systematicHR

The intersection between HR strategy and HR technology

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Cycling Technology: Power Meters

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Cycling is an easy sport to figure out from a measurement perspective.  Speed is a function of power output and weight (all other things held constant).  So if you’re going uphill, the number of watts you generate as a factor of your weight (you + the bike) will determine your speed.  The lighter you are and the more watts you can put out, the faster you go.  The advent of power meters a few years back have really allowed cyclists to train not only just to make their legs stronger, but to scientifically increase their wattage output.

When it comes to HR, we sit around and try to figure out how to create meaningful decision support, but we don’t use all of the power of modern analytics that is available to us.  Even today, I still see turnover and headcount reports that barely reflect a current point in time, and most of them are for the current month or quarter.  Today, we have the ability to take any analytic and trend it over months, quarters or years (given we have the data back in time) and then further slice and dice the data.

Simple turnover reports are an easy example.  You should be able to run a turnover report that allows you to drill down from the enterprise organization and discover problem areas within the divisions and cost centers.  Once there, you should be able to look at the problematic cost centers and “slice” the data by age, gender, ethnicity/race, tenure or any other parameter in your database to understand where the turnover is occurring.

The idea behind analytics and your HR business intelligence practice is making your HR organization more powerful.  The above described as it is does not actually make you more powerful.  Now that you have pinpointed where in the global organization your problematic turnover lies, and in what specific population, you can no diagnose the reasons.  Let’s say you have turnover in the customer service area with people in their 30’s and with a tenure between 3-5 years.  Do you now do interviews with them and some key stakeholders for this population to discover the root causes?  Is it time to run more analytics centered on this population so you can review differences in training hours for this population against other divisions or cost centers?  Perhaps you can run the results against engagement scores from the survey you hopefully are running and have your survey company correlate turnover to some of their key questions.

At the end of the day, it’s about power.  But you only get more power if you run the data.

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