“The more people are able to speak data science, the better.” ” – Derek Franks, IBM Center for Applied Insights Analyst
Our Blogger Beat series features our top bloggers to gain a little insight into what makes them tick. In our last Blogger Beat, we met a blogger who told us about brain implants to learn Mandarin (is that ‘out of the box’ or what?). In this post, we meet another fascinating blogger who has been known to use data science to decide which used car to buy and even to analyze his newborn’s eating and sleeping behavior.
Meet Derek Franks. As an analyst with the IBM Center for Applied Insights, his primary focus is deriving insights and value from data. He does a lot of work with quantitative data analysis, statistics, predictive modeling, and machine learning. Prior to joining the Center, he worked with the IBM retail industry solutions group. He has a bachelor’s degree and an MBA from Texas A&M.
Tell us a bit more about yourself – Who are you? And what do you do in your spare time? I live just outside Dallas, TX with my wife, Beth, of 9 years and our 11-month-old son, Julian. Most of our spare time for the past year has been focused on diapers and bottles, but otherwise we enjoy traveling, exploring the outdoors, and camping. Our family has a ranch in South Texas, so we spend time there. We also like to visit Whistler in British Columbia, Canada.
What are you currently working on or are most excited about? The projects I’m typically most excited about are those that give me a chance to leverage more sophisticated analytics techniques to drive quantifiable value for the business.
Recently, I’ve been working with a team that has access to some new data sources that allow us to build models that make some surprisingly accurate predictions about specific market behaviors.
Why do you like to blog about data science? It’s easier to write about topics that you’re interested in and passionate about. Data science, and all of its related topics, ticks both of those boxes for me, so it’s really a good fit.
How would you explain data science to a kid who knew nothing about it? Frankly, it’s difficult to articulate a definition of data science that everybody will agree on. But generally speaking, I think it’s safe to say that data science is focused on developing an understanding of trends and patterns within data. Often, the goal is to build predictive models that help enterprises act more effectively.
To me, data science sits in the intersection of statistics, computer science and domain knowledge. A data scientist needs to have a certain level of knowledge and experience in all of those categories. Additionally, an often-overlooked aspect of data science is the ability to communicate your findings effectively to stakeholders and help them with implementation. The best insight or coolest algorithm in the world isn’t worth anything if it just sits on a shelf somewhere.
Are there other experts, bloggers, articles or books that you find interesting in this space, or that you have drawn inspiration from? A great book, especially for somebody without a technical background in statistics and machine learning, is “The Signal and the Noise” by Nate Silver.
Where do you see technology creating the greatest impact? Obviously, technology impacts our lives in all sorts of ways. But to me, one of the most profound impacts is how it’s changed/changing predictive modeling. Techniques that were effectively theoretical 30 years ago are now in widespread use.
Also, although I’m not particularly a fan of the term “big data,” we certainly do have access to more data than ever before. And when you combine all of that data with these increasingly sophisticated predictive modeling approaches, we see an increased ability to predict all types of behavior at a variety of different levels.
This is having a huge impact on how we live our lives. But because it’s not often visible to us, we don’t realize it’s even happening.
Can you share a personal AHA moment about the value of an emerging technology? My “aha” moment with the value of data, statistics, analytics, etc. was about twelve years ago when I read Moneyball. The book exposed me to the world of Sabermetrics, and how a data-driven approach to baseball let the relatively underfunded Oakland Athletics compete head-to-head with teams like the Yankees and Red Sox that seemingly had limitless resources.
Recently, I walked our team through some predictive modeling that replicated analysis of the Oakland A’s in Moneyball using historical data as a part of an education session. It’s startling how accurately the data predicted their performance in the 2002 season.
Can you share a funny story about when you’ve been “incredibly wrong” about a technology trend? (e.g., didn’t initially see the value, thought market would head a different way, etc.) To be completely honest, for a long time I thought Twitter was a waste of time and bandwidth. Although I still don’t spend that much time on it, I’m starting to come around. Clearly, there are plenty of people out there that saw things differently than me.
What’s next for you? I’m going to continue to look for new and interesting data sets and data science project opportunities.
I’ll also continue helping others interested in learning more about data science and developing their own knowledge and capabilities. The more people who are at least able to “speak” data science (even if they aren’t practitioners themselves), the better. Data scientists benefit from having educated consumers for their work.
Also check out the new IBM Center for Applied Insights study on Data Science.
Originally published on the IBM Center for Applied Insights blog Nov 2015