Tag: workforce

People Analytics vs. HR Analytics Google trends

People Analytics vs. HR Analytics Google trends

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.

If you want to get such data yourself, have a look at the Google Trends project.


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.

publicatie-online.nl/uploaded/flipbook/15810-v-d-laken/12/

Want to learn more about people analytics? Have a look at this reading list I compiled.

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