People Analytics – the decoded employee

People analytics applications are increasingly finding their way into companies. This is not only a reaction to the changes that have taken place in the unbounded world of work in the wake of COVID-19. Demand is also driven by the idea that HR decisions based on employee-related data and analytics are more effective and accurate than those based primarily on intuition, experience or personal recommendation. According to a Deloitte study, about 85% of companies surveyed consider employee-related data important and only three percent believe they already sufficiently evaluate and use the data. So, this trend seems to be a “done deal”.

But wait! The mere collection and analysis of employee-related data is by no means enough for people analytics to become the decisive driver of business excellence. More is needed, and buzzwords such as “Algorithmic Discrimination,” “Taylorism 4.0,” “Data Gold Rush,” or “Big Brother” vividly illustrate how people analytics can jeopardize employees’ working lives, their unique value proposition, and the corporate culture as a whole. At the HSG, the Institute for Work and Work Environments around Prof. Dr. Antoinette Weibel and Dr. Simon Schafheitle has been conducting intensive research on these topics for 6 years.

The reason for this pause is the often unintended or unforeseeable consequences, especially for employees, that the use of people analytics entails. In scientific terms, the use of people analytics means the so-called datafication of employees, i.e. the translation of the individual value contribution of employees, their personality and the quality of their social relationships into zeros and ones. The term “transparent employees” has sounded like science fiction until now, but it is not – not anymore. We already know that algorithms are better able to process large amounts of data more accurately, faster and less prone to error than we humans are. But what’s new is that intelligent people analytics applications can not only measure accurately, but also evaluate and draw conclusions on their own – even without the involvement of managers. Amazon’s “firing-by-algorithm policy”, i.e. automated firing, is a dramatic example of this. Less drastic, for example, is the app “Humu”, which independently sends so-called “behavioral nudges” to managers to help them with performance feedback. In the run-up to a meeting, for example, the app analyzes the data traces of the participants (e.g., e-mails, telephone calls, URL/log file histories) and sends the manager suggestions for action in advance, for example, on which topics he or she should ask which questions or which type of feedback would be suitable for certain participants as a journal article from Strategic HR Review notes. Similarly, the algorithm “Corti” analyzes the phone conversation in emergency numbers to provide guidance to employees on “whether it is really as serious as it is portrayed” according to The Verge.

Looking at the last two examples, the benefits of people analytics clearly emerge: used correctly, a digital tool can make work much easier, cater to the individual needs of employees, and thus also increase the efficiency of workflows. It can also facilitate collaboration between colleagues. However, this requires transparency, voluntariness, customized solutions and a manager who recognizes the inevitable dilemmas of using technology and, in case of doubt, courageously stands by the employees.

Light inevitably casts a shadow – the human factor is a key consideration when using people analytics. Employees will inevitably become more transparent as a result of the new technologies – but is that what they want? Do they want to put themselves on display more and more? Do they want to reveal “everything” about themselves to their superiors? This can quickly “backfire” for companies. If the so-called buy-in of employees does not become the touchstone of the success of people analytics, or if this is only done half-heartedly, employees can quickly refuse to use new technologies, quit internally, or rather make an effort to look good in front of the algorithm instead of devoting themselves to the actual work content. So, it is important that they also have moments of “feeling unobserved” as written in the Journal Academy of Management Annals. After all, who wants to sit in the “Big Brother” house?

When activities become complex, require a high degree of tacit knowledge, or focus on teamwork, the use of data science and automation needs to be monitored particularly critically. Recently, for example, Asian researchers have been experimenting with the use of people analytics technologies in the supreme discipline of business excellence. According to the Wall Street Journal by using EEG analysis, they aim to identify patterns in the minds of C-suite managers to infer automation potential in corporate governance. A robot as CEO? The evaluation of such a scenario, we leave to you, dear readers.

The use of people analytics thus requires a great deal of intuition, the drawing board has had its day in any case, and it requires skills from managers and employees alike that often only come to light in the implementation process. Are you prepared for the use of people analytics in your company, or would you like to learn more about the topic? Then we recommend the training course “People Analytics – Trust and Leadership in the Digital Era“.

This article was first published on jobs.nzz.ch.