19 new Textbooks on HR Tech, Data & People Analytics

Dear visitor!

As you know the speed of the world has increased dramatically and the world wide web and the smart phone were important drivers for it.

Technology has also established itself as you know human resources management althought a little time later than in marketing for example. But HR tech or digital HR are not new in organizations anymore, and People Analytics as you may no has established itself as a cross-function in at least big organizations (more or less HR data science).

To keep up with developments in HR tech and HR & People Analytics and get inpiration for your work it is a good idea to exlore the book market from time to time. This I have done these days for you and me: I have collected in this newblog article 15 new books that cover these hot HR topics and which were published since June 2023.

Please note: The list is ordered by publication date (day-month-year) backwards; Translations into English with deepl.com; Images are embedded from their original source; If no remark in the note regardin a work all textbooks are published as first editions.

And: I will write some words or maybe a review like this about one or more of the selected works in the future. So stay tuned and connect with me on LinkedIn or contact me to keep in touch. Can’t wait? Then take a look on my previous newsblogs article with more cool HR tech stuff.

Let’s start!

Data-Driven Talent Management

Image: Kogan Page (embedded)

Basic information:

Author: Kristin Saling
Publication date: 03/08/2024 (Kogan Page), 27/08/2024 (amazon.de)
Publisher: Kogan Page
Language: English
Pages: 288
Price (print): 31.99 Euro (Paperback), 95.00 GBP (Hardback)
Website: https://www.koganpage.com/hr-learning-development/data-driven-talent-management-9781398615786

Note: A table of content is available online on the website. Kogan Page will be publish the textbook in August 2024 according to their website.

About the author:

Kristin Saling is Director at the Innovation Cell for the United States Army Human Resources Command where she enables the command to capitalize on the latest HR business practices and technologies.

See also this article about the unit: https://potomacofficersclub.com/news/us-army-using-data-ai-to-improve-workforce-management/

Source: Publisher; Web search

Predictive HR Analytics: Mastering the HR Metric

Image: Kogan Page (embedded)

Basic information:

Author: Daisung Jang, Martin Edwards, Kirsten Edwards
Publication date: 25/06/2024
Publisher: Kogan Page
Language: English
Pages: 536
Price (print): 50.50 Euro (Paperback), 170.86 GBP (Hardback)
Website: https://www.koganpage.com/hr-learning-development/predictive-hr-analytics-9781398615656

Note: A table of content is available online on the website. Kogan Page will be publish the textbook as the third edition in June 2024 according to their website.

About the authors:

“Martin R Edwards is an Associate Professor in Management at UQ Business School, University Queensland, Australia.

Kirsten Edwards is HR Lead for Advanced Analytics and Data Science at Rio Tinto and has over 20 years’ broad international experience in analytics, HR and management consulting, based in Queensland, Australia.

Daisung Jang has over 10 years’ experience of data analysis and is an expert on R. He is a lecturer in UQ Business School, University of Queensland, Australia.”

Source: Publisher

Creepy Analytics: Avoid Crossing the Line and Establish Ethical HR Analytics for Smarter Workforce Decisions

Image: McGraw Hill (embedded)

Basic information:

Author: Salvatore V. Falletta
Publication date: 23/02/2024
Publisher: McGraw-Hill Professional
Language: English
Pages: 288
Price (print): 29.29 Euro
Website: https://www.mhprofessional.com/creepy-analytics-avoid-crossing-the-line-and-establish-ethical-hr-analytics-for-smarter-workforce-9781265132675-usa

Note: Neither a book preview on the website is available, nor on Google Books. McGraw-Hill’s publishing date is shortly in one week as we read on its internet page.

About the author:

“Dr. Salvatore Falletta is a director and professor of Human Resource Leadership and Organizational Science at Drexel University. […] As a former chief human resources officer, head of Global HR Research and Analytics at a Fortune 100, and thought leader on this subject, Salvatore Falletta has witnessed first-hand the emergence of “creepy analytics” as a hot-button issue.”

Source: Publisher

HR Tech Strategy: Revolutionizing Employee Experience Through HR-Tech Synergy

Image: Business Expert Press (embedded)

Basic information:

Author: Marlene de Koning
Publication date: 30/01/2024
Publisher: Business Expert Press
Language: English
Pages: 214
Price (print): 34.92 Euro / 34.99 USD
Website: https://www.businessexpertpress.com/books/hr-tech-strategy-revolutionizing-employee-experience-through-hr-tech-synergy

Note: A Book preview from the title until page 23 plus “About the Author” and the back cover is available on the website.

About the author:

“Marlene de Koning is a director of HR Tech & Data at PwC, where she leads a team of HR tech and data specialists, who help clients drive workforce transformation, create data driven strategies and implement innovative technologies.”

Source: Publisher

Human Resource Management: People, Data, and Analytics

Image: Sage Publications (embedded)

Basic information:

Authors: Talya Bauer, Berrin Erdogan, David Caughlin, Donald Truxillo
Publication date: 19/01/2024
Publisher: Sage Publications
Language: English
Pages: 600
Price (print): 125.68 Euro (Loose-leaf), 157.39 Euro (Paperback)
Website: https://us.sagepub.com/en-us/nam/human-resource-management/book274799

Note: A table of contents of this second edition is available online on the website.

About the authors:

You can find detailed information about the authors on the website.

Recruiting Analytics: Mehr Erfolg mit Data Driven Recruiting und Talent Intelligence

Image: Haufe (embedded)

Basic information:

Authors: Marcel Rütten, Tim Verhoeven
Publication date: 02/01/2024
Publisher: Schäffer-Poeschel (part of Haufe Group)
Language: German
Pages: 168
Price (print): 49.99 Euro
Website: https://shop.haufe.de/prod/recruiting-analytics

Note: The table of contents of the book is available online and as a PDF on the website.

About the authors:

“Marcel Rütten has been working in HR management for over 15 years and is a widely known HR and recruiting expert as Global Talent Acquisition Lead, HR blogger and author. […]”

Source: Publisher


“Tim Verhoeven heads the Talent Intelligence team at Indeed in the DACH region and previously managed recruiting and personnel marketing at an international management consultancy. […]”

Source: Publisher



30 Open Data Sources – Not only for HR & People Analytics

Dear visitor!

Data are at the heart of our society, technology, and organizations. In fact without data, tools to collect and methods to analyze and interpret them our ancestors would not have been able to follow the traces of wild animals for food and to grow plants and breed animals later.

And in the course of this what later was named the First Agricultural Revolution (Neolithic Revolution) cities were build which led to further use of numbers: With taxation as one of the most important (and still is from the state points of view).

Tip: I have collected some important terms around data in German with links, so you might check this page out as well.

The Scientific Revolution

The progress continued slowly but surely and exploded at the end of the Middle Ages and the beginning of the Renaissance with data-based inventions and explorations by people like Nicolaus Copernikus (see this ARTE Documentary about him and his fellow Georg Joachim Rheticus), Johannes Kepler, Tycho Brahe, Galileo Galilei, as well as Isaac Newton and Gottfried Wilhelm Leibniz.

Exact and systematic observation of natural events, the collection of these data over a long period, thinking about these and developing theories, testing these with experiments as well as the exchange and correspondence with other researchers marked the start of the Scientific Revolution and its scientific method which paved the way to the Industrial and Digital Revolution on wich all our knowledge and wealth is based on until today.

Standing on the shoulders of giants”, as Google often quotes, is as true as the onegoing efforts of many people today to solve the micracles of the universe and to answer open questions regarding our earth, our economy, and technological challenges.

Bricks without clay

“Data! data! data!” he shouted impatiently. “I can’t make bricks without clay.”

These words come from the mouth of Sherlock Holmes and the short story “The Adventure of the Copper Bleeches” by Arthur Conan Doyle, which was first published in The Strand Magazine in June 1892 – and which also appeared in the anthology “The Adventures of Sherlock Holmes” in October of the same year.

But let’s the master detective from London himself speak:

Video: Created with D-ID based on an image from Stable Diffusion.

Although the famous detective from London is a fictional character, for his time and even today, his approach is a prime example of how to solve a difficult task, a puzzle and a case: through precise observation, the collection of data, scientific methods and logical reasoning (deduction). In other words, a forerunner of the data scientist!

And every data scientist or people analyst – like Sherlock Holmes – needs data! Data! Data! Fortunately, technological progress since the 1990s with hardware such as computers, chips, the internet, smartphones and increasingly powerful software has led to huge mountains of data from which the valuable “data ore” now needs to be mined (technically: data mining).

Data as ores for knowledge and wisdom

Even if data does not have quite the same material significance as oil or gold, a comparison we read more often, it is still central to making decisions and translating results into action when it comes to the right selection, cleansing of raw data (interesting: similar to ore as a metal or mineral mixture and raw material), analysis and visualization.

Tip 1: See also the data science pyramid (DIKW) with the levels from bottom to top: World → Data → Information → Knowledge → Wisdom; (see e.g. Herter, 2022, “Was ist Data Science?”, p. 25, in Wawrzyniak & Herter (Eds.), Neue Dimensionen in Data Science: Interdisziplinäre Ansätze und Anwendungen aus Wissenschaft und Wirtschaft, Berlin – Offenbach: Wichmann/VDE). Note: Michael Herter is CEO of Bonn based data science company infas 360 GmbH.

Tip 2: A short practical book on data science in German has written Michael Oettinger (2020).
And: For Germany you might take also a look on the work of the German Data Science Society (GDS e. V.) and their event German Data Science Days taking place from March 7-8, 2024 in Munich. See also upcoming events of the Data Science Summit.

Origin of data I: Classification

Anyone who practices data science or people analytics (HR data science) therefore needs data. But where exactly does it come from? What sources are there? And how available is it?

Of course, organizations today primarily generate mass data (big data) as well as smaller amounts of data (small data): Here, data can be differentiated according to who or what it basically comes from and where it originates, such as:

  • Data from nature and agriculture (e.g. weather, soil, animals, plants)
  • Data from technical systems and machines (e.g. power plants, factories, vehicles)
  • Data from the economy, corporate management and the financial sector (macroeconomic figures, key business figures, taxes).

And what I am interested in as an HR data scientist or people analyst: data from people. More precisely: data from people in organizations – i.e. from employees, managers and trainees (HR data).

There are a number of other ways of classifying data, such as raw data, aggregated data or metadata, according to data type or file format or authorizations. However, it is important that such data classification takes place in accordance with the existing guidelines and is checked over time.

This is always personal data under data protection law, as is the case with customer data or patient data, which is subject to special legal protection.

Personal data also includes data that can be used to identify a person with reasonable effort, such as the license plate number, the account number or the personnel number, which are often used in databases as so-called primary and foreign keys.

Origin of data II: Internal sources

But let’s leave these information technology and legal aspects behind and return to the initial question: Where does the data come from?

Because as I said: (HR) data science and people analytics need to develop and implement solutions to HR challenges: Data.

Fortunately, a large amount of data is collected and stored within an organization today, which, together with other internal or external data, is available to the employee or external service provider for analysis.

Well, in the case of data science or people analytics projects, we usually have access to this data – even if it often involves a lot of effort, communication and processing; as well as to the relevant data sources of interest from business and human resources management such as databases, data warehouses or data lakes (or other modern architectures such as data lakehouses or data meshes). The relevant data is often also available as files (flat files) in various formats (e.g. cvs, xls, xml, json).

The development of data systems, the storage and use of data (transformation, extraction) is summarized under the term data engineering, which has led to the profession of data engineer, as the complexity of IT systems and the challenges posed by big data, IT system landscapes, software diversity and cyber security, for example, have grown significantly in the last 10 years.

The data from Human Resource Management (HR data for short) includes, for example, personnel master data and applicant data, wage and salary data, data on sick leave and fluctuation, data on qualifications and further training (e.g. e-learning) or on job satisfaction and employee management.

In the case of internal data from other areas of the company, HR data science and people analytics projects may be interested in communication data, company figures or working hours (e.g. overtime), depending on the issue at hand.

However, there are situations in which we do not have access to company data, but still need it for testing, training or demonstration purposes. What can we do? The solution: Public or open data!

Tip: For a comprehensive overview of these internal data sources and data from third parties (external data), see the short and practical reference book by Steffi Rudel (2021).

Origin of data III: External sources

There are a lot of external sources for data available which allow access of public or open data.

However, while there are many Internet offerings for a lot of data from politics, society, the environment, transport and health, to name but a few, real data on human resource management is very rare for obvious reasons of data protection and company secrecy.

However, there are some real and fictitious HR data sets that can be used for various purposes for data science and data analysis. For example, for practicing and learning, for testing hypotheses or for comparison with your own HR data.

External data from public, general and special sources with data on the labor market, employer ratings, customer satisfaction, demographic characteristics or the industry and market are also used for specific questions in an HR data science or people analytics project.

Image: First page of our list “30 Open Data Sources 2024 – Not only for HR & People Analytics.”

Schorberg Analytics and Stefan Klemens have collected 30 sources of public and open data in a PDF, which also contains links to a number of HR datasets: If you are interested in this collection contact Stefan Klemens via contact form, e-mail or LinkedIn message. [Please connect there and like three of my latest post, if you have not yet, or comment on it. Friends and supporters of Schorberg Analytics and Stefan Klemens get the PDF of course immediately!].


70+ HR & People Analytics Vendors in 2024

Dear guest!

Do you as an HR leader think about implementing a new HR or People Analytics platform in your company? Or perhaps you you are running a bought or self-created Analytics system that does not fulfill actual and future HR demands anymore?

Then there is good news – and not that surprisingly when you have been in HR business some time. “To build or buy software” is the key sentence in this matter and there are many websites, guides, and consulting companies that support you with this challenge.

In the last month we at Schorberg Analytics have been researching the HR & People Analytics’ market to get an overview about data driven solutions that help reaching business and HR goals better and faster.

Our current list includes more than 70 vendors that offer more or less different tools for such tasks: big names, established names, and newcomers:

Valuable and always recommendable in chosing a HR tech partner is the work of Redthread Research and Stacia Sherman Garr (to name just one of their engaged staff members). So take a look on them!

Have a funny rest of the week!

(Note: Today starts in the Rhineland the Street Carnival where the woman take over the town halls: Altweibertag; So be aware if you wear a tie because it is in danger to be cut off.)

Stefan Klemens

PS: Do you want to exchange ideas about Human Resource Management, People Analytics, Digital Assessment, or Artificial Intelligence? Then network, send a message and/or schedule an online meeting. Or the classic way: a phone call.

And: Do you like my work and the content I regularly share? Then I’m happy about a Like or comment on LinkedIn. Thank you! 🙂 🙋‍♂️🌳


13 HR & People Analytics Conferences in 2024 you should check out

Dear guest!

As a reader of my newsblog you know that one task at Schorberg Analytics in the last two weeks was to create a list of HR tech and People Analytics conferences in 2024.

This job is finished by now and the final list contains more than 130 conferences, exhibitions, and some smaller events on these hot HR data based topics – including artificial intelligence in HRM as well as some selected analytics and big data summits (with relevance to HR data science / People Analytics).

Our list shows that people worldwide come together to talk about and exchange ideas on how to reach business and HR goals better by advanced data analytics: In Europe, the USA and Canada, Asia and Australia, and South America.

This blog article presents 13 HR and People Analytics Conferences in 2024 from our collection you should check out:

Update 17/02/2024: Added one more conference, so the sum is now 14 conferences!

HR Data Analytics & AI Summit

Date: February 26-27, 2024
Loaction: Atlanta, Georgia, USA
Website: https://www.hrotoday.com/events/hr-data-analytics-and-ai-summit

HR Analytics Summit DACH 2024

Date: March 10-12, 2023
Location: Berlin, Germany
Website: https://www.hr-analytics-summit.de

Wharton People Analytics Conference 2024

Date: March 14-15, 2024
Location: Philadelphia, USA
Website: https://wpa.wharton.upenn.edu/2024-conference

HR Analytics and AI

Date: March 20-22, 2024
Location: Long Beach, California, USA
Website: https://www.hr-analytics-summit.us

2024 INFORMS Analytics Conference

Date: April 14-16, 2024
Location: Orlando, Florida, USA
Website: https://meetings.informs.org/wordpress/analytics2024

Note: This is not a pure HR & People Analytics conference, but covers wider topics and industries on analytics (and data science). I added it later (and found out) because of Kristin Saling and her upcoming book “Data-Driven Talent Management” that I and 18 other titles wrote about in this newsblog article.

People Analytics World 2024 London

Date: April 17-18, 2024
Location: London, UK
Website: https://www.peopleanalyticsworld.com/2024-london

Swiss HR Analytics

Note: First date in 2024, other dates in June and September (usually the last Thursday). Check their website for news.

Date: April 25, 2024
Location: Online
Website: https://swisshranalytics.ch/240425

9th Annual People Analytics Canada Summit

Date: May 14-15, 2024
Location: Toronto, Canada
Website: https://www.peopleanalyticscanada.com

People Analytics & HR Data Summit

Date: June 12-13, 2024
Location: Sydney, Australia
Website: https://www.peopleanalytics-summit.com

People Analytics Day 2024: Unlocking the Power of People Analytics

Date: June 13, 2024
Location: Gentofte-Hellerup (near København), Denmark
Website: https://www.noca.dk/en/noca-kalender/kommende-aktiviteter/people-analytics-en/people-analytics-day-2024

People Analytics Exchange

Date: June 25-27, 2024
Location: Minneapolis, USA
Website: https://www.hrexchangenetwork.com/events-people-analytics-exchange-june

HR Analytics Summit

Date: September 4, 2024
Location: London, UK
Website: https://www.hranalyticssummit.com

HR Leaders People Analytics Summit 2024

Date: September 19, 2024
Location: Online
Website: https://hrleaders.co/peopleanalyticssummit

HR Analytics & AI Summit Europe 2024

Date: November 24-26, 2024
Location: Berlin, Germany
Website: https://www.hr-analytics-summit.com

More conferences and exhibitions in 2024

There are of course more HR and HR tech conferences and exhibitions in 2024 that cover a greater variety of topics, but can include (and many do) HR & People Analytics. A list about HR tech conferences and exhibitions will follow in another newsblog article. Connect via LinkedIn to stay tuned or write me a message via the contact form. Thank you!


30 People & HR Analytics Groups on LinkedIn

Dear visitor!

Being on LinkedIn (as I assume) you know certainly about the groups around there and are probably a member in one or more yourself.

And maybe LinkedIn groups to exchange ideas and to connect with mind-like people are even a main reason for your profile in this business network, especially for those in Germany that left XING because it deleted all groups by the end of 2022.

But with 1+ Billion members on LinkedIn worldwide and an estimated number of 2 to 3 Million LinkedIn groups according to my internet research it may be a take a little time and effort to find the right group for you. Since I am working in Human Resources Management and focusing on People Analytics, AI, and Digital Assessment I am a member and interested in groups with these topics – as you are maybe also if HR Data is part of your job.

Yesterday I searched LinkedIn systematically for People & HR Analytics groups to get an overview, inspirations (for a planned local meetup), and further input for our upcoming Schorberg Analytics’ Market Report on People Analytics and AI in HRM (check this newsblog for further information and publication date).

The results: There were 1,000 entries on such related groups on 100 pages (+ many more with “HR Analytics” as a search term), but fortunately LinkedIn presented the relevant ones according to the number of members on the first 10 to 15 pages. I put the names of a group in a table with their links and language, and sorted them from the highest number of members downwards.

But why do I tell you that? Well, perhaps you want to get an overview about these groups too. And to safe you time and work I will send you the list in PDF for free via a personal message on LinkedIn, contact form or e-mail!

So that I know that you want to have it just like or comment on my LinkedIn post (And if you are not yet in my network: Please connect also in order to sent it to you) – or contact me.

I wish you a great weekend!

Stefan Klemens

PS: Want to exchange ideas on Human Resources, people analytics, digital assessment, or artificial intelligence in HRM? Then network, write a message and/or make an appointment for an online meeting. Or the classic way: phone call.

And: You like my work and the content I regularly share? Then I’m happy about a Like or comment on LinkedIn. Thank you! 🙂 🙋‍♂️🌳

Foto: Stefan Klemens (showing the skyline of the Düsseldorf Medienhafen in northern direction with the Rhine Tower)


HR Data Science: Salary Prediction at Trivago and StepStone

Dear guest!

On Tuesday evening, 29th of August 2023, I was at the Düsseldorf Data Science Meetup at Trivago, the Online search company for hotels, in their extraordinary headquarters in the Medienhafen of Düsseldorf, my birth city.

I left my office in Solingen early in the afternoon that day since I met with my business friend Dominik Rühl before the event – and walked in the sun from the Düsseldorfer Landtag (state parliament) at the Rhine to our meeting point at UCI cinema near Trivago.

Stefan Klemens with Dominik Rühl at the meetup. Foto: Stefan Klemens

It was good to see Dominik after about five years – And we had a fruitful exchange on our common topics artificial intelligence (AI), recruitment, skills, and digital assessment as well as some private issues. He is now working as a HR & Recruiting Manager at Advance Business Partner GmbH based nearby in the city of Neuss on the other side of the Rhine. The consulting company focuses on mobility services in different areas like recruitment, innovation, and transformation management.

Although the summer and weather this year in Germany is pretty unstable, we enjoyed sitting outside with our drinks at unique brewery bar Eigelstein.

Find out more about the Düsseldorf Data Science Meetup Group with its interests in Data Science, Machine Learning and Python/R, on this website.

Arriving at Trivago

The Trivago building as seen from the north-east of the Medienhafen Düsseldorf. Foto: Stefan Klemens

At 6 pm it was time to walk to nearby Trivago building, finished in 2018. The individual modern styled entrance area and the café behind offers a glimpse on how the interior of the building is decorated (see this article and this article about the New Work culture at Trivago and the architecture of the headquarter´s spaces.)

Surprisingly we, with another guest, were the first participants arriving (ok, it was half our before the official start and talks started even later), but were soon picked up by Gina from Trivago. Together we (and a cart full of pizza in yellow boxes for the data people) were lifted by one of the elevators to the top floor for the location event.

A stunning view to the south-west skyline from the roof terrace reached our eyes, and Dominik, the coming participants, and me enjoyed drinks and pizza before the event started at 7 pm.

Our co-host Aida Orujova gave us a very warm welcome, she introduced the speakers, and broke the ice by asking who is from data science, who is from engineering, and who just there to know more about salaries.

Co-host and moderator of the evening Aida Orujova welcoming the Data Science crowd. Foto: Stefan Klemens (with approval of the her)

First talk: Alexander Fischer, Trivago

Alexander Fischer from Trivago started with his talk about is passion for the programming and statistics software R, and his (and the economists´) “Swiss knife” methodical approach for prediction outcome variables: Linear Regression. He showed how he and his team used this classical algorithm with packages R´s fixest, and PyFixest to predict wage by using the variables education and ability (e.g. intelligence).

In his presentation of the problem in doing that (“The error term is correlated with the dependent variable”) he referred to a recent study using data from 59,000 Swedish men published 2023 by Marc Keuschnigg, Arnout van de Rijt, and Thijs Bol in the European Sociological Review (number 20, pages 1-14), titled “The plateauing of cognitive ability among top earners” (online article published here on January 28, 2023).

Since AB-Testing (or randomized experimental and control group design) is not feasible in the model (sending randomized individuals in one group for example one year more to college) the classical solution in Social Sciences and Psychology are Quasi-Experiments which were first introduced in the literature by standard book “Quasi-Experimentation: Design and Analysis Issues for Field Settings” written by Cook and Campell (1979).

As a solution for not manipulation experimental the years of education as predictor of the wage Alexander used therefore a variable called “distance to college” as a natural differing factor between people regarding their years of education.

The data scientist from Trivago further pointed out in his “The Secret Sauce” slide that taking the role of companies into account in the corresponding regression model, the computation is quite demanding (millions of employees, thousands of companies, 20 years of data) – But he presented of course a solution for it (and that was not Spark!).

At the end with the help of programming language Python and package PyFixest Alexander showed that the prediction of salary can be done, and he answered the questions of the audience.

Second talk: Michael Matuschek & Tim Elfrink, StepStone

In the second talk this evening we learned from Michael Matuschek and Tim Elfrink how StepStone is predicting the salaries of all kinds of jobs for their salary products.

Michael begun the session, and gave an overview about StepStone´s salary products include the Salary Planer, Salary on Listings, and Auto-generated Salary SEO pages.

As a result of a 2020 study and further research before it turned out that salary is for 96 % of the respondents the most important criteria when choosing a job (flexible working ours, career & training opportunities, and corporate culture, reach only 90 % resp. 91 %).

Michael Matuschek with Tim Elfrink from StepStone answering a question from the audience. Foto. Stefan Klemens. Thanks both for their approval of the picture!

Michael told us also about the challenges in prediction salary at StepStone regarding data distribution and features (more white collar jobs and little part-time data for example) and: The gender pay gap, quality assurance, feature engineering, the underlying model and the used algorithm, as well as the metrics (main business KPIs) accuracy and generalisation.

After him Tim Elfrink took the mic and explained the broader infrastructure of the predicting IT system with AWS and the auto deployment of the model. Further subtitles of his presentation were for example: Creating scalable infrastructure and development environment.

A number of questions (and some hints for improving their model) came from the participants, and Michael and Tim were happily answering them.

Closing, socks, and outlook

At 8.30 pm presenter Aida Orujova returned to the stage again and thanked all guests and speakers for being there. As several others I took the chance to talk with some participants (see header picture), before I needed to catch my tram to travel home.

Trivago-Logo in front of the building after sunset. Foto: Stefan Klemens

My second Düsseldorf Data Science Meetup was another wonderful experience (read about my first here), and the scheduled next event in October 2023 is of course on my list.

Oh, one last thing (we learned this from the apple guy, right?) I did not mention yet. Before the start the participants could grab one, two, or three promotional gifts from Trivago as shown in the picture: One for using your hand to write (still common among a few people I was told), one for storing big data in a small piece of metal, and one to keep your feet between 28 ° C and 33 ° C (surface temperature of the extremities as I learned writing this sentence) when external temperatures fall in later autumn.

Promotional gifts for the participants of the Düsseldorf Data Science Meetup from Trivago. Foto: Stefan Klemens

As I like to test digital and analogue things (I have high scores on openness to experience (see the Big Five Personality Traits) and curiosity which is one of my signature strengths according to the VIA-Model), the usefulness of the trivagonian socks to prevent cold toes needed to be proven also.

Note: If you like to know more about psychological traits and psychometric assessment of these for HR recruiting, selection, and development, then click on my work as a Work Psychologist as presented here: https://www.digitalassessment.de/

I can say that my feet got warmer but the real test of course – and perhaps then like a case study (N = 1) with more treatments like a stepstonian, a sipgatian, and quantopian fabric as well a control (no treatment, that is walking without socks! preparing for that right know!) – will be conducted in colder times which are coming soon to Germany. I will report on it! 😉 And perhaps you wanna join the experiment to lift the “N”, so results will be more valid?

Me testing the Trivago socks: And I am smiling realizing the double meaning of the words and symbols matching the two main areas of the company. Foto: Stefan Klemens

With this of course rather funny ending, I thank very much the organizers and speakers for this evening, and Trivago for hosting the meeting! Will we see us next time on a Düsseldorf Data Science Meetup (or another place if you like)?

Many greeting and all the best to you!

Stefan Klemens

PS: Want to exchange ideas on people analytics, digital assessment or artificial intelligence in HRM? Then network, write a message and/or make an appointment for an online meeting. Or the classic way: phone call.

And: You like my work and the content I regularly share? Then I’m happy about a Like or comment on LinkedIn. Thank you! 🙂 🙋‍♂️🌳