Sandy (Sandra) Dias is doing a Kellogg-Schulich Executive MBA. She has more than 10 years of sales experience and has spent the past six years working for L'Oréal Canada. She is both a national key account manager and a district manager for Quebec and Ontario boutiques for the luxury brand, Kiehl's Since 1851.
When I look back on my education, there are classes I remember fondly and then there are classes I don't. One of the classes was statistics 101. I remember walking into my undergraduate class and getting a feeling of dread. I knew I was walking into a class where I would most likely walk out more confused than when I had walked in. So you can imagine my excitement when I found out statistics would be a part of Module 1 of the Kellogg-Schulich EMBA.
But it came as a pleasant surprise that this time around it wasn't so bad. Am I older? More invested? Sure, but the difference was there was a true purpose to everything that was being taught. In other words, the approach was all business. The class was not easy for me, the verbiage is intimidating, and there were serious moments of #$%^&! But eventually you get over it and start connecting the dots. If you are not from a business background, or haven't taken statistics or quantitative methods, it can be very challenging - and I really recommend doing the readings before class. What saved me (and I suspect others) was that we applied what we learned to problems immediately. I didn't understand it all right away but I at least knew what direction I was going in.
In the end, I was pleased with the takeaways. For example, linear regression is a great tool for predicting and forecasting. If you have a few variables to start with, you can very readily predict differing influences over your business and have the statistical analysis to back it up. Moreover, if you hire a consulting firm to do that work for your company, you are now equipped with some simple key indicators allowing you to ask the appropriate questions regarding their findings.
Decision trees were another simple tool that I had never been exposed to. A decision tree is apparently one of the most systematic tools of decision-making theory and practice. Trees are particularly helpful in situations of complex multistage problems. It's a useful tool when you need to figure out how a sequence of decisions affects the possible outcomes - helping you determine the decisions your company should make. It can be as complex and as simple as you need it to be. I was struck at how useful a tool it would have been for my father, who owned and operated a grocery store, to help him manage inventory and maximize his revenue - something he could have worked out on the back of a packing box with a pen and calculator in hand. I wonder why it isn't a tool more readily used on a day-to-day basis.
One of the last major topics we covered was optimization, which, as its name suggests, is designed to help figure out a system or process where the outcome is as good as possible within whatever parameters you have. Often used to analyze growth and profitability, the "optimal value", it was a great exercise in deciphering, through word problems, answers to everyday challenges that come up in business. Once you had the problem set up, it was as easy as putting it into Excel, and the program would "solve" it for you.
The overall goal of the class was to make sure we understood the concepts, but ultimately it was about taking those concepts and putting them to work. We were coached (patiently) by our professor on how to run linear regressions and Solver in Excel - two simple tools that "do the math" for you, and give insights on how to analyze the results.
I am not claiming to be any sort of statistician after taking the class. Instead, I feel confident I came away with three very useful and specific tools I can use in my professional life. In the end, statistics may not be the class I look back on as inspiring my most stellar performance, but there will be no feelings of dread going forward. Moreover, I did not finish the class more confused than when I started - and that's worth a smile.Report Typo/Error
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