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4 years ago
What Is the Best Way to Get More Women into Analytics? Don’t Make Them into Men


Originally published in Linkedin

I recently sat on a panel of “Women in Predictive Analytics”, moderated by Greta Roberts with Jeanne Harris from Columbia University and Lauren Haynes from University of Chicago.  Although the three of us on the panel had never previously met, we were incredibly consistent with our perspectives on how to get more women into analytics – don’t try to make women into men.

This point was made several different ways. I will highlight two here.

The first point was that “the truth is one but the paths are many” (Gandhi). Many of these “Women in STEM”-type panels almost always include deep lamentations and gnashing of teeth related to the dearth of women in computer science. To that I say “meh”. While anyone (regardless of their estrogen levels) needs to know how to code, computer science is only one path to analytics. In fact, I think I could make a strong argument that computer science may not even be the optimal path to becoming an analytics professional. All of us on the panel discussed examples of great students and practitioners who came into analytics from the social sciences – Psychology, Marketing, Sociology or the health care sciences – Epidemiology, Biology, Biochemistry. These disciplines tend to overindex with females. The people that came into analytics and data science from these disciplines typically did so because they had a passion to solve a problem or research a question that required the translation of massive amounts of structured and unstructured data into information. Learning the computer science dimension of analytics was important, but was not the focus – it was a necessary part of the path to get to the “truth”. These “accidental data scientists” often then find themselves with a pivoted career path – applying analytics to their domain of interest. Channeling these people – again who overindex female – into mathematics or computer science, may have ultimately achieved the opposite result. Everyone needs to find their path.

The second point was related to the “composite unicorn”. Most in our discipline have accepted that breeding analytical unicorns is not the right approach to closing the talent gap. The alternative is creating “composite unicorns” through the attentive construction of an analytical team with complementary skills. I highlighted an example from our own university – the analytical “shoot out” course. In this course, companies come into the classroom in the beginning of the semester with a dataset, a series of pain points, and an analytical challenge. Then they go away. We then put the students into teams. These teams include students who may have studied Psychology, Economics, Sociology, Physics, Electrical Engineering, Math, Nursing… Over the 15-week semester, the teams have to work together – leaning into their individual skills and talents – to complete a daunting analytical challenge in a short period of time. It’s incredible to watch these groups figure out how to leverage the unique intellectual orientations that the different disciplines (and genders) can bring to the project. In the context of this team-based shootout course, the students arguably experience more latent learning related to appreciating the contributions that come from studying lab sciences versus computational sciences versus social sciences…all in the context of analytical problem solving. Again, these contributions are not gender specific or even necessarily gender aligned…but the students learn that a complete analytical team – a composite unicorn – needs problem framing, domain experience and communication skills as much as it needs programming and mathematics.

It was a great panel – I was honored to be included with three inspirational thought leaders in this space. In the end, we all agreed that data scientists and analytical professionals take a lot of shapes and sizes. And the nascent discipline of data science can benefit from all of these – and all their reproductive parts.

About the Author:

Dr. Jennifer Lewis Priestley, Ph.D., is a Professor of Applied Statistics and Data Science at Kennesaw State University, where she is the Director of the Center for Statistics and Analytical Services. She oversees the Ph.D. Program in Advanced Analytics and Data Science, and teaches courses in Applied Statistics at the undergraduate, Masters and Ph.D. levels. In 2012, the SAS Institute recognized Dr. Priestley as the 2012 Distinguished Statistics Professor of the Year. She served as the 2012 and 2015 Co-Chair of the National Analytics Conference. Datanami recognized Dr. Priestley as one of the top 12 “Data Scientists to Watch in 2016.”

She has authored dozens of articles on Binary Classification, Risk Modeling, Sampling, Applications of Statistical Methodologies for Problem Solving as well as several textbook manuals for Excel, SAS, JMP and Minitab. Prior to receiving a Ph.D. in Statistics, Dr. Priestley worked in the Financial Services industry for 11 years. Her positions included Vice President of Business Development for VISA EU in London, where she was responsible for developing the consumer credit markets for Irish and Scottish banks. She also worked for MasterCard International as a Vice President for Business Development, where she was responsible for banking relationships in the Southeastern US. She also held positions with AT&T Universal Card and with Andersen Consulting.

Dr. Priestley received an MBA from The Pennsylvania State University, where she was president of the graduate student body, and a BS from Georgia Tech. She also received a certification from the ABA Bankcard School in Norman, OK, and a Certification in Base SAS Programming, and a Business Analyst Certification from the SAS Institute.

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