With the recent developments in AI, the supremacy of algorithms and analytics in the management of business – and people – seems more and more obvious in terms of driving up the performance of organisations. But should human beings be analysed in the same way as inventory entries? A research team including Dr. Ulrich Leicht-Deobald, Trinity Business School,and his collaborators Dr. Lisa Marie Giermindl, Dr. Franz Strich, Dr. Oliver Christ, and Abdullah Redzepi uncovers new findings on the risks of people analytics.
People Analytics: The risks behind the promises by CoBS Editor Aymeric Thiollet. Related research: Lisa Marie Giermindl, Franz Strich, Oliver Christ, Ulrich Leicht-Deobald & Abdullah Redzepi (2022) The dark sides of people analytics: reviewing the perils for organisations and employees, European Journal of Information Systems, 31:3, 410-435, DOI: 10.1080/0960085X.2021.1927213.
Perhaps you have seen it in the Harvard Business Review or another business-oriented magazine: people analytics seems to be the ultimate efficiency solution for the agile company of the 21st century. The promises of data-driven decision-making when it comes to human resources can be summarized as such: exploiting large quantities of data through complex algorithms in order to make optimum, efficient, and unbiased use of employees and their potential for added value. But is it that simple?
Whether you’re a manager or an employee, you’re probably already exposed to some degree of integration of people analytics into your organisation’s decision-making. In their research, Giermindl, Strich, Christ, Leicht-Deobald, and Redzepi put forward new elements that enable employees and their firms to take stock of the state of maturity of their people analytics systems and the risks associated with them. Knowing so will allow both to move towards a more appropriate reliance on people analytics in the company’s decision-making processes.
The golden assumptions behind people analytics
To understand the perils of people analytics and their negative implications for organisations and employees, Giermindl, Strich, Christ, Leicht-Deobald, and Redzepi analysed dozens of documents from various databases relating to information systems, organisational behaviour or human resources management, identifying five emerging themes relating to the opportunities, barriers, maturity, idiosyncrasies and risks of people analytics.
It appeared that numerous studies address the promises of and the barriers to people analytics adoption, whereas the idiosyncrasies, the negative implications and associated risks received little attention. Perhaps more startlingly, the researchers discovered that the assumptions behind people analytics are overwhelmingly positive: they seem to be fueled by assumptions of objectivity and infallibility – as opposed, presumably, to human decision-making – and of the ability to predict future human-behavior based on historical data.
The emergence of autonomous analytics
For Giermindl, Strich, Christ, Leicht-Deobald, and Redzepi, the main issue lies there: even if analytics has enabled significant progress in terms of business optimization, human resources are not material resources, and it could pose ethical and moral risks to consider them as such.
To better evaluate the nature of people analytics and the risks involved, the researchers made a new theoretical contribution to the field: to the existing three maturity levels of people analytics (descriptive, predictive, and prescriptive analytics), they proposed a fourth maturity level: autonomous analytics. This novel maturity level helps account for the recent technological developments and the emergence of learning algorithms and AI in the analytics field. Overall, these maturity levels represent a spectrum along which humans increasingly delegate their autonomy in decision-making to a system, which poses specific challenges.
The perils of people analytics
How can we understand the potential perils of people analytics for organizations and employees, depending on the level of technological maturity? To answer this question, the researchers first determined a list of risks specific to people analytics. Indeed, such technology can:
- Bring about an illusion of control and reductionism
- Lead to estimated predictions and self-fulfilling prophecies
- Foster path dependencies
- Impair transparency and accountability
- Reduce employees’ autonomy
- Marginalise human reasoning
- And erode managerial competence.
It is all the more interesting to note that the negative implications of these risks can worsen with technological progress (AI systems, learning algorithms). However, given the current predominant research focusing on the upcoming AI hazards, it is tempting to downplay the risks of today’s widely deployed technologies. Indeed, the researchers argue that we shouldn’t downplay these risks at lower maturity levels because they still exist, generating problematic consequences on human resources.
Preparing for more responsible people analytics
Three practical implications emerge from the research for organisations and managers dealing with the implementation and use of people analytics:
- It contributes to more realistic expectations on people analytics’ technologies at work by challenging its underlying assumptions
- It raises awareness about the risks of transferring other areas’ analytics logic to the management of employees
- And it helps managers to better understand and evaluate the perils arising from people analytics according to their maturity level.
In a nutshell, Giermindl, Strich, Christ, Leicht-Deobald, and Redzepi argue that by looking beyond the positive assumptions and carefully considering the risks posed by people analytics, employees and organizations can appropriately and responsibly adjust their level of reliance on these technological tools. By doing so, organizations can continue to grow while paying careful attention to the autonomy and value of their human resources.
Looking at the degree of integration of the people analytics in your own organization, you can now ask yourself which are the most prevalent risks – and how you can effectively manage them.
- Link up with Ulrich Leicht-Deobald, Lisa Marie Giermindl, Franz Strich, Oliver Christ & Abdullah Redzepi on LinkedIn
- Read a related article: AI: A paradigm shift in people management
- Discover Trinity Business School, Trinity College Dublin
- Apply for the Trinity Business School MBA.
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