This brings us to “people analytics,” a data-driven approach for managing the workforce. Think of the techniques used in the movie Moneyball and apply these to recruiting and developing everyday employees instead of baseball players. Xerox is one company which has embraced people analytics. If you apply for a job in their call centre division, the good news is you won’t have to navigate your way through a stressful interview. Instead, Xerox have built a data model based on an analysis of who their high performing call centre operators already are. As soon as you submit your CV for consideration, it will be instantaneously compared to the data model, and if a reasonable match, you get offered the job. Fully automated, the computer either says yes or no. The advantage of such a data driven approach is the uncovering of patterns which would have traditionally remained invisible. For example, previous call centre experience has no bearing on whether you will be a successful employee in Xerox, but having reliable transportation, moderate participation in social networks, and a creative rather than an inquisitive personality does.
The use of people analytics is becoming widespread. A 2020 McKinsey survey reports that 70% of executives now see people analytics as a top priority in their organisations. While analytics can be quite effective in some parts of the company, optimising a supply chain for example, quantifying people is far more complex. The intended purpose of people analytics is laudable – using statistical insights from employee data to make talent management decisions – but such quantification approaches may unintentionally alter important attributes such as creativity, behaviour, and accountability.
Steve Jobs, the legendary Apple co-founder, firmly believed creativity depends on exposure to diverse ideas and people. Jobs saw to it that Apple’s headquarters were designed in a way which encouraged people with diverse backgrounds to mingle together. Dissimilarity forces your mind to make connections it might otherwise miss. While people analytics may provide objectivity, it can potentially stifle creativeness. Take Amazon for instance. A company famed for its success in using algorithms to predict what you want to buy, turned its hand to predicting top talent to recruit. Similar to Xerox, a data model was developed based on existing employee data from the previous 10 years. What the data model gave them was exactly what they already had an abundance of – white males. The analysis of historical employee data to make hiring decisions was not only biased against non-white males, it would have resulted in groupthink and less innovative ideas. Amazon ultimately abandoned the project.
Nowhere more than the world of sports has the data and analytics revolution been more heavily worshipped. Heart rate, power output, sleep pattern, field position, acceleration, lateral motion, and speed, to name but a few measures, are all captured and quantified by professionals and amateurs alike desperate to eke out that extra 1% competitive advantage. Such hyper-quantification may well be helping sports teams and athletes improve their performance, but it is also changing behaviours in unintended ways that could transfer to the data driven workplace. In a recent study of professional rugby, players at one particular club revealed how 70 different variables describing a player’s positive and negative actions and physical condition were constantly gathered through wearable devices. As this data was visible to management and all squad members through an app, and also used in contract negotiations, players started to obsess over these metrics and admitted to focusing on improving their own individual stats, even if the resulting behaviour compromised team performance. Similar behaviours have been noted in football and basketball where players opt for the safe pass which improves their tracked pass completion rate but offers no advantage to their team, rather than taking on the risky defence splitting pass which could result in a score.