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Establish Realistic Diversity Targets with Data

Within certain countries, the topic of diversity has become a strategic focus. Whether it's issues like gender pay equity or proper representation within a company's workforce, making good decisions regarding diversity goals and strategies has become key. Of course, by "good decisions," I am referencing the data-driven kind!

Within companies today, several diversity topics are being examined. Gender is the most obvious, but companies within the U.S. are also looking at veteran / non-veteran equity and proper representation across ethnicities. For the purpose of this article, I will use gender to make my point since it's most familiar to all.

The past few years in the U.S. have seen a growing focus on balancing the workforce across gender. Within a few European countries, we see mandated quotas of female representation on boards and executive level jobs. Most companies are now setting diversity goals for their entire workforce since that's the pipeline that eventually feeds the higher positions.

How do we use data to set those goals?

The U.S. population is estimated to be about 50.8% female, according to the 2013 census data. Would it be realistic to set a diversity target for each company to be 50.8% female? Obviously not. The reality of the situation is that the supply of people into each job role is not fed by sources that are all 50.8% female, so a deeper assessment is needed. An obvious example you can think of is technology companies and product companies where the workforce is dominated by various types of engineers and IT experts. These roles are predominately male because the supply of these experts in the market and graduating from universities is predominately male.

I work with several companies like these who are trying to determine a realistic goal for "how female" their workforce can become. Let's use Chemical Engineers as one job role for example. If I open up my "NI Data Portal" where I store a variety of up-to-date dashboards about the labour force and graduate data for my clients and my own use, I can see that the graduating classes of 2015 in the U.S. produced 8,217 grads with Bachelor's Degrees in Chemical Engineering. Of those, 33.6% were female. The provisional data for graduates in 2016 for the same degree produced 9,105 grads with 32.2% of those being female. So companies can examine their current state of diversity and look at supply data like the one I've used here, to set a rough diversity goal. This, of course, needs to be coupled with a study of what I call the "flow of diversity" in and out of the company and at various levels and job categories.

As an aside, once you have that goal, the graduate data that I have is an excellent source of finding which schools are producing the type of graduates you need.

Until next time,


Tracey Smith is an internationally recognized analytics expert, author and speaker. She is one of the most highly respected voices when it comes to business analytics and HR analytics. She is the author of multiple business books and hundreds of articles in a variety of publications. Tracey has worked with and advised organizations, both well-known and little-known, on how to use data analytics to impact the bottom line. If you would like to talk to Tracey about consulting work, the NI Data Portal or speaking engagements, please visit or contact Tracey Smith through LinkedIn.

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