Staring At the Hood Of Your Car? Get Proactive Using Analytics

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“Stop staring at the hood of the car!” the driver education instructor exclaimed. I, among other students, all had the look of panic on our faces as we watched the car swerve from one side of the road to the other. I could barely stomach the “near misses” as others cars blew their horns. The other students in the back seat grabbed their seatbelts tightly eyes wide open and mouths dropped open.

The driver’s education instructor quickly assessed that the student behind the driver’s wheel was only staring at the hood of the car. As a result, the student driver had no sense of her surroundings much less being able to identify when the road changes direction in order to turn the steering wheel.

I am glad to say that day we were able to leave that driver’s education event unharmed. But when it comes to organizations using analytics to measure and predict success, at times I am reminded of that day in the driver’s education car.

There are many different kinds of analytics:

Descriptive: What’s happening?
Diagnostic: Why is it happening?
Predictive: What’s is likely to happen?
Prescriptive: What do we need to do?

Use basic measures: Does your car have tires and the right type?

At times, I have observed organizations that chose to measure only the descriptive type of analytics and forced them to ask what happened? At an elementary level, if the measure is static month-by-month, it may not account for seasonal fluctuations or trends providing us better clues to what happened. Let’s use turnover for example. If we measure turnover as in what happened each month we may see something like this:

January	February	March	April	May	June
2.8	2.4	2.5	2.6	1.4	2.5

While this may tell me what has happened per month, it doesn’t tell us if this is a good or bad trend. It doesn’t tell us if there is a seasonal fluctuation. It doesn’t really allow us to get ahead of turnover.

Getting ahead of trends: Are we looking down the road?

If we are serious about getting ahead of turnover or other business trends, then we want a predictive type of measure. At the very basic level, annualized turnover will tell us at the current rate of turnover where will we wind up at the end of the year. By annualizing the above table and assuming our headcount remains the same, turnover will wind up being around 28.4%. If industry average is around 10-12%, then we know we have a problem and will wind up out of whack by end of the year.

Forecasting or predictive analytics

To look beyond the hood of the car when driving, forecasting is important. Forecasting will enable you to effectively see trends, seasonal fluctuations, or better understand likely scenarios before they occur.

To forecast the future and account for seasonal trends, it is best to obtain a few years of data per month. That way you can assess seasonal trends and account for it in a forecasting model. Taking our turnover example, if we want to identify seasonal trends grabbing data for 3-5 years can give clues. If you overlay year-over-year, in a visual format you can easily identify if there are common seasonal peaks in turnover.

Predictive analytics in the HR space is becoming more sophisticated as technology continues to develop. There are some organization’s HR leaders working with the idea of being able to predict when an employee is close to resigning before they actually do.

Benchmarking: Is the navigation system on?

I have observed an occasional flaw when a leader wishes to compare to an industry benchmark, but with ineffective internal measures. Anyone can shape numbers to fit a pre-determined story.

Effective benchmarking compares apples to apples. It allows us to let the data, as it is and without bias, tell the story. Otherwise, if we water down measures, set up arbitrary rules, and then compare to industry benchmarks, we are only fooling ourselves.

Much like effective driving requires looking beyond the hood of the car, effective analytics require the same concept. Otherwise, you may be destined to wind up in a ditch.

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Tresha Moreland

Tresha Moreland is a 30-year organizational effectiveness and strategic workforce planning expert. She partners with business leaders to develop workplace strategies that achieve best-in-class results. She has held key organizational leadership roles in multiple industries such as manufacturing, distribution, retail, hospitality, and healthcare. Tresha is the founder and principal consultant of HR C-Suite, LLC (www.hrcsuite.com). HR C-Suite is a results-based HR strategy resource dedicated to connecting HR with business results. She has received a master’s degree in human resource management (MS) and a master’s degree in business administration (MBA). She has also earned a Senior Professional in Human Resources (SPHR), Six Sigma Black Belt Professional (SSBBP) Certification. She is also recognized as a Fellow with the American College Healthcare Executives with a FACHE designation.