Recently, in speaking with the President of Kobie regarding my team, I used the term “data munging” to describe a lot of the work that we do. He laughed, thinking I had said “data munching” (mmmm, tasty!) and asked if that was a technical term. The short answer is that yes, it is another term for data wrangling (which, incidentally, is one of my favorite terms in the industry).

In looking for ways to prove that the term was real, I searched for it (because Google knows all) and found a really cool, fun article on The Three Sexy Skills of Data Geeks. In case you’re wondering, these skills are Statistics, Data Munging (hence the tie-in with the search), and Visualization.

These really are the great trinity of data work – and correspond nicely to the position I’m often looking to fill in my group of Database Marketing Strategist. Most often I will find someone with one or maybe two of these skills but not all three. For instance, I have a Statistician on the team with little data analysis experience. They rely on the Data Analysts, who can ETL and query the heck out of a database (and use programming languages I don’t dare touch including RegEx, my sworn enemy). Once models are complete, they go to the Strategist for interpretation and packaging/visualization to the business, as well as development of strategic and tactical approaches for use. While Strategists usually have some knowledge of Stats, it’s usually more comparative than predictive.

What we’re talking about here is a business-facing Data Scientist. And given just how much press this emerging field is getting, it’s definitely something for the budding data analysis geek to consider. I will say that it’s not always the most glamorous of workloads. In fact, the last statement of an article from TechReview is just about spot-on. More often than not, even Data Scientists, with as sexy a title as the industry can provide, end up doing a lot of grunt work to clean up bad data.

And for any of you out there reading this thinking that it’s not possible to gather all the skills necessary to get into this field, I encourage you to start with Coursera classes. There’s some startling revelations abounding around the ability to take some basic programming and machine learning and make it sing in the world of analytics. It doesn’t necessarily require a PhD in a physical science or mathematics or programming theory. Data Scientists can come from all walks of life.

So if you’ve got the inclination to work with lots of data, curiosity for determining why something happened and what you can do to predict, or even change, its occurrence in the future, take a quick course or two and start making it happen. We need you in the industry!