More than ever, companies are using data to both measure and shape employees’ workdays. People analytics uses statistical methods and intelligent technologies (e.g., sensors, digital devices) to create and analyze digital records of employee behavior and employ an evidence-based approach to increase the organization’s efficiency and productivity. While the goal of this approach is to increase productivity, increased monitoring can also increase stress, reduce trust, and even cause employees to act less ethically. Even so, adoption of employee monitoring tools is rapidly accelerating. Companies that want to ethically and successfully deploy people analytics should do three things: 1) Make clear that analytics aren’t a step towards automation, 2) Seek holistic applications that encourage employee growth rather than focusing on narrow productivity metrics, and 3) Avoid labeling or treating employees as pieces of data.
Once upon a time, organizations were made up of people. Today they consist of data. As companies have learned to mine their data to better identify new opportunities, improve predictions, and make better decisions, interest has shifted from the humans who do the work to data on what they do during work hours (e.g., how may emails they sent, how many people they talked to, how many breaks they took). In particular, employee data is being used more and more in human resources management (HRM) — and, more recently, people analytics (PA) — and workers are increasingly being defined in terms of their data.
The implications of this shift are significant. An approach that defines people and their value to the company (actual and predicted) in terms of data runs the risk of depersonalizing the people that make up companies, reducing them in the eyes of their employer to the level of interchangeable objects. Moreover, it has the potential to create a work culture that denies employees’ privacy and in which people feel less safe.
This trend of depersonalizing employees isn’t necessarily new. For some time now, HRM has focused less on approaching the employee as a “whole” human being and more on promoting a one-size-fits all approach to manage employees. To reduce costs and promote efficiency of compliance and standardization, HRM has approached employees mainly in terms of the quotas they are supposed to hit, the sales they make, the deals they close, and so forth. There’s an ugly logic to this: treating employees as interchangeable commodities makes it easier to impose the ever-increasing burden of bureaucracy that defines contemporary organizations.
Recently, however, this approach has evolved: human resource management has paved the way for people analytics, which uses statistical methods and intelligent technologies (e.g., sensors, digital devices) to create and analyze digital records of employee behavior and employ an evidence-based approach to increase the organization’s efficiency and productivity. Today, up to 70% of executives consider the implementation of PA capabilities as a top priority and predictions are that the value of the global big data analytics market will be around $68 billion by 2025.
PA goes beyond the traditional procedures for measuring and quantifying employee performance — intelligent technologies working with large, unstructured, real-time data and aggregated data sets allow organizations to make predictions rather than simply measure outputs. But, the real departure from traditional HRM practices is that PA often means that employees are surveilled and analyzed at increasingly intimate levels all the time. Data from devices such as cameras, Bluetooth beacons, mobile phones, IoT wearable devices, and environmental sensors are analyzed with the aim of making predictions that allow supervisors to address, evaluate, and — if needed — punish employee behavior.
