HR by the Algorithm: Playing by the Numbers

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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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Christa Degnan Manning

Christa Degnan Manning leads Bersin by Deloitte’s solution provider research. In this role, she collaborates with Bersin and Deloitte colleagues across practices to identify and analyze software and service providers that support and sustain success in human resources, workforce enablement, and organizational effectiveness. Christa’s research and advisory work helps businesses align their workforce support strategies with the right third-party partners and governance models to deliver functional capabilities and employee experiences that support productivity, engagement, and workforce efficiency. She also helps solution providers map their capabilities and go-to-market activities with solution buyers’ and users’ critical needs. Christa draws on more than two decades of business-to-business market research, operational leadership, and global workforce experience. She holds a Master's degree from the University of Massachusetts, and a Bachelor of Arts degree from Barnard College, Columbia University, including studies at University College, University of London.

2 thoughts on “HR by the Algorithm: Playing by the Numbers

  1. Great blog, Katherine. As the Principal Data Scientist at Ultimate Software, I think you are 100% correct in suggesting that there has to be trust between the predictors and those using them. At Ultimate, we do share with our customers what makes up the scores, but we also strongly encourage our customers to not get too wrapped up in it. The goal is not to "raise scores" – it is to alter the day-to-day relationship between the manager and the employee so that the employee, manager, and company all benefit.

    Predictive analytics are merely predictions, which can be right or wrong, all with some degree of accuracy. (and ours are quite good!). But no matter how good a manager thinks he/she is, the underlying characteristics found in data will always be able to provide additional insight that the manager did not expect. These data points can alert a manager, especially one that is more focused on themselves and their own daily tasks, to turn their attention to a specific employee in a specific way.

    But it is also equally important to understand that nobody’s predictive analytics will never contain "all" of the data about a person. So I do not believe it will ever be possible, or applicable, to ignore the "people" side.

    For example, an employee may miss a great deal of time dealing with a sick parent. If "number of weekly hours worked" is a contributing attribute, then that employee may receive a "likely to leave" prediction, which wouldn’t be true. So a good manager would take no action at all. A bad manager would apply some generic action that could actually cause the employee to leave.

    But the vast majority of cases do not have something special like this. So the predictors can make a huge difference. Our customers are taking advantage of the predictors, and they are seeing substantial ROI in their bottom lines.

    So the best guidance I can give is to let the data be the driver to identify the people of interest, but then utilize both the data and the "people" side to take appropriate action/non-action. This is the easiest path to successfully leveraging predictive analytics.

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