I'm amazed at the viral popularity of the term "big data." Initially, I was excited since the phenomenon seemed to be a bellwether signalling that businesses and their leaders had finally recognized the importance of data in decision-making.
But then I read an article by Christopher Mims on Quartz: "Most data isn’t “big,” and businesses are wasting money pretending it is." And I realized I had been about to fall into the same trap as so many others. You see, the data I analyze these days is big - but only to me.
It’s hugeness on a personal scale - I think that's why the phrase has resonated with so many - it articulates so succinctly (without sounding like a scaredycat) the challenge that any amount of data presents to a business - what's the key finding out of all of this data, and am I seeing it?
That's often my biggest fear, and probably the underlying reason for why I am so painstaking in running multiple queries, pivots, scenarios, and reports. I, like many other data junkies, don't want to miss the key insight that we're sure is lying in wait. It's there, in the numbers, wanting to be teased out just like Fermat's Last Theorem. And, like Big Macs, we've Americanized our fear of missing it by throwing everything we can at the data.