Rotman School of Management, University of Toronto

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Geran Wen speaks on last year's Datathon!

Every year, there is a growing interest and participation in the Datathon competition. Participants from around the world are tasked to apply their managerial, analytical, and communication skills to develop a strategy for a managerial problem set by one of Rotman’s industry partners. This blog post details the experience of Geran, a winner from last year’s Datathon competition.  

We had approximately two weeks to work on the Datathon, and the case was for a fictional regional bank based in US Midwest region that wanted to expand to Nebraska. So, the case gave you a cut of a pretty big data set of fixed-rate mortgages from Nebraska. And then you are to use that data set to help the fictional company to decide how to enter the region. They give us a lot of information, but the answer that they want is, in a way, open-ended. They want you to approach it as if you are a consultant advising bank board members. What should they do? What can you expect of that kind of thing?  

In the context of Datathon, we were presenting to the board member of the banks. So, you have to be sure that what you're presenting is something that the bank, or the board member can see and act upon. So, we want to go to Nebraska... how do we actually go to Nebraska? What's your proposal like one year, five years, ten years from now? That's what the banks in this situation want. Be sure to bring the business context into an analytical project. So, you've spent a lot of time thinking in this analytical mindset, and you think about how what can I get out of this dataset.  

But really, I think what differentiated our group, at the end, and winning the Datathon, was that we presented our conclusion in the form of a business presentation. We present it as if we had a fully-fledged, well- rounded deliverable to them. So instead of telling them “We found 123”, or “we built this amazing model that can predict with super high accuracy”, we presented several pieces of actionable suggestions, based on the technical things we did. Based on these ABC, we can recommend that the fictional company should adopt this market entry strategy. We think this recommendation is reasonable because of reasons 123. We incorporated the business elements, not just the data and the analytics.  

For someone like me, who came from an engineering background,  it is easy to get stuck in a data-based mindset. When you apply for a program like MMA (Master of Management Analytics), you think that it is going to be just about statistics, how one uses a data set,  and how that might be the core of your project. However, this Datathon prepares you to present information beyond the data to the client. You are presenting actionable information to a client that is backed by data.  

Don't get overwhelmed and don't get preoccupied by the data set. You must think about what you're presenting, who you're presenting to, and how to put all the analysis in a context that makes sense to the client.

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