Category: hr

Book tip: On the Clock

Book tip: On the Clock

Suppose you operate a warehouse where workers work 11-hour shifts. In order to meet your productivity KPIs, a significant number of them need to take painkillers multiple times per shift. Do you…

  1. Decrease or change the KPI (goals)
  2. Make shifts shorter
  3. Increase the number or duration of breaks
  4. Increase the medical staff
  5. Install vending machines to dispense painkillers more efficiently

Nobody in their right mind would take option 5… Right?

Yet, this is precisely what Amazon did according to Emily Guendelsberger in her insanely interesting and relevant book “On the clock(note the paradoxal link to Amazon’s webshop here).

Emily went undercover as employee at several organizations to experience blue collar jobs first-hand. In her book, she discusses how tech and data have changed low-wage jobs in ways that are simply dehumanizing.

These days, with sensors, timers, and smart nudging, employees are constantly being monitored and continue working (hard), sometimes at the cost of their own health and well-being.

I really enjoyed the book, despite the harsh picture it sketches of low wage jobs and malicious working conditions these days. The book poses several dilemma’s and asks multiple reflective questions that made me re-evaluate and re-appreciate my own job. Truly an interesting read!

Some quotes from the book to get you excited:

“As more and more skill is stripped out of a job, the cost of turnover falls; eventually, training an ever-churning influx of new unskilled workers becomes less expensive than incentivizing people to stay by improving the experience of work or paying more.”

Emily Guendelsberger, On the Clock

“Q: Your customer-service representatives handle roughly sixty calls in an eighty-hour shift, with a half-hour lunch and two fifteen-minute breaks. By the end of the day, a problematic number of them are so exhausted by these interactions that their ability to focus, read basic conversational cues, and maintain a peppy demeanor is negatively affected. Do you:

A. Increase staffing so you can scale back the number of calls each rep takes per shift — clearly, workers are at their cognitive limits

B. Allow workers to take a few minutes to decompress after difficult calls

C. Increase the number or duration of breaks

D. Decrease the number of objectives workers have for each call so they aren’t as mentally and emotionally taxing

E. Install a program that badgers workers with corrective pop-ups telling them that they sound tired.

Seriously—what kind of fucking sociopath goes with E?”

Emily Guendelsberger, On the Clock
My copy of the book
(click picture to order your own via affiliate link)

Cover via Freepik

2019 Shortlist for the Royal Society Prize for Science Books

2019 Shortlist for the Royal Society Prize for Science Books

Since 1988, the Royal Society has celebrated outstanding popular science writing and authors.

Each year, a panel of expert judges choose the book that they believe makes popular science writing compelling and accessible to the public.

Over the decades, the Prize has celebrated some notable winners including Bill Bryson and Stephen Hawking.

The author of the winning book receives £25,000 and £2,500 is awarded to each of the five shortlisted books. And this year’s shortlist includes some definite must-reads on data and statistics!

Infinite Powers – by Steven Strogatz

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.

https://royalsociety.org/grants-schemes-awards/book-prizes/science-book-prize/2019/infinite-powers/

Invisible Women – by Caroline Criado Perez

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.

https://royalsociety.org/grants-schemes-awards/book-prizes/science-book-prize/2019/invisible-women/

Six Impossible Things – by John Gribbin

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.

https://royalsociety.org/grants-schemes-awards/book-prizes/science-book-prize/2019/six-impossible-things/

The other shortlisted books

Survival of the Best Fit: A webgame on AI in recruitment

Survival of the Best Fit: A webgame on AI in recruitment

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.

www.survivalofthebestfit.com/ via Medium

Survival of the Best Fit was built by Gabor CsapoJihyun KimMiha 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.

survivalofthebestfit.com

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.

If you want to read more about people analytics and machine learning in HR, I wrote my dissertation on the topic and have many great books I strongly recommend.

Finally, here’s a nice Medium post about the game.

https://www.survivalofthebestfit.com/game/

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.

People Analytics: Is nudging goed werkgeverschap of onethisch?

People Analytics: Is nudging goed werkgeverschap of onethisch?

In Dutch only:

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?

Lees het volledige artikel hier.

Books for the modern, data-driven HR professional (incl. People Analytics)

Books for the modern, data-driven HR professional (incl. People Analytics)

With great pleasure I’ve studied and worked in the field of people analytics, where we seek to leverage employee, management-, and business information to better organize and manage our personnel. Here, data has proven valuable itself indispensible for the organization of the future.

Data and analytics have not traditionally been high on the list of HR professionals. Fortunately, there is an increased awareness that the 21st century (HR) manager has to be data-savvy. But where to start learning? The plentiful available resources can be daunting…

Have a look at these 100+ amazing books
for (starting) people analytics specialists.
My personal recommendations are included as pictures,
but feel free to ask for more detailed suggestions!


Categories (clickable)

  • Behavioural Psychology: focus on behavioural psychology and economics, including decision-making and the biases therein.
  • Technology: focus on the implications of new technology….
    • Ethics: … on society and humanity, and what can go wrong.
    • Digital & Data-driven HR: … for the future of work, workforce, and organization. Includes people analytics case studies.
  • Management: focus on industrial and organizational psychology, HR, leadership, and business strategy.
  • Statistics: focus on the technical books explaining statistical concepts and applied data analysis.
    • People analytics: …. more technical books on how to conduct people analytics studies step-by-step in (statistical) software.
    • Programming: … technical books specifically aimed at (statistical) programming and data analysis.
  • Communication: focus on information exchange, presentation, and data visualization.

Disclaimer: This page contains links to Amazon’s book shop.
Any purchases through those links provide us with a small commission that helps to host this blog.

Behavioural Psychology books

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Technology books

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Ethics in Data & Machine Learning

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Digital & Data-driven HR

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Management books

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Statistics books

Applied People Analytics

Programming

You can find an overview of 20+ free programming books here.

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Data Visualization books

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A note of thanks

I want to thank the active people analytics community, publishing in management journals, but also on social media. I knew Littral Shemer Haim already hosted a people analytics reading list, and so did Analytics in HR (Erik van Vulpen) and Workplaceif (Manoj Kumar). After Jared Valdron called for book recommendation on people analytics on LinkedIn, and nearly 60 people replied, I thought let’s merge these overviews.

Hence, a big thank you and acknowledgement to all those who’ve contributed directly or indirectly. I hope this comprehensive merged overview is helpful.

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Animated vs. Static Data Visualizations

Animated vs. Static Data Visualizations

GIFs or animations are rising quickly in the data visualization world (see for instance here).

However, in my personal experience, they are not as widely used in business settings. You might even say animations are frowned by, for instance, LinkedIn, which removed the option to even post GIFs on their platform!

Nevertheless, animations can be pretty useful sometimes. For instance, they can display what happens during a process, like a analytical model converging, which can be useful for didactic purposes. Alternatively, they can be great for showing or highlighting trends over time.  

I am curious what you think are the pro’s and con’s of animations. Below, I posted two visualizations of the same data. The data consists of the simulated workforce trends, including new hires and employee attrition over the course of twelve months. 

versus

Would you prefer the static, or the animated version? Please do share your thoughts in the comments below, or on the respective LinkedIn and Twitter posts!


Want to reproduce these plots? Or play with the data? Here’s the R code:

# LOAD IN PACKAGES ####
# install.packages('devtools')
# devtools::install_github('thomasp85/gganimate')
library(tidyverse)
library(gganimate)
library(here)


# SET CONSTANTS ####
# data
HEADCOUNT = 270
HIRE_RATE = 0.12
HIRE_ADDED_SEASONALITY = rep(floor(seq(14, 0, length.out = 6)), 2)
LEAVER_RATE = 0.16
LEAVER_ADDED_SEASONALITY = c(rep(0, 3), 10, rep(0, 6), 7, 12)

# plot
TEXT_SIZE = 12
LINE_SIZE1 = 2
LINE_SIZE2 = 1.1
COLORS = c("darkgreen", "red", "blue")

# saving
PLOT_WIDTH = 8
PLOT_HEIGHT = 6
FRAMES_PER_POINT = 5


# HELPER FUNCTIONS ####
capitalize_string = function(text_string){
paste0(toupper(substring(text_string, 1, 1)), substring(text_string, 2, nchar(text_string)))
}


# SIMULATE WORKFORCE DATA ####
set.seed(1)

# generate random leavers and some seasonality
leavers <- rbinom(length(month.abb), HEADCOUNT, TURNOVER_RATE / length(month.abb)) + LEAVER_ADDED_SEASONALITY

# generate random hires and some seasonality
joiners <- rbinom(length(month.abb), HEADCOUNT, HIRE_RATE / length(month.abb)) + HIRE_ADDED_SEASONALITY

# combine in dataframe
data.frame(
month = factor(month.abb, levels = month.abb, ordered = TRUE)
, workforce = HEADCOUNT - cumsum(leavers) + cumsum(joiners)
, left = leavers
, hires = joiners
) ->
wf

# transform to long format
wf_long <- gather(wf, key = "variable", value = "value", -month)
capitalize the name of variables
wf_long$variable <- capitalize_string(wf_long$variable)


# VISUALIZE & ANIMATE ####
# draw workforce plot
ggplot(wf_long, aes(x = month, y = value, group = variable)) +
geom_line(aes(col = variable, size = variable == "workforce")) +
scale_color_manual(values = COLORS) +
scale_size_manual(values = c(LINE_SIZE2, LINE_SIZE1), guide = FALSE) +
guides(color = guide_legend(override.aes = list(size = c(rep(LINE_SIZE2, 2), LINE_SIZE1)))) +
# theme_PVDL() +
labs(x = NULL, y = NULL, color = "KPI", caption = "paulvanderlaken.com") +
ggtitle("Workforce size over the course of a year") +
NULL ->
workforce_plot

# ggsave(here("workforce_plot.png"), workforce_plot, dpi = 300, width = PLOT_WIDTH, height = PLOT_HEIGHT)

# animate the plot
workforce_plot +
geom_segment(aes(xend = 12, yend = value), linetype = 2, colour = 'grey') +
geom_label(aes(x = 12.5, label = paste(variable, value), col = variable),
hjust = 0, size = 5) +
transition_reveal(variable, along = as.numeric(month)) +
enter_grow() +
coord_cartesian(clip = 'off') +
theme(
plot.margin = margin(5.5, 100, 11, 5.5)
, legend.position = "none"
) ->
animated_workforce

anim_save(here("workforce_animation.gif"),
animate(animated_workforce, nframes = nrow(wf) * FRAMES_PER_POINT,
width = PLOT_WIDTH, height = PLOT_HEIGHT, units = "in", res = 300))