As more credit unions design and test their approaches to data analytics, a few common traps that slow success are emerging. During their talk at the 2018 NAFCU Annual Conference, our own Tim Peterson and Shazia Manus talked through five of these pitfalls and offered advice for side-stepping them.
A little while ago, I was giving a talk on predictive analytics, and a young professional in the audience interrupted me: “What exactly is a data scientist, anyway?” he asked. Given the number of people who suddenly perked up to hear the answer, I realized he wasn’t the only one in the dark. If you’re wondering, too, allow me to shine a little light science on the topic. Let’s start with data science.
In 2012, the Pittsburgh Pirates had a few big problems: they hadn’t made it to the playoffs for 20 years, attendance at games was down, and team morale was low. But, against all odds, they made it to the playoffs in the 2013 season (as documented in the book “Big Data Baseball” by Travis Sawchik). How? By leveraging their biggest and most robust asset. No, it was not an all-star player nor new equipment, it was their data.
In today’s data-driven world, credit unions understand the value of insights that can be gained from member data. Yet, for many, there’s uncertainty on how to make that happen, along with the usual technology growing pains. Creating a data platform is a great first step, and segmentation and predicting member needs is even better.
Amazon. Google. Netflix. We know these brands as the head honchos of data management and data analytics. In many ways, they’ve written the handbook for a game in which both the rules and the playing field change in the blink of an eye. If keeping up with that rapid evolution is can seem daunting, the good news is that data collection and analytics continues to become more accessible in a way that credit unions can utilize and maximize.
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