@data_nerd: A Badge of Courage

 In Data Science

Carla Gentry, self-proclaimed @data_nerd and owner/founder of Analytical Solution, has been in the business for 17 years (and has owned her own business for the last 4 years). We had the privilege of interviewing her and hearing about how data is changing, and how it isn’t. The biggest takeaway – data means knowledge (which means power and profits) for companies. But, not so fast! The key is to have good data scientists who can turn that data into knowledge, and relay that knowledge to decision makers.

Tell us about yourself. How did you get started? What do you do today? 

As the owner/founder of Analytical Solution, I am a data nerd. Here’s my backstory: I entered the University of Tennessee at Chattanooga in the spring of 1993. I was a single mother of two sons, but I was eager to pursue my degree and take the next step (I certainly don’t like to back down from a challenge). There, I excelled at math and gained more exposure to advanced mathematics. During my entire tenure at UTC, I worked in the Developmental Math Lab assisting students with all levels of mathematics. In 1998, I graduated with a double major (Applied Mathematics and Economics), and moved on to start my career in analytics in Chicago.

Today, I act as a liaison between IT departments and Executive staff by deciphering business needs via large, complicated databases; I come back to their teams with intelligence that quantifies spending, profit, and trends. Over the last 17 years, I have worked with Fortune 100 and 500 companies, such as Discover Financial Services, J&J, Hershey, Kraft, Kellogg’s, SCJ, McNeil and Firestone.

I love my identity as a “data nerd.” It is a badge of courage. As a curious Mathematician/Economist, knowledge is power and companies are now acknowledging the importance and possibilities of accessing knowledge via data.

What types of companies do you work with?

I typically work with SMB companies primarily in education, health care, risk prevention, and data science as a service. I will work with anyone with data. For instance, most recently, I have been doing a lot of adhoc projects for the supply chain industry. 17 years is a long time and the clients and customers have been very diverse.

You’ve mentioned that data science has been around since 1974. What does the practice look like today versus what it looked like then?

When I started, mainframes and SAS were the “soup du jour.” In the 90’s, I started in RJKA (Ronald J Krumm and Associates) – it was a wonderful place, like college but with a paycheck. However, I was also told to forget everything I learned about analytics in college. I was told this is the REAL world and there are no textbook solutions like there were in college. After RJKA, and over the next two decades, I moved around a lot and learned an immense amount. However, having seen everything come full circle today, I have come to this conclusion: many act like “data science” and “behavioral analysis” is new. Go figure, but this is very much not the case. In fact, most of the data science/behavioral analysis tools have the same principles as seen before, simply coined under a different name.

Thinking about the term “big data,” does all data have to be “big” to be valuable?

Definitely not at all, though this is a common misconception within an ongoing big data vs. little debate. My article “Big Data needs Data Science but Data Science doesn’t need Big Data” explores how data science has been around for decades, and it’s not just big data. In fact, a lot of the data a typical company handles on a daily basis or houses internally is not big data. In my opinion, as I’ve shared in the article, what really matters in data science is the team effort and your role as a liaison, more so than if the data is “big” or not. The best data scientists embrace curiosity before “big data.”

What are some of the emerging tools and techniques that you are excited about? 

I rarely get excited about tools. Once you have seen them all, you understand, that they DO NOT vary greatly. What does vary is what tool is for which job. Oftentimes, if a company is small, it means they can’t afford SAS… and if a company is big, they’re likely using Hadoop.

In which industries or domains do you see data and analytics making a positive impact?

I see data and analytics making a positive impact in all industries that take the plunge and try it. Most specifically, medicine is a space where data and analytics are making a positive impact in a big way – like saving lives! Thanks to EHR (electronic health records), I hope that more can be done to find correlations and trends in medicines and pharmaceuticals in the future.

It seems that many companies are just starting to use data and analytics to drive their businesses forward and make better decisions. Do you agree? If so, what do you think is required for a wider adoption of data and analytics?

Yes and no. There are lots of sales people out there selling their wares and some are buying. Only time will tell the difference between all the hype and true dollar value; but if we weed out the gurus and only hire real talent, my guess is there will be lots of success! So the question is, “What can your data do for you?” and the answer is LOTS, if you are willing to listen!

What makes a good data scientist?

The best data scientists don’t just address business problems. They pick the right problems that have the most value to the organization. Some have described data scientists as “part analyst, part artist.” Moreover, a data scientist does more than simply collect and report on data. He/she also looks at it from many angles, determines what it means, then recommends ways to apply said data.

A good data scientist is also inquisitive – explore, ask questions, question existing assumptions and processes, and always approach data with “what if” analysis. With an inquisitive approach, armed with data and analytic results, a top-tier data scientist can inform and recommend techniques across a company’s leadership.

For more on what it means to be a great data scientist, as well as tips on best management style for data scientists, check out my article “Being a Data Scientist.”

You’ve written about women in technology. If you could give your high school self some advice, what would that be?

I’d tell myself: Don’t quit! Your life would have been SO much different if you had only had patience!

Great advice, Carla. Interested in telling us your backstory? Tweet us @6senseinc.

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