Let me start out by saying I realize the idea that data can never be misconstrued or altered to fit a particular narrative is complete hogwash. I’ve learned through my professional and academic experiences that data can, in fact, lie. However, if you’ve clicked through and are reading this, then my devious headline worked — so please hang on just a bit longer.
Last week, CreditUnions.com featured a series of benchmarking and data mining articles. The most popular feature, 4 Ways To Benchmark Credit Union Performance by Callahan analyst Janet Lee, offered an overview of the pros and cons of four popular financial performance metrics as well as a discussion on the factors that influence each ratio. After all, knowing the potential drawbacks of using one metric over another is just as critical as determining how you want to set goals and define success in the first place, and the reader feedback below reinforces the need to look at a range of factors when setting performance benchmarks.
John H.: As fond as the financial services industry is of peer comparisons, we have to keep in mind that it’s never a one-size-fits-all situation. The credit union’s business model, field of membership, and strategy all shape the interpretation of performance ratios. Importantly, credit unions need to focus on member-denominated metrics to ensure the majority of those relationships are deep and healthy.
Tony H.: The member/employee ratio can be hard to use for comparisons. One could argue that a highly efficient credit union has low member engagement. I think membership growth is an important metric, but some credit unions are in areas of low or negative population growth. ... The best credit unions tend to be those that are growing faster than peer averages, as a reflection of demand for their products and service.
And now we’re back to my “data doesn’t lie” premise. It’s not uncommon for a credit union to set a goal, determine what it wants to benchmark, and then be surprised at the results. But that’s not a bad thing, that’s the power of data. At its core, data is impartial and is more powerful than human observation in identifying a credit union’s strengths and weaknesses.
According to Wired author Clive Thompson, “The way we observe the world is deeply unstatistical.” In his April 11 opinion piece, Thompson talks about the annoying, incessant family-oriented posts of new mothers — the over sharing, the flood of baby pictures, the minute-by-minute accounts of what genius miracle baby was doing at that exact moment, etc. It’s nauseating … and it’s a complete misperception. According to a Microsoft Research study, new moms are actually less active on Facebook (the article did not state whether that is less active than they were previously or less active than the average poster).
The research cites Facebook’s algorithmsas as contributing to the perception that new moms post more, but Thompson points the finger at frequency illusion. Once something has caught your attention, for better or for worse, you see it everywhere. And that illusion creates biases whether about new mommy baby posts or credit union performance trends. And that’s where good, clean, hard, impersonal data comes in handy. When used correctly, it cuts through the bias and provides an accurate portrait of credit union performance.
To paraphrase Thompson, there is value in observing the world like a scientist and seeing what’s actually going on instead of what happens to catch your attention.