One of the hottest areas in our run-for-the-numbers race right now is predictive analytics – algorithmically-based formulas that might predict such important things as who will be a top performer or who is likely to leave the organization.
Rather than looking for a crystal ball, the intent in these algorithms is to use mathematics (referred to as “science” by the vendors) to provide HR with the data points for better decision making. Let’s look at one popular example today: predicting employee voluntary attrition. This feature is increasingly prevalent now for two reasons: one, attrition is important to organizations worldwide in a tight market for skills, and two, it is generally easier to “predict” based on the kind of data that HR generally has available: time in one job, duration without promotion, reputation of the manager with his or her subordinates historically, length and time of the commute to work, and the like.
Just this morning I listened to one solution provider, Ultimate Software, talk about its retention predictor and upcoming high-performance predictor. Likely these can be valuable tools for many. But maybe we should play out conceivable outcomes of the predictive philosophy.
Let’s look at two possible though perhaps unlikely ramifications for the “Predictive Economy” as it applies to the workplace: misunderstanding the algorithms on which these predictions are made and the replacement of people in the decision-making process through over-reliance on numbers.
Solutions that predict behavior, success, retention or leadership abilities are by definition fairly complex: they must crunch data from many sources to ascertain a conclusion such as “Sally is a likely high potential performer.” An HR professional or indeed, Sally’s manager, will be delighted to see this fact highlighted in Sally’s profile – but will either stop to consider on what basis the conclusion was made? Will anyone in an organization take to time to figure out how a software-provided algorithm works and evaluate the validity of the data used by the program to support such a conclusion within his or her specific environment? If a formula is a “predict the difference between a C and an A player 75% of the time,” is it meaningful to you? And are you comfortable with looking at a computer-derived conclusion and making an employee decision on it?
But let’s say the algorithms are faultless: we can predict to a one who will succeed, who is a good team player, who will leave the organization, and who will be a great leader. We can replace the “people part” of HR and training because we will be able to calculate good hires, automatically assign appropriate learning to them because we know what they will excel at, algorithmically assign them to teams because we know their social behaviors, automatically plot out promotion paths and salary increases – and eliminate the decisions and indecision that often surrounds the employee acquisition and optimization processes. Human intervention not needed.
Now, not to sound overly curmudgeonly, I do like scientifically-derived data about almost everything—especially people. But as we move into the hyper-analytic movement in human capital management, let’s just make sure we really know what we looking at in those algorithmically created conclusions that can affect our employees lives.
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