Imagine being able to predict which employees will work well together or how well connected the CEO of a company is as compared to the Project Manager. Tomas Chamorro-Premuzic’s recent article published in the Harvard Business Review entitled Tech Is Transforming People Analytics. Is That a Good Thing? explains that the incoming wave of technological and data-driven solutions to common human resource problems will change the way we work. But this is beyond a silver bullet solution. There are the risks of over-surveillance, crossing ethical boundaries into people’s personal lives, and the potential for over-dependency and inappropriately falling into the trap of the correlation-causation fallacy. So what does this all mean and what can we do about it?
Humans like to think that they are quite logical but there is always a healthy dose of bias and instinct that tend to form part of our judgments. For example, we struggle to conceptualize true randomness. If the winning lottery numbers were 1, 2, 3, 4, 5, 6, 7, and 8, a large majority would believe that the system was rigged. This is despite the lottery number sequence of 45, 23, 74, 12, 3, 63, 13, and 9 having the same probability of occurring, yet we wouldn’t question this. This phenomenon becomes important because data analysis involves taking seemingly ‘non-sensical’ data and modeling it into revealing important relationships. Sometimes these relationships will be true, and others might be through sheer coincidence. And it is during these scenarios that the relationship between two or more variables may not be causal and just be coincidental. Imagine analyzing data that implied that people who were shorter had higher productivity levels and that based on that, you hired only people of a certain height, only to find out that this wasn’t true. Think of the ramifications in terms of your brand perception, future productivity, and workplace culture (not to mention the ethical questions too!).
That is why, in this era of ‘big data’, it is crucial that hypothesis and statistical training is given to future practitioners. Laying out clear and descriptive questions to test mitigates drawing inappropriate conclusions and costly decisions. It also helps in understanding the ethical ramifications of future choices, thereby contributing to which data should and should not be collected.
Underlying this whole process is getting businesses to reach a consensus on how data concerning workforce performance should be used. Going through this process promotes best practices with regard to data collection, handling, and analysis that all businesses can benefit from in making informed choices about their workforce. By teaming up together, industry standards in People Analytics provide mutual benefits across industries in meeting acceptable standards. This reduces the potential of businesses making inappropriate conclusions that could be costly or breaching ethical standards that land them in legal trouble. It also protects workers in knowing that their performance is for the betterment of themselves as a developing employee and the organization that they work for. In doing so, human resource departments will be able to surf the incoming waves of big data rather than flail and tumble in the sea.