The Value of a Business Analytics Capstone Project
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University of Notre Dame Mendoza College of Business
It’s fashionable for business leaders today to tout their sophisticated data science initiatives. Yet the path between identifying business problems and realizing the business value is not often clear.
A reality for many enterprises is that they have:
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diverse individual customers, globally, with growing brand expectations.
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ever more complex customer offerings - each with quality, lead time, conversion rate and dozens of other success metrics.
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growing complexity in managing the ethical and regulatory aspects of their digital offering.
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many partner suppliers with their own individual contributions.
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lots of data…
Certainly, data creates potential. The means of unlocking that value is not a so much role or set of skills (i.e. data scientist), but a way of working - a workflow.
Cassie Kosyrkov of Google, in a recent Harvard Business Review article titled What Great Data Analysts Do — and Why Every Organization Needs Them:
“While statistical skills are required to test hypotheses, analysts are your best bet for coming up with those hypotheses in the first place. For instance, they might say something like “It’s only a correlation, but I suspect it could be driven by …” and then explain why they think that. This takes strong intuition about what might be going on beyond the data, and the communication skills to convey the options to the decision-maker, who typically calls the shots on which hypotheses (of many) are important enough to warrant a statistician’s effort. As analysts mature, they’ll begin to get the hang of judging what’s important in addition to what’s interesting, allowing decision-makers to step away from the middleman role.”
Caitlin Malone and Ben Jaffe in their Linear Digressions podcast episode Data Scientists: Beware of Simple Metrics observe the critical role of business engagement:
“Picking a metric for a problem means defining how you’ll measure success in solving that problem. Which sounds important, because it is, but oftentimes data scientists only get experience with a few kinds of metrics when they’re learning and those metrics have real shortcomings when you think about what they tell you, or don’t, about how well you’re really solving the underlying problem.
A classic example of metrics run amok is the Wells Fargo debacle, where employees opened 3.5 million deposit and credit card accounts without customers’ consent in an effort to implement its now infamous “cross-selling” strategy. In addition to paying initial fines ($185 million), reimbursing customers for fees ($6.1 million), and eventually settling a class-action lawsuit to cover damages as far back as 2002 ($142 million), Wells Fargo now faces headwinds in attracting new retail customers.
Don’t Let Metrics Undermine Your Business
Michael Berthold writes in What Does It Take to be a Successful Data Scientist?
“But is a theoretical education sufficient? My answer here is no. Data science is as much about knowing the tool as it is about having experience applying it to real-world problems, about having that ‘gut feeling’ that raises your eyebrows when the results are suspiciously positive (or just weird). I have seen this countless times with students in our data science classes. Early on, when aspiring data scientists start working on practical exercises, no matter how smart they are, they present results that are totally off. Once asked ‘Are you sure this makes sense?’ they realize and begin to question their results, but this is learned behavior.”
Analytics leaders create value by championing integrated, rigorous and ethical work, no matter the industry or the size of the team. They foster the tough conversations, asking the sharp questions. Capstone projects create opportunities to collaborate to realize tangible business value in ways that coding class, bootcamps, and Kaggle cannot.
University of Notre Dame Mendoza College of Business MSBA Capstone
