I had a plan, and I had data. I should have been good to go, right? But we all know what happens to the best-laid plans.
So, what went wrong? It was the data. Or rather, it was what the data couldn't tell me. Or rather, it was what I could not get out of the data.
It was intended to help us generate a list of top-performing credit unions. We would then profile them in a special report.
As a journalist, I know that if I call 10 people to interview them, I can assume that, even if what I want to interview them about is totally positive and will put them in the best possible of lights, only a handful will actually take and/or return the call and then only a fraction of those will agree to be interviewed.
It's why, even though we only needed maybe four or five credit unions for this story, the plan called for generating a list of more than 20.
But how do you define a "top-performing" CU? What metrics should you consider? We had capital, return on assets, return on equity, loan growth, asset growth, membership growth, earnings, net worth, loan performance…and the list goes on. We had a whole lot of data.
If I learned nothing else from this experience, it is that there's a very good reason I'm not a data analyst.
But what it really put in perspective for me was the enormity of the task credit unions are looking at when they talk about data analytics. It's not enough to have the data—you also have to know how to read that data, how to scrub it for outliers and other elements that can skew what it's telling you.
You also have to know which data to pull in the first place...and how all that data is interrelated. No sooner do you add one element—say, loan growth—when you realize another element is also needed, because you can't look at loan growth without also looking at loan performance. After all, what good are all those new loans if all of them are bad?
Next thing you know, you've gone down multiple rabbit holes with nothing to show for it. It also highlighted something that data—no matter how much of a whiz you are with a spreadsheet—simply can't tell you, such as the impact a given credit union has had on the lives of its members.
Do you know what your data is telling you? Are you looking at the right data?
And are you looking beyond that data?
Editor in Chief Lisa Freeman can be reached at
