There are many tools to connect virtually with your coworkers. Think of Teams, Zoom, Google Meet, or Slack. And during the recent pandemic, we have seen their usage surge. Yet, most of these tools try to recreating the office experience using video conference calls.
At Shopify, Daniel Beauchamp and his team took a different approach. They created SHOPIFY PARTY: a fullblown virtual world designed for social play and hanging out.
Here, Shopify employees can now play games during their 1:1s, standups, and other team events. They can hold boat races, log jumps, dance contests, exploration hikes, or just chill with their coworkers by a virtual campfire.
This must provide an incredible boost for the employee experience, well-being, and for forming workplace relationships in general!
The virtual environment is created in Unity and runs right in the webbrowser through use of WebGL.
A few years back I completed my dissertation on data-driven Human Resource Management.
This specialized field is often dubbed HR analytics, for basically it’s the application of analytics to the topic of human resources.
Yet, as always in a specialized and hyped field, diifferent names started to emerge. The term People analytics arose, as did Workforce analytics, Talent analytics, and many others.
I addressed this topic in the introduction to my Ph.D. thesis and because I love data visualization, I decided to make a visual to go along with it.
So I gathered some Google Trends data, added a nice locally smoothed curve through it, and there you have it. As the original visual was so well received that it was even cited in this great handbook on HR analytics. With almost three years passed now, I decided it was time for an update. So here’s the 2021 version.
If you would compare this to the previous version, the trends look quite different. In the previous version, People Analytics had the dominant term since 2011 already.
Unfortunately, that’s not something I can help. Google indexes these search interest ratings behind the scenes, and every year or so, they change how they are calculated.
In my dissertation, I wrote the following on the topic:
This process of internally examining the impact of HRM activities goes by many different labels. Contemporary popular labels include people analytics (e.g., Green, 2017; Kane, 2015), HR analytics (e.g., Lawler, Levenson, & Boudreau, 2004; Levenson, 2005; Rasmussen & Ulrich, 2015; Paauwe & Farndale, 2017), workforce analytics (e.g., Carlson & Kavanagh, 2018; Hota & Ghosh, 2013; Simón & Ferreiro, 2017), talent analytics (e.g., Bersin, 2012; Davenport, Harris, & Shapiro, 2010), and human capital analytics (e.g., Andersen, 2017; Minbaeva, 2017a, 2017b; Levenson & Fink, 2017; Schiemann, Seibert, & Blankenship, 2017). Other variations including metrics or reporting are also common (Falletta, 2014) but there is consensus that these differ from the analytics-labels (Cascio & Boudreau, 2010; Lawler, Levenson, & Boudreau, 2004). While HR metrics would refer to descriptive statistics on a single construct, analytics involves exploring and quantifying relationships between multiple constructs.
Yet, even within analytics, a large variety of labels is used interchangeably. For instance, the label people analytics is favored in most countries globally, except for mainland Europe and India where HR analytics is used most (Google Trends, 2018). While human capital analytics seems to refer to the exact same concept, it is used almost exclusively in scientific discourse. Some argue that the lack of clear terminology is because of the emerging nature of the field (Marler & Boudreau, 2017). Others argue that differences beyond semantics exist, for instance, in terms of the accountabilities the labels suggest, and the connotations they invoke (Van den Heuvel & Bondarouk, 2017). In practice, HR, human capital, and people analytics are frequently used to refer to analytical projects covering the entire range of HRM themes whereas workforce and talent analytics are commonly used with more narrow scopes in mind: respectively (strategic) workforce planning initiatives and analytical projects in recruitment, selection, and development. Throughout this dissertation, I will stick to the label people analytics, as this is leading label globally, and in the US tech companies, and thus the most likely label to which I expect the general field to converge.
My former colleague at Tilburg University, dr. Brigitte Kroon, summarizes decades of scientific evidence in the field of human resource mangement in her new book – Evidence-based HRM.
She published it open access, so everyone can access it for free.
Brigitte explains what science can (and can not) tell us about the most effective ways to organize and treat people in the workplace. She was able to nicely distill the practical insights from the theoretical frameworks and perspectives.
Human Resource Management is about managing the labor side of organizations. As labor resides in people, managing labor involves managing people. Because people can think and act in response to management, effective management of people involves a good understanding of psychology, sociology, laws, and economics. Any person in a managerial position should therefore have some basic understanding of human resource management. However, since not every organization is the same, and because the challenges that organizations face are different, there is no ‘one best practice suits all’ recipe for doing HRM. Hence, organizations need people who know where to find the best HRM interventions for the issues that they face.
The captivating story of mathematics’ greatest ever idea: calculus. Without it, there would be no computers, no microwave ovens, no GPS, and no space travel. But before it gave modern man almost infinite powers, calculus was behind centuries of controversy, competition, and even death.
Taking us on a thrilling journey through three millennia, Professor Steven Strogatz charts the development of this seminal achievement, from the days of Archimedes to today’s breakthroughs in chaos theory and artificial intelligence. Filled with idiosyncratic characters from Pythagoras to Fourier, Infinite Powers is a compelling human drama that reveals the legacy of calculus in nearly every aspect of modern civilisation, including science, politics, medicine, philosophy, and more.
Imagine a world where your phone is too big for your hand, where your doctor prescribes a drug that is wrong for your body, where in a car accident you are 47% more likely to be seriously injured, where every week the countless hours of work you do are not recognised or valued. If any of this sounds familiar, chances are that you’re a woman.
Invisible Women shows us how, in a world largely built for and by men, we are systematically ignoring half the population. It exposes the gender data gap–a gap in our knowledge that is at the root of perpetual, systemic discrimination against women, and that has created a pervasive but invisible bias with a profound effect on women’s lives. From government policy and medical research, to technology, workplaces, urban planning and the media, Invisible Women reveals the biased data that excludes women.
This book does not deal with data or statistics specifically, but might even be more interesting, as it covers the topic of quantum physics:
Quantum physics is strange. It tells us that a particle can be in two places at once. That particle is also a wave, and everything in the quantum world can be described entirely in terms of waves, or entirely in terms of particles, whichever you prefer.
All of this was clear by the end of the 1920s, but to the great distress of many physicists, let alone ordinary mortals, nobody has ever been able to come up with a common sense explanation of what is going on. Physicists have sought ‘quanta of solace’ in a variety of more or less convincing interpretations.
This short guide presents us with the six theories that try to explain the wild wonders of quantum. All of them are crazy, and some are crazier than others, but in this world crazy does not necessarily mean wrong, and being crazier does not necessarily mean more wrong.
Survival of the Best Fit is a webgame that simulates what happens when companies automate their recruitment and selection processes.
You – playing as the CEO of a starting tech company – are asked to select your favorite candidates from a line-up, based on their resumés.
As your simulated company grows, the time pressure increases, and you are forced to automate the selection process.
Fortunately, some smart techies working for your company propose training a computer to hire just like you just did.
They don’t need anything but the data you just generated and some good old supervised machine learning!
To avoid spoilers, try the game yourself and see what happens!
The game only takes a few minutes, and is best played on mobile.
Survival of the Best Fit was built by Gabor Csapo, Jihyun Kim, Miha Klasinc, and Alia ElKattan. They are software engineers, designers and technologists, advocating for better software that allows members of the public to question its impact on society.
You don’t need to be an engineer to question how technology is affecting our lives. The goal is not for everyone to be a data scientist or machine learning engineer, though the field can certainly use more diversity, but to have enough awareness to join the conversation and ask important questions.
With Survival of the Best Fit, we want to reach an audience that may not be the makers of the very technology that impact them everyday. We want to help them better understand how AI works and how it may affect them, so that they can better demand transparency and accountability in systems that make more and more decisions for us.
I found that the game provides a great intuitive explanation of how (humas) bias can slip into A.I. or machine learning applications in recruitment, selection, or other human resource management practices and processes.
Note, as Joachin replied below, that the game apparently does not learn from user-input, but is programmed to always result in bias towards blues. I kind of hoped that there was actually an algorithm “learning” in the backend, and while the developers could argue that the bias arises from the added external training data (you picked either Google, Apple, or Amazon to learn from), it feels like a bit of a disappointment that there is no real interactivity here.
Voor Privacyweb schreef ik onlangs over people analytics en het mogelijk resulterende nudgen van medewerkers: kleine aanpassingen of duwtjes die mensen in de goede richting zouden moeten sturen. Medewerkers verleiden tot goed gedrag, als het ware. Maar wie bepaalt dan wat goed is, en wanneer zouden werkgevers wel of niet mogen of zelfs moeten nudgen?