With today’s rapidly changing mix of analytic techniques, toolsets, and platforms, it’s difficult for any organization to be confident it is keeping its analytic workforce and skillsets up to date.
I often have clients ask if they need to consider turning over a large portion of their analytics organization in order to adapt to these changes. I firmly believe that this is usually not the case and that the fundamental skills for analytic success are in place. Those skills simply need tuning and updating.
With today’s rapidly changing mix of analytic techniques, toolsets, and platforms, it’s difficult for any organization to be confident it is keeping its analytic workforce and skillsets up to date.
I often have clients ask if they need to consider turning over a large portion of their analytics organization in order to adapt to these changes. I firmly believe that this is usually not the case and that the fundamental skills for analytic success are in place. Those skills simply need tuning and updating.
In fact, I see a strong parallel between athleticism and analytic capability. I also see a strong parallel between learning to speak multiple languages and learning to work within differing analytic environments. I’ll explain what I mean by both of these statements in this blog in the hope that it will help make the path forward seem clearer and less intimidating.
Analytic Athleticism
People generally accept the notion of inherent athleticism. This concept says simply that there are people who are athletic and those who aren’t. While anyone can maximize their inherent athleticism with training, someone who isn’t very athletic will never compete at an elite level in any sport. On the other hand, people with an elite level of athleticism can often compete competitively in numerous sports. Part of why the premise of athleticism is accepted is that it is so easy to see it in action. If you watch someone playing on a field or a court, you can quickly tell if a high level of ability is present or not. Athletes in particular can quickly assess other players.
My belief is that skill with statistics, data, and analytics is also based in large part on an inherent capability not unlike athleticism. Someone with the right inherent skills, especially if they are motivated, can achieve a very high level of analytical prowess. At the same time, people who struggle with math, don’t understand how complex data fits together, and /or are uncomfortable with how analytics work will likely never become more than mediocre at analytics.
The good news is that if you have hired the right people with solid inherent analytic skills, they will be able to adapt to new tools, new techniques, and new problems without too much pain. It will require some effort, but it is totally achievable just as it is achievable for good athletes to play multiple sports. It isn’t as easy for a layman to see analytic talent as athleticism. However, analytic managers who are good and knowledgeable can see the analytic talent in others easily.
Learning Language Versus Learning A New Language
Another area that many people worry about is whether pursuing a different tool or analytic programming language means having to hire entirely new people. Again, I believe this is a misguided concern. If someone is a rock star when it comes to programing analytic processes in SAS, they can become a rock star with R. A person who can code at an elite level in R can become an expert in SAS. The same goes for Python or any other language. Why is that the case?
The reason is that the really hard part about programming is learning how to program in the first place. The concepts of conditional logic, merging / joining data, and the like take some getting used to. Regardless of what programming language you start with, you’re going to have to learn how it all works. However, once you know how to program, it is pretty easy to learn a new programming language. If you understand the exact logic you need to apply and exactly how your process needs to flow, you’ve done the hard part. Programming in a new language is simply translating the logic you have formulated into a new syntax. That is far easier than learning your first syntax.
In this regard, I see a tie to how we learn language. As a young child, it is a major task to learn to communicate with both written and spoken language. What is a noun? How do you organize a sentence? These are things we take for granted today but that were not at all obvious when we first entered the world. Once we understand what language is, it is relatively easy to learn a new language. If I know exactly what I want to say and why I want to say it in English, it is a somewhat tactical and mechanical process to translate those words into German or French instead.
The Take Away
If you focus on hiring the right people who have a strong level of analytic athleticism, your organization will be well prepared for the future. Just as good athletes can spot one another, so can people who are good at analytics.
Similarly, remember that learning to speak a new language is easy compared to learning to speak in the first place. In the same way, someone who has shown they understand analytic logic and programming in one context will not have much trouble applying that knowledge to a new language or environment.
Don’t underestimate the capabilities of those you’ve already invested in. Chances are they are ready to step up and lead you on your new journeys.
Originally published by the International Institute for Analytics
