Data + analytics
+ visualization = insight. And insight yields outcomes, right? Possibly, but achieving the desired outcome becomes much shakier without understanding why
the data’s being reviewed. Without a why, the timeless ready-fire-aim problem arises.
Now, don’t get me wrong; on more than one occasion I’ve waded chest-deep into a data set hoping to find something useful and sometimes have. In most cases though, I find:
- Focus on the critical question lacking or
- Inappropriate data to answer the question
Just last year, I helped with a high-priority project in advance of an upcoming executive meeting. I was given an inadequate set of data and asked to discover what caused a particular situation. The haphazardly collected data was not only incomplete but hadn’t been purposefully collected with the why in mind.
Regardless, I dove head-first into analytical method after analytical method attempting to generate any meaningful insight. At conclusion, the focus became storytelling to describe the nuanced insight rather than a focus on actions to solve the situation.
As data sets get bigger and algorithms get smarter, it can be tantalizing to lean toward a haphazard approach of finding a question to answer versus answering a question that’s been asked
. So, as you embark on your next extreme data challenge, ask yourself, do I feel lucky or do I know why
I’m drowning in data?