Predicting Employee Turnover: A Basic Data Checklist

big data in human resource management

By John Lipinski

Would you ever fly on an airplane if the pre-flight checks were based on a pilot’s memory or “gut feeling”?

Me neither.

In aviation, pilots use checklists to solve the problems of safety and risk inherent in flight.

In a competitive marketplace, virtually all businesses are trying to solve the problem of employee turnover. When people leave, it costs time and money. And when those people need to be replaced, it costs more time and more money.

The task of reducing employee turnover is crying out for an analytics-based attack. Yet my conversations with many HR/ Human Capital professionals across industries suggests that efforts to understand and lower turnover are based more  often on gut feeling than repeatable, testable analysis.

This checklist is a simple yet incredibly powerful step to gathering the data you need to predict and reduce employee turnover.

Be Systematic: Use a Checklist

Too often, the data sources we consider are simply ad-hoc, a product only of a recent conversation, or  our own in-the-moment whims about what we should look at.

If we don’t use and develop our own systematic checklists, we run a huge risk of overlooking a key data source or measure, producing subpar analyses, and making an even worse decision.

Save Your Brain: Use a Checklist

Using a checklist not only reduces the risks of overlooking a key element of turnover at your organization, it also makes our job EASIER! If you have a checklist, you can conserve your brain power and energy for the important stuff instead of wasting it trying to just remember the kinds of data you need.

The Starter Checklist

This checklist will get you started. Many of the items listed are the very things you WOULD typically think of. The difference with a checklist is that they are all right here….right in front of you all at the same time. You CAN’T overlook them.

That said, we may have added a few things you would not normally consider.

As a general rule, it pays to think about human behavior and decision-making at multiple levels: the individual, the team, the organization, and the community/ region.

I have structured this checklist accordingly.

This is only a starting point for your consideration. You might not have data available for some of the things listed. Others simply won’t be relevant.

The point is not to provide a definitive, comprehensive “granddaddy of them all” list.

The point is to start thinking systematically and consistently…in a word, analytically.

Eliminate what you want to, add other data sources that you think of.

Develop this checklist over time and let it grow and change along with your career and your company.

A Note of Caution

With great data analytics, comes great responsibility. Some organizations are very open to using analytics to address turnover, others not so much. Make sure to consider what makes sense for your organization, your leadership, and most importantly the people on whose data our work depends.

Suggestions? Criticisms? Share your thoughts with us!


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