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Publishing Apples and Oranges

To publish or not to publish? That, is the question. 

When it comes to releasing HR data to the public, there are so many thoughts that come to mind when a client asks my opinion on this topic.

  • Will a company be viewed in a favourable way for its participation in data transparency, or
  • Will the company be putting itself in a position of higher risk for criticism?

After 25 years of analyzing data, I can say that there is virtually no data set in a company that doesn’t have some type of nuance to it. Understanding these nuances is the only way to accurately use the information. Here’s one example of a normal business situation that adds a nuance to your data. If you’re not careful in how you analyze that data, it can be viewed as inaccurate or reported inconsistently.

While working for a Fortune 100 company, someone asked me, “How many employees are in the U.S.?”

My reply… “I can give you three answers to that question and they are all correct. Please explain what you need the information for so I can determine which of the three numbers is accurate in your situation.” 

Since it was a global company, it was normal to have several expats outside of the U.S. but being paid by the U.S. The company also had its Latin America region headquarters located in the southeast U.S. So, the question really came down to which of the following questions the person really needed answered:

  • How many employees are physically located in the U.S.?
  • How many employees were being paid by the U.S.?
  • How many employees are part of the U.S. region?

The answer to each of these questions yields a different value. As careful data people, we must add fine print to our reports and analyses to ensure that the number reported is interpreted accurately.

Now, let’s consider our HR data further and in the context of reporting something like headcount to the public. A few questions come to mind:

  • Do we include full-time contingent workers?
  • Do we include part-time employees? Do we count them as 1 headcount, 0.5 or a more exact FTE value based on their hours per week?
  • Do we report only the U.S. headcount or values for each country?
  • Do we release data on gender and ethnicity?

These are all very real questions being discussed inside companies today. Even when creating a presentation to summarize analysis results for client HR data, I find myself inserting more fine print than a commercial for medications or legal services. Here’s an incredibly simple example.

Suppose the company has 15,000 employees. We report the gender diversity split by showing the number of employees for each gender and the resulting percentages. Suppose we have 7,475 females and 7,475 males. You know that the first question will be why these two values don’t add up to 15,000. 

Data experts reading this will instantly realize that there are blanks in the GENDER field, but to the presentation audience, that isn’t their first thought. The first thought is sometimes confusion as they start to wonder about the accuracy of the analysis they are about to receive. So, enter the first fine-print note at the bottom of the slide indicating 50 blanks, representing one third of one percent of the data. This last statement demonstrates that the analysis results being presented were not impacted by such a tiny amount of missing data.

Now, let me get back to the topic of releasing data. There are both benefits and risks in releasing data and most of this data comes with fine print. In fact, most of the data I’ve seen comes with far less fine print than would fully document enough information for anyone to make an apples-to-apples comparison of two companies. Did Company A only include employees in the denominator of its Revenue per FTE calculation? Did Company B divide revenue by the sum of its full-time employees and contingent workers?

We live in a data world full of apples and oranges… and people will compare that data nonetheless. Care and attention should be exercised in releasing data to the public, for without the fine print, the data can be easily misinterpreted. Misinterpreted data leads to misperceptions about your company which leads to your corporate communications department needing to do “spin control.”

Bio

Tracey Smith is an internationally recognized analytics expert, speaker and author. Her hands-on consulting approach has helped organizations learn how to use data analytics to impact the bottom line. Tracey’s career spans the areas of engineering, supply chain and human resources. She is CPSM certified through the ISM.  If you would like to learn more, please visit www.numericalinsights.com or contact Tracey Smith through LinkedIn. You can check out her books on her Amazon Author Page.