While some might argue that good decision making is as much art as science, there’s no question that good data, analyzed properly and applied strategically, can help organizations make better decisions more often than not. After all, perception has its limits.
And nobody knows that better than someone who makes her living gathering, reviewing, and analyzing data and then using those “numerical insights” to make recommendations for action (or inaction, as the case may be) to business leaders eager to learn what their numbers have to say about the future.
During an extended e-mail chat, Tracey Smith of Numerical Insights told me all about it.
CS: Tell me a little bit about Numerical Insights. Why did you start it?
TS: Numerical Insights provides clients with customized guidance in the areas of HR analytics, strategic workforce planning, and additional topics involving the data-driven side of HR. As the former leader of global strategic workforce planning for FedEx Express, I provide a tailored approach to meet the strategic HR needs of my clients. With more than 20 years of experience in the areas of HR, supply chain, and engineering, I’m experienced at seeing the bigger picture. I hold degrees in Mathematics, Engineering, and Business from the U.S. and Canada.
TS: Numerical Insights was started when I recognized that most companies were struggling to transform HR from a gut-based decision-making approach to one driven by data analysis. With a desire to use my mathematical and business skills to help companies move forward in this HR evolution, I launched my consulting business, Numerical Insights LLC.
CS: Do you have a favorite client “type”?
TS: My favorite type of client is one that constantly strives to do things in a better way and sees the value in using “the power of information” to maximize their HR budget.
CS: How much time does the typical project last?
TS: In my world of analytics, there is really no such thing as a typical project. They are as different as the clients themselves, which is what makes working as an independent consultant incredibly interesting for me. I love the challenge. That said, the projects I have completed range anywhere from 2 days to several months. Usually the shorter engagements are for clients who are just getting started in analytics and wish to test the value of it with smaller projects before making larger commitments.
CS: What has been your favorite project to date?
TS: I have an enthusiastic client who wants to proactively plan the workforce he needs to meet future growth. Together, with the help of the leaders of this company, we established the workload drivers for key roles in the organization. Using this information, I’ve built a mathematical model of their future workforce needs including a projection of the cost of the workforce. This client will now have a clear understanding of where additional [personnel] is most needed as the company grows.
CS: What wrong with perception?
TS: The problem with perception is that it can waste valuable resources and money. Here’s an example. I was sitting in a meeting with several executives. One executive was directing the others to assign staff to create a more substantial exit interview process because “so many people” were leaving his department. When I asked him what made him think a large number of people were leaving his group, he proceeded to name a handful of people who had moved on. These people all had offices near his.
TS: Having analyzed the turnover in this group recently, I informed the attendees of this meeting that, in fact, the turnover in this group had gone done for the past 3 years, and in the current year, the turnover was on track to be the same as the previous year. There was no need to dedicate resources to create a new exit interview process.
TS: As you can see, relying on perception can sometimes send you in the wrong direction. In this case, because a few people who had left the team happened to sit near the executive’s office, he perceived that a large number of people were leaving the department when they actually weren’t. The important lesson to learn here is, if you have access to data, why not use it to make the right decision?
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