Women in Analytics: Katherine Sanborn, Kellogg Company
By: Kelly LeVoyer, Marketing Editorial Director, SAS
For Katherine Sanborn, science and math were always like part of the family. Dinnertime conversations with her parents, both pharmacists, often revolved around the latest exhibits at the local science museums or the results of Sanborn’s latest chemistry class experiments.
“Having grown up around scientists, you’d think I would have gone in the direction of biology or medicine – and I considered it, but I was always drawn more towards mathematics,” Sanborn says. During her elementary and secondary education in a small town in Michigan, she attended math-focused events at local universities that specifically encouraged girls to participate, educating them about different careers in STEM fields. “Math camp, Science Olympiad … it was all around me from an early age,” she says.
And while her love of math was clear, she found her way to a career in analytics by way of business and economics. “In college, I became drawn to the practical application of math – in business, statistics and especially economics,” she says. “I loved the study of both the macro view of economics and the very micro views, like how people make purchasing decisions every day.”
Try out SAS Visual Analytics
Get pertinent answers from any-size data. Smart data exploration makes it clear. Interactive reporting makes it collaborative. Self-service analytics makes it usable by anyone. And scalability and governance make it the perfect fit.
Try it or buy it
Off on the right start
Sanborn is quick to recognize that not everyone who has innate skills for math-related subjects ends up cultivating those skills. “If young people, especially girls, have a bad experience with math or science, it can be difficult to overcome,” she says.
“My advice to rising college students is to try a variety of classes, and to not be intimated by the titles of the classes. If you didn’t excel in math in high school, consider taking college math since classes are taught differently. Just because you didn’t do well in math in high school doesn’t mean you won’t in college.” In fact, Sanborn took an undergraduate programming class that she didn’t like very much, but, she says, she was introduced to SAS in graduate school. “And then it clicked. It came easier to me the second time.”
After getting her BA in Economics from Saint Mary’s College in Notre Dame, Indiana, Sanborn took time to evaluate other career options including attending law school. While in law school she discovered her strong desire to be involved in a quantitative field again. “In law school I learned a very valuable lesson about engaging in subjects you are truly passionate about. I also believed in my own voice enough to course correct.” Sanborn then went on to get her Masters in Economics in her home state at Western Michigan University.
Through an active recruiting program in the Michigan area, consumer giant Kellogg Company selected Sanborn for a business analytics internship with the company’s pricing team. It was there that her fascination with consumer economics and math fueled her learning about how analytics can guide important business decisions in the consumer packaged goods industry.
“Companies who offer internships need to make them practical and useful for the students,” Sanborn says. “From the moment I started at Kellogg, I was working on relevant projects, and I really felt part of the team. Analytics can feel very theoretical when you’re in college, but when you work on price elasticity and you see how consumers react when prices are adjusted, it becomes very real – and very motivating!”
Sanborn was offered a permanent position at Kellogg and has been working on multiple business analytics teams focused on providing analytical support behind promotional, merchandising, and display planning and optimization using solutions like SAS Visual Analytics.
My advice to rising college students is to try a variety of classes, and to not be intimated by the titles of the classes ... Just because you didn’t do well in math in high school doesn’t mean you won’t in college
Katherine Sanborn • Kellogg Company
Paying it forward
Sanborn is still a recent enough graduate that she can relate to the incoming interns and spends a lot of time mentoring and coaching them.
“Having come from a women’s college for my undergraduate study, I’m not focused on the difficulties women in particular might face in this field,” she says. “I was surrounded by women studying for STEM-related careers who have been very successful. But I do believe it’s important to have mentors model that long-term success – and for me it was my mother. She was a pharmacist in a time when women were not encouraged to explore science professions. She encountered her own struggles, she found a way through them, and she modeled that path for me.”
Now, it’s important to Sanborn to pay it forward by modeling the path of analytical skills and careers for the newest generation of professionals. She’s involved in expanding university engagement programs at Kellogg and is starting a SAS users group since it’s become such a hot topic.
She says, “It’s gratifying there’s now more awareness about what analytics is, and that it’s a serious discipline open to anyone. I want more people to consider it – especially those young women who are being told they’re not good at math or might have given up on it.
“What I love about my job is the ability to provide people with the information they need to make solid decisions that truly affect the business. They’re especially appreciative if we can present information in ways that are easily understood. That’s exciting to me, and I want more people to experience it."
Recommended reading
- Article What is synthetic data? And how can you use it to fuel AI breakthroughs?There's no shortage of data in today's world, but it can be difficult, slow and costly to access sufficient high-quality data that’s suitable for training AI models. Learn why synthetic data is so vital for data-hungry AI initiatives, how businesses can use it to unlock growth, and how it can help address ethical challenges.
- Article Unlocking a strategic approach to data and AIAI is only as good as the data that powers it – this is a fundamental truth about data and AI that defines the limits of what’s possible with artificial intelligence. It may seem surprising, but it's rarely a bad algorithm or a bad learning model that causes AI failures. It's not the math or the science. More often, it's the quality of the data being used to answer the question.
- Article AI anxiety: Calm in the face of changeAI anxiety is no joke. Whether you fear jobs becoming obsolete, information being distorted or simply missing out, understanding AI anxiety can help you conquer it.
- Article SAS AI Cities Index 2024 - where is the most AI ready in the UK?It’s poised to solve some of the world’s biggest challenges - from a climate change emergency to tackling dementia; boosting GDP, to improving the education system. Artificial intelligence (AI) is reshaping the world as we know it.