No doubt, the explosion in business automation software has been a boon to HR departments. Certain business processes have become significantly easier (managing headcount, labor costs, turnover rates, demographics). Business software is also providing us with reams of actionable HR data that was previously unattainable. So what could go wrong?
You can send a whole department down a rabbit hole of data-mining if you don’t have a strategy and a framework for understanding, and acting upon, that data. If you don’t know precisely what you’re looking for, you’re not likely to find it. What began as efficiency measures have now become a drag.
HR departments and entire business units are collecting terabytes of data on employees’ behavior and performance. But overdoing it with analytics can lead to ‘analysis paralysis’. How do you find that sweet spot with HR analytics and use it to maximum advantage?
“I think the main area where companies overdo analytics is when a large number of individual metrics are created without an overarching framework for why these particular metrics matter,” says John Hausknecht, Associate Professor of Human Resource Studies at Cornell University’s ILR School.
“A stronger approach moves beyond individual metrics and gets into understanding linkages among multiple factors. It shows what HR data, specifically, predict performance, or better yet, can show that changes in certain HR policies or practices actually cause business performance to increase.”
“Most HR issues cannot be reduced to a single number, so it makes sense to think about building (and testing with data) more sophisticated models that capture the causes and consequences of whatever it is we’re interested in understanding. Once that work is done, we can have greater confidence in why we’re tracking and trying to influence certain metrics.”
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