Analytics Has A Pigeon Problem
Data Science Doubly So…
Quick, name the Warner Brother’s pigeons! I bet most of you can’t. Don’t say they look the same — they really don’t. Of course, unless you were a raging fan of this segment of the Animaniacs — they likely look like any other three animated pigeons to you.
Squit — Bobby — and Pesto … didn’t want you to run off to Google it.
It is actually kind of interesting how much any three animated pigeons look a like. To most of us, pigeons are just pigeons — even the animated and talking variety. Isn’t that strange?
Analysts are a lot like pigeons, to non-analysts. People from other parts of the company have a hard time distinguishing us. Not as people. As analysts. Okay, some times as people, too. They just have no idea how to tell us apart. I have often thought that many business people love the idea of co-location simply so they can tell the analysts apart (developers, too — but you guys should get your own article).
“I should introduce you to our analyst!”
Not John or Sue. Not even our customer behavior analyst or pricing analyst. Although sometimes we get an adjective — data scientists never do.
It is a bit like naming your pigeon — Pigeon. Oh, wait.
Analysts aren’t a commodity. I am not sure pigeons qualify either. But both are readily commoditized by everybody else. This pattern grows exponentially with scale. Inevitably the top brass start wondering why they even have so many analysts, despite the repeated pleads for more. It is time to put everyone in a box.
Most Pigeonholes are boxes… or should I say cubes?
In theory, putting analysts in boxes makes some sense. It is actually a well known principle — the Pigeonhole Principle. Imagine that.
In a large organization, it is a way to understand which needs (boxes) are filled (occupied) by more than one analyst (pigeon). In reality, there are often more empty boxes than ones with dual occupancy. Regardless, the business happily announces that all dual occupancy pigeons will be given a new hole! If only, it worked that way.
Pigeonholes are all the same. Non-animated pigeons feel pretty much the same, too. Sorry — they are nowhere near as unique as the animated ones… So a re-sort makes sense… for pigeons! Analysis, analysts, and even data science (sorry) are not all the same. Being pigeonholed in analytics — is actually an opportunity to do something different! Though no one in management understands that and there is at least a strong chance you will be given a task you have no interest in OR no developed skills to do.
All of this goes a long way to explaining the average analyst’s view of the corporate structure. Viewed as a commodity, shoved in a cube, and nothing is likely to change unless you get up and fly away. Well… you can always redecorate! By the way, things aren’t likely to be so different at the next stop either. Same pigeons… different city. Enjoy the view.
It does pay well … assuming you can fight through the indistinguishable masses
Learn who is tossing the free bird seed and life can be easy. I am not sure what kind of existence or satisfaction that gives, but a full belly has its advantages. Is it me or do all pigeons appear a little fat?
Analysts and Data Scientist have a pigeon problem. True career success requires you distinguish yourself from the hungry masses. It requires that you build a broad set of experience despite your companies interest in putting you in a cube and giving you a life of meaningless repetition. And let’s face it — the data scientists are all plotting to build machines to take that away! Of course, as I noted, machine building is even less unique than most other analytics. So we are all in this together. Birds of a feather…
Thanks for reading!
Don’t be a pigeon and avoid the pigeonholes:
