Dear Data Scientists,
Please Stop Defining Data Analysts… It’s Embarrassing
Somewhere, out there, another article is being written attempting to distinguish Data Scientists from Data Analysts. It is only natural. When you re-brand old things, people get confused. Those who believe it was more than just a re-branding get defensive. We all want to feel special, but meaningless distinctions and imagined certainty don’t help people succeed in the real world. So maybe it is time to be honest?
Look, I can’t really speak for “All” other DS&A professionals, but I do speak to a lot of them and we aren’t keen on what we are reading. For starters, your definition of data science is … well rather untoward.
This is rather ironic given how many of these posts attempt to distinguish data scientists as ingenious predictors. Remember when there was an analytic discipline known as forecasting… no, I suppose not. It pre-dates most millennials. Many now believe there is a predefined R function for it.
For that matter, why are all of you so preoccupied with your tools? R, Python, Jupyter Notebooks… yawn. Remember SAS, VB, Crystal Reports … oh, the last one gave me shivers. These things come and go. Imagine if we defined other trades by the tools they used? What makes you a plumber? Well, I use a monkey wrench! And I am a mechanic because I use a Torque wrench! Reminds me of some 80’s cartoons and action figures… damn, I am getting old.
Let’s face it. We ALL use SQL. We ALL need to understand the business, the P&L, and statistics. Of course by some of your logic — we data analysts are also statisticians. You know, because we use Excel… Hey, remember when data scientists were called statisticians or statistical modelers? Did the name change when you all moved to Google Sheets? Ouch, maybe that is why you seem so defensive?
Look the reality IS that data science and data scientist are the new catch all in the business world. As other titles go out of favor, it appears more and more popular. Perhaps we should all remember how to actually analyze trends and stop buying our own hype? Is there a Keras API for cannibalization?
Meanwhile, the title Data Analyst is more and more relegated to the IT and Engineering departments. Somewhere along the way, people started putting too much into that Data adjective. But it is all subjective!
A data scientists is whatever the job description defines it as. At least until the actual role turns out to be something quite different entirely. I laugh at the number of meetings I have with third party “data scientists” who immediately start back tracking. “Well Mr Earl, you are far more of data scientists than I am…”. Look, Mr Earl is my dad and people call me a data scientist, too — I am just ornery enough to correct them on both accounts.
The sad reality is, most people writing the job specs are really quite clueless, too. They aren’t sure what a data scientist is OR what the individual they ultimately hire should do to actually earn their paycheck. Perhaps this is why the very same folks who endlessly blog about the definition of data science also now tell us that all data scientists are leaving?
Is this further irony… or do the layers make it deep learning? Uggh, I guess we should ask what tools they are using? I mean besides SQL. What was wrong with just calling all of us analysts? Did we really need all this distinction? Rather than spend so much time defining titles, could someone just build a useful data dictionary? That is something we can ALL agree on…
Business analyst, data analyst, data scientist, or data visualization specialist (yeah, no one is talking about that last one… yet) — none of us got much from our degrees that really applied to the business world and none of us are keeping our jobs if we don’t adapt to what our business really need of us. So stop the false distinctions and go analyze something that grows the revenue or cuts the costs! Wear whatever title you want but stop pretending it is all so clear, certain, or has anything to do with your favorite software package. That doesn’t help anyone get or keep a job in DS&A.
Oh … and thanks for reading!
