Data Illiterates and the Glenn Gould of Excel
I just finished reading Data Fluency: Empowering Your Organization with Effective Data Communication by Zach and Chris Gemignani.
Of all people, this book got me thinking of Glenn Gould. Yes, the eccentric Canadian pianist.
You see, Glenn had a complicated relationship with the piano. From what I understand, he found the piano an obstacle to getting at the music. It was an ugly, messy tool.
(Musicians — if you have more research on this, please pass along. I heard this in a lecture once and am having trouble tracing the source.)
This bit of his biography somewhat corroborates this:
He liked to call himself “a Canadian writer, composer, and broadcaster who happens to play the piano in his spare time.
So what does this have to do with Excel?
Of all the silly memes I’ve created, this one remains my favorite:
This blog in part was born by the observation that I wasn’t doing my best at work, and poor data was largely at fault.
“So, when am I gonna get to use those elegant financial and statistical models I learned in school?” I thought to myself.
Yeah…. not until you spend the next six hours mapping out journal entries because nothing is documented consistently.
It is ugly and painful but necessary — if I wanted to be a good analyst, I had to master data.
So data is my tool to business analysis like the piano is to Glenn Gould. I love business and economics, and I just happen to use data.
What does this have to do with… data fluency?
Let’s be honest… putting most “data fluent” analysts next to the average coworker or manager would be like asking Glenn Gould to do a duet with William Hung. Actually, Gould may have loved Hung, who knows? You get my point.
Data fluency is the quality of knowing how to translate data into business decision-making. It is a means to an end.
But much of the time, instead of using data like this — we get fixated on it, like here:
When individuals and teams within an organization achieve data fluency, unproductive discussions about “where did that data come from?” and “what does that value mean?” are exchanged for deeper discussions about key organi