‘sosok bayangan’ yang ada di mana-mana ini, termasuk dari sudut pandang kesetaraan upah.

Dicari: tenaga kesehatan – tetapi mengapa lowongan ini tidak terpenuhi?

Terlepas dari adanya peningkatan, masih terdapat kesenjangan yang signifikan antara penawaran dan permintaan staf perawatan kesehatan pada tahun 2029 di Swiss, menurut laporan nasional tahun 2021 tentang kebutuhan tenaga perawatan kesehatan di masa depan, yang diterbitkan oleh Swiss Health Observatory pada bulan September. Laporan tersebut memperkirakan bahwa pada tahun 2029, permintaan tenaga kerja di sektor perawatan kesehatan dapat meningkat menjadi 222.100. Dibandingkan dengan jumlah dasar 185.600 staf yang tercatat pada tahun 2019, jumlah tambahan 36.500 staf akan dibutuhkan.

Menyambut Trond Henning Olesen sebagai Wakil Presiden Integrasi Pelanggan dan Penjualan Solusi terbaru kami

Dengan bangga kami mengumumkan bahwa Trond Henning Olesen akan bergabung dengan JANZZ.technology, yang berbasis di San Francisco, sebagai Wakil Presiden baru untuk Integrasi Pelanggan dan Penjualan Solusi. Dia akan bertanggung jawab untuk semua pelanggan di Amerika Utara dan Selatan, EMEA dan Asia.

Trond adalah ahli strategi, teknolog, dan penggemar startup yang sangat berpengalaman. Dengan lebih dari 20 tahun kepemimpinan global dan pengalaman penjualan di industri teknologi, serta gelar PhD di bidang Ilmu Komputer, Trond membawa jejak yang mengesankan dalam membangun tim yang berpusat pada pelanggan, meluncurkan bisnis baru, dan mencapai kesuksesan operasional.

Sepanjang karirnya, Trond telah membangun perusahaan dari awal hingga IPO yang sukses, mencapai pertumbuhan puncak, perubahan haluan, dan kepuasan pelanggan yang tinggi dalam situasi pasar yang beragam. Dia juga telah melayani akun-akun utama dan mengelola proyek-proyek berskala besar yang kompleks di seluruh dunia, memimpin tim secara efektif untuk membawa perubahan mendasar dan peningkatan dalam strategi, proses, dan fokus pelanggan. Dengan keahlian teknis dan bisnisnya yang luas, Trond telah bertindak sebagai konsultan untuk perusahaan seperti LinkedIn dan Purisma, secara pribadi melatih karyawan tingkat C dan mendukung mereka dalam meningkatkan organisasi, proses, dan karyawan mereka. Baru-baru ini, dia adalah salah satu pendiri dan CTO dari startup Silicon Valley VeraScore, di mana dia memimpin tim teknis, berpartisipasi dalam pengembangan dan memberikan kepemimpinan teknis untuk semua aktivitas penjualan.

Trond sangat antusias dengan kinerja tinggi, teknologi pencocokan pekerjaan yang digerakkan oleh AI dan solusi pasar tenaga kerja yang ditawarkan oleh perusahaan Swiss JANZZ.technology kepada perusahaan dan lembaga pemerintah di seluruh dunia. Pada saat terjadi perubahan struktural besar di pasar tenaga kerja, Trond sangat antusias dengan kesempatan untuk bekerja dengan klien global guna memberi mereka solusi digital yang dirancang dengan sempurna untuk manajemen bakat dan pasar tenaga kerja yang efektif.

“Dengan perpaduan latar belakang teknis yang kuat dan keahlian dalam strategi dan kesuksesan pelanggan, Trond adalah tambahan yang sangat baik untuk tim kami,” kata Stefan Winzenried, CEO JANZZ.technology. “Trond akan mempercepat pertumbuhan JANZZ dan terus memajukan misi kami untuk memberikan layanan yang lebih baik kepada pelanggan kami. Kami sangat senang Trond bisa bergabung dengan perusahaan kami.”

The One-Eyed Leading the Blind – Mengapa Anda membutuhkan lebih dari sekadar data science dan machine learning untuk menciptakan pengetahuan dari data

Banyak mesin pencocokan dan rekomendasi pekerjaan yang saat ini ada di pasaran didasarkan pada Machine Learning (ML) dan dipromosikan sebagai revolusi teknologi SDM. Namun, terlepas dari semua upaya yang dilakukan untuk meningkatkan model, pendekatan, dan data selama dekade terakhir, hasilnya masih jauh dari apa yang diharapkan oleh pengguna, pengembang, dan ilmuwan data. Namun, konsensus yang ada tampaknya menunjukkan bahwa jika kita mendapatkan lebih banyak lagi data dan model yang lebih baik, serta meluangkan lebih banyak waktu, money and data scientists at the problem, we will solve it with machine learning. This may be true, but there is also ample reason to at least think about trying a different strategy. A lot of very clever people having been working very hard for many years to get this approach to work, and the results are – let’s face it – still really bad. In this series of posts, we are going to shed some light on some of the pitfalls of this approach that may explain the lack of significant improvement in recent years.

One of the key aspects that many experts have come to realize is that because job and skills data is so complex, ML-based systems need to be fed with some form of knowledge representation, typically a knowledge graph. The idea is that this will help the ML models better understand all the different terms used for job titles, skills, trainings and other job-related concepts, as well as the intricate relationships between them. With a better understanding, the model can then provide more accurate job or candidate recommendations. So far, so good. So the data scientists and developers are tasked with working out how to generate this knowledge graph. If you put a group of ML experts in a room and ask them to come up with a way to create a highly complex, interconnected system of machine-readable data, what do you think their approach will be? That’s right. Solve it with ML. Get as much data as you can and a model to work it all out. However, there are multiple critical issues with this approach. In this post, we’ll focus on two of these concerns that come into play right from the start: you need the right data, and you need the right experts.

The data

ML uses algorithms to find patterns in datasets. To discern patterns, you need to have enough data. And to find patterns in the data that more or less reflect reality, you need data that more or less reflects reality.  In other words, ML can only solve problems if there is enough data of good quality and an appropriate level of granularity; and the harder the problem, the more data your system will need. Generating a knowledge graph for jobs and associated skills with ML techniques is a hard problem, which means you need a lot of data. Most of this data is only available in unstructured and highly heterogeneous documents and datasets like free text job descriptions, worker profiles, resumes, training curricula, and so on. These documents are full of unstandardized or specialist terms, synonyms and acronyms, descriptive language, or even graphical representations. There are ambiguous or vague terms, different notions of skills, jobs, and educations. And there is a vast amount of highly relevant implicit information like the skills and experience derived from 3 years in a certain position at a certain company. All this is supposed to be fed into an ML system which can accurately parse, structure and standardize all relevant information as well as identify all the relevant relationships to create the knowledge graph.

Parsing and standardizing this data is already an incredibly challenging task, which we’ll discuss in another post. For now, let’s suppose you know how to build this system. No matter how you design it, because it’s based on ML to solve a complex task, it’s going to need a lot of data on each concept you want to cover in your knowledge graph. Data on the concept itself and on its larger context. For instance, it will need a large amount of data to learn that data scientists, data ninjas and data engineers are closely related, but UX designers, fashion designers and architectural designers are not. Or that CPA is an abbreviation of Certified Public Accountant, which has nothing in common with a CPD tech in a hospital.

This may be feasible for many common white-collar jobs like data scientists and social media experts, because their respective job markets are large and digitalized. But how much data do you think is out there for cleaner/spotters, hippotherapists or flavorists? You can only solve a problem with ML that you have enough data for.

The experts

Let’s suppose (against all odds) that you solved the data problem. You can now choose one of three possible approaches:

  1. Eyes wide shut: Build the system, let it generate the knowledge graph autonomously and feed the result into your job recommendation engine without ever looking at it.
  2. Trust but verify: Build the system, let it generate a knowledge graph autonomously and fine tune the result with humans.
  3. Guide and grow: Build the system and let it generate a knowledge graph using human input along the way.

Based on what’s currently on the market, one wonders if most knowledge graphs in HR tech are built on the eyes wide shut approach. We have covered the results of several such systems in previous posts (e.g., here, here and here). You may also have come across recommendations like the ones below yourself.

 

JANZZ.technology          JANZZ.technology         JANZZ.technology

 

If not, it may be that the humans involved in the process are all data scientists and machine learning experts, i.e., people who know all about building the system itself, instead of domain experts who know all about what the system is supposed to produce. Whether you fine tune the results at the end or give the system input or feedback along the way, at some point, you will have to deal with domain questions like the difference between a cleaner/spotter and a (yard) spotter, what exactly a community wizard is, or whether a forging operator can work as an upset operator. And then of course, all the associated skills. If you want a job matching engine to produce useful results, this is the kind of information your knowledge graph needs to encode. Otherwise you just perpetuate what’s been going on so far, namely:

 

JANZZ.technology

 

You simply cannot expect data scientists to assess the quality of this kind of knowledge representation. Like IKEA said at the KGC in New York: domain knowledge takes years of experience to accumulate—it’s much easier to learn how to curate a knowledge graph. And IKEA is “just” talking about their company-specific knowledge, not domain knowledge in a vast number of different industries, different specialties, different company vocabularies, and so on. You need an entire team of domain experts from all kinds of different fields and specialties to assess and correct a knowledge graph of this magnitude and depth.

Finally, what if you want a knowledge graph that can be used for job matching in several languages? Again, an ML expert may think there’s a model or a database for that. Let’s look at databases first. Probably one of the most extensive multilingual databases for job and skills terminology is the ESCO taxonomy. However, apart from not being complete, it is riddled with mistakes. For instance, one of the skills listed for a tanning consultant is (knowledge of) tanning processes. Sound good, right? However, if you look at the definitions or the classification numbers for these two concepts, you see that a tanning consultant typically works in a tanning salon while tanning processes have to do with manufacturing leather products. There are many, many more examples like this in ESCO. Do you really want to feed this into your system? Maybe not. What about machine translation? One of the best ML-based translators around is DeepL. According to DeepL, the German translation of yard spotter is Hofbeobachter. If you have very basic knowledge of German, this may seem correct because Hof = yard and Beobachter = spotter. But Hofbeobachter is actually the German term for a royal correspondent, a journalist who specializes in reporting on royalty. A yard spotter, or a spotter, is someone who typically moves or directs, checks and maintains materials or equipment in a yard, dock or warehouse. The correct German term would be Einweiser, which translates to instructor or usher in DeepL. There is a simple explanation for these faulty translations: there is just not enough data connecting the German and English terms in this specific context. So, you need an entire team of multilingual domain experts from different fields and specialties to assess and correct these translations – or just do the translations by hand. And simply translating isn’t enough. You need to localize the content. Someone has to make sure that your knowledge graph contains the information that a carpenter in a DACH country has very different qualifications to a carpenter in the US. Or that Colombia and Peru use the same expressions for very different levels of education. Again, this is not a task for a data scientist. Hire domain experts and teach them how to curate a knowledge graph.

Of course, you can carry on pursuing a pure ML/data science strategy for your HR Tech solutions and applications if you insist. But – at the risk of sounding like a broken record – a lot of very smart people having been working on this for many years with generous budgets and no matter how hard the marketing department sings its praise, the results are still appalling. Anyone with a good sense of business should realize that it’s time to leave the party. If you’re still not convinced, keep an eye out for our next few posts in this series. We’re going to take down this mythical ML system piece by piece and model by model and show you: if you want good results anytime soon, you’ll need more than just data science and machine learning.

“Para penumpang yang terhormat, mohon perhatiannya…” Tentang tanggung jawab konsumen

 

Simak artikel terakhir kami dalam seri tentang peristiwa terkini di pasar tenaga kerja, yang ditulis dari sudut pandang petugas bandara. Kami merangkumnya dengan beralih ke persoalan tekanan harga dan masalah tanggung jawab konsumen yang terkait, dan mengilustrasikannya dengan contoh industri penerbangan yang sedang mengalami kesulitan. Dengan demikian, kami juga menunjukkan bahwa permasalahan di sektor ini terjadi di mana-mana dan harus menjadi perhatian kita bersama – tidak hanya karena dampaknya terhadap lingkungan. 

 

Dear readers,

Over the past few weeks, I have followed with great interest (and often also a dash of horror) the reporting by various media channels on the ongoing crisis situation in the aviation industry that is caused by a shortage of staff. Even though I did not always agree with everything that was said – especially in the comments sections – I feel that it is very important that this matter is being addressed publicly. (Disclaimer: I myself have been working as a baggage handler in this very industry for many years, have a lively exchange with my work colleagues at the airport and am therefore speaking from actual experience). In said news contributions, I have read, seen or heard almost everything, from management salary analyses to insurance tips for travelers. However, one, in fact obvious, aspect of the whole mess seemed to be missing in the conversation. You may have already guessed it, I’m referring to the role of consumers, who coincidentally (?) often also belong to the target group of cited media. In the following, I would therefore like to say a few words on this subject. Before you now dismiss it, because you do not feel addressed as a non- or non-frequent flyer, I would like to add this preliminary remark: My experience in the airline industry is just one instance of a problem that extends far beyond into other areas. No matter whether we talk about aviation or the import of cheap goods. The luxury of some people always comes at the expense of others. And be aware that we look at this very fact through the lens of the rich developed nation of Switzerland – what it means for other countries, most people do not even want to imagine.

One more preface: I would really like to thank those who publicly speak out for better working conditions and wages in my industry. This verbal support is an important start to improving circumstances in sectors and professions like mine. But it is also just that; verbal and a start. Is it laudable to use your voice to stand up for workers who due to the holiday season have other things to do than argue about long waiting times in the comments? Or to say thank you to them? Absolutely. But unfortunately, at the end of the day, I still get very little out of it if my salary  is based on the (still applicable) crisis collective wage agreement and allows me to live just above the poverty line. In addition, the authors of such statements lose a great deal of sincerity and credibility in my eyes when in real life you still end up encountering them hunting for the cheapest bargain or loudly proclaiming their annoyance over a lost suitcase. It’s like “I really do think that those poor bastards who carry my luggage deserve more. But…”. As soon as they get to feel the pain themselves, solidarity comes to an abrupt end for many people. Even better, of course, are those who don’t even think about people like me and just think that “it’s so cool” to party every weekend in a different European city…

In the current discussion about aviation, you can hear a lot of moaning about the mismanaging, greedy executives of the airlines and airports. Believe me, I probably agree with you on many points there. Of course, a general price increase will not guarantee an automatic positive impact on employees’ wages. But this does not change the fact that most people are not willing to pay higher prices for their flights in the first place – not always just because they could not afford it. After all, I’ve already stumbled over the word “human right” several times in relation to flying nowadays. “Fun” fact from the TV program Kassensturz: In order to cover all the costs actually incurred, air ticket prices would actually have to be at least twice as high. These actual costs refer to the circumstance that the airline industry still benefits from a number of outdated special rights, resulting in reduced costs: Unlike for cars, for example, there is no mineral oil tax for airlines. And unlike train tickets, no value-added taxes are levied on plane tickets.

If the calls for fairer wages from those outside the industry are meant to be sincere, the real cost of air travel in the broader sense should also include the almost universally mediocre pay of many employees in my industry. Since the beginning of the 1980s and the worldwide deregulation of commercial aviation, flying has become steadily cheaper, so it is no wonder that today’s consumers have become very sensitive to cost increases – they are not used to anything other than low-cost carriers. But they should be (or become) aware that the price pressure they induce is often passed on unfiltered to the staff in the air and on the ground. As I said, there can’t only be winners in such equations. Just as a small example: A cabin crew member in Switzerland earns between CHF 3,145 and 4,500 gross, depending on seniority. After social deductions and health insurance, this is hardly enough to live on, especially if you are not allowed to live more than an hour away from the airport because of your job. Let alone to provide for old age. It’s therefore not surprising that it’s becoming increasingly difficult to find people who will work under such conditions. Clearly, it is also up to those “price-sensitive” customers to recognize their responsibility for the consequences of their demands and to redefine their point of view. Not to mention the hypocrisy of certain passengers who claim to environmentally compensate their flight to Bali with a Meatless Monday and some fair fashion…

And one more thing: As is already becoming apparent, it is not only the airline industry’s environmental impact that ultimately affects us all. What the situation at many international airports is showing us right now is that stingy behavior that is neither ecologically nor economically sustainable will ultimately backfire. Sooner or later not only the ‘direct losers’ in the equation will have to bear the resulting costs, but the general public as well. Be it in the form of delays, lost luggage or when taxpayers’ money has to be used to pay the health insurance premium subsidies of underpaid stewardesses and the national pension scheme has ever larger funding holes due to the inadequate salaries. We are also already seeing what the consumers’ reaction to the cancelled flights can bring: many people switch to driving by car (not to trains that would have been more climate-friendly) and traffic jams are currently forming the likes of which we have not seen for a long time. Now whether that’s better, you tell me.

That is why, ladies and gentlemen, I plead for more consumer (co-)responsibility, also in the case of aviation. Of course, it is easier and more pleasant to deny any involvement in problematic developments and to point fingers at others or to describe everything as a structural problem (and therefore unsolvable by the individual). But this does not resolve the issue and is of limited help to the employees directly affected by the crisis. The scenario is similar to that in other sectors of the economy; just think of the food service sector or the fast-fashion paradox. In my opinion, being unaware (or is it calculated ignorance?) does not fully shield you from complicity, and as long as I continue to overhear passengers getting upset about the smallest increases in fees and prices, I cannot take their appreciation and compassion 100 percent seriously. Therefore, dear consumers, please do care, because you will feel the effects of the current mess not only during the next strike.

Mario V., Reader [1]

 

At JANZZ, it is not only important to us that the best job candidates, regardless of location and industry, get the best match with an advertised position, but also that they are compensated appropriately for their work. This is one of the many reasons why we are a trusted partner to an ever-growing number of Public Employment Services (PES) in various countries around the world. We develop evidence-based solutions and have been successfully deploying them since 2010. Our job and skills-matching solutions are fair and non-discriminatory and provide completely unbiased results in accordance with the OECD principles on AI.

Would you like to take a step towards fairer labor markets? Then contact us at info@janzz.technology, via our contact form or visit our product page for PES.

 

[1] Mario V. is a fictional character from our series on current events in aviation and their impact on the labor market. See here for Part 1 and Part 2. Any similarities with real persons, living or dead, are purely coincidental.