Cathy Missildine, HRExaminer Editorial Advisory Board Member

Cathy Missildine, HRExaminer Editorial Advisory Board Member

We have been measuring activities for a long time in HR.  In my classes and workshops, I always ask the same question, “So, what are you measuring in HR?”  I usually get answers similar to this:

  • Cost per hire
  • Time to fill
  • Turnover (at the organizational level)
  • Training cost per employee
  • # of employees trained per year
  • # accidents
  • # ER issues
  • # requisitions filled/recruiter

Don’t get me wrong in order to run an efficient HR department you need metrics just like the ones above.  I call those HR tracking metrics; they make sure all our HR trains are running on time and to the right place.  That’s great, (here it comes) BUT…those metrics aren’t going to show HR impact or impress the C-Suite.  What those metrics show is, “hey, we are doing our job.”

So, the goal is to move from HR metrics to HR analytics.  Let’s clear up a couple of definitions:

HR Metrics-are a vital way to quantify the cost and the impact of employee programs and HR processes and to measure the success (or failure) of HR initiatives. They enable a company to track year-to-year trends and changes in these critical variables.

HR Analytics-is the process of combining data mining with business analytics techniques to analyze human resources data.  The goal of human resources analytics is to provide an organization with insights for effectively managing employees so that business goals can be reached quickly and efficiently.  Analytics is also predictive in nature thus allowing for better business outcomes.

Think about the typical way an organization reports data in regards to organizational turnover.  Most companies report turnover at the organizational level.  Some HR individuals may breakdown the turnover by department or location.  The idea is to think like a marketer and slice and dice the data looking for root cause of turnover.  So, you can start looking at turnover by:

  • Manager
  • Tenure
  • Generation
  • Location
  • Department
  • Reason for leaving
  • Performance level (High performers)
  • Engagement scores

By taking a more granular approach to analysis you can begin to unfold a “turnover story” that begins to yield “HR Intelligence” rather than just metrics.

Let’s take our example one step further.  By applying a few simple statistical tests, using the data most organizations have readily available, you can be predictive about turnover.  So, by looking at turnover historically, along with employee performance and engagement data, you can determine who is at risk for leaving the organization.  Can you imagine the reaction an HR professional would get by identifying those high performers that were at risk for leaving the organization?  Talking about impact!

If your HR Department would like to answer yes to the following questions, then it’s time to get on the analytics train:

  • What if you are able to develop a “high performer profile” that enables you to hire the right people the first time?
  • What if you are able to identify your high performers that are at risk for leaving the organization?
  • What if you are able to determine the HR initiatives that will best contribute to the bottom line?

The next question becomes, how do I make the transition form metrics to analytics?

The first step is you need to make a business case for analytics. The best way to do that is to solve an existing business issue by using analytics.  Don’t wait for the business to ask for your data, be proactive and start now.  It could be something as simple as “Why are sales down at XYZ location?”  By digging into sales, customer, and employee data, you would be amazed at what you find.  After, you score a big win, with solving a problem, then leadership will want more and your “credibility” problems will be long gone.

The time is now to move away from metrics and move towards analytics.  CEO’s demand for data has never been higher.  The focus on high performing talent has never been greater.  If HR intends to remain a player…analytics are table stakes.

 
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