There are data geeks and there are data scientists. Then there are data plumbers, and there are total posers. In this modern world where the line between “real” and “fake” is ever-blurrier, some may not even care for such differences.
Call me old-school, but at least in some fields, I believe that “the ability to do things” still matters. Analytics is one of those fields. When it comes to data and analytics, you either know how to do it, or you don’t know how to do it. The difference is as clear as a person who can play a musical instrument and one who is tone-deaf.
Unfortunately, there is no clear way to tell the difference in this data and analytics field. It’s not like we can line up contestants and ask them to sing and be judged here. Furthermore, “posers” often have louder voices — armed with fancy visuals and so-called automated toolsets.
I’ve been to many conferences and sat through countless presentations in my lifetime. It may sound harsh for me to criticize fellow data players and presenters, but let me just come out and say it: A great many presenters and panelists at conferences are posers.
How do I know that? Easy. I asked them. For example, when I stalked some panelists who preached about the best practices of personalization after the session, the answers were often “Well, it is not like we do all those things for real …” Sometimes I didn’t even have to ask the question, as I could tell something is seriously broken in their data and promotion chain by observing their marketing messages as a customer.
The bad news for the users of information — and for consumers, for that matter — is that it takes a long time to figure out things are not going fine. Conversely, we can all tell who is tone-deaf as soon as a singer opens her mouth. It is so hard to tell the difference between a data scientist (i.e., an analyst who provides insights and next steps out of mounds of data) and a data plumber (i.e., supposedly an analyst who moves big and small data around all day long and thinks that is his job), that I admit it sometimes takes a few months — generally after some near meltdowns — for me to figure it out.
Hi Stephen:
I enjoyed this piece! Can you weigh in on your opinion of preparing for a career as an authentic data scientist by attending a boot camp after college, such as NYC Data Science Academy? My son is interested in pursuing a career as a data scientist, and he does NOT want to be a data poser.
Get him started by browsing everything on kdnuggets.com. If he has enough drive, he can figure it out from there. I’d also look into general assembly.
My trusted editor inserted the picture in the article, and I’ve already received emails inquiring what is wrong with the poser in it. I don’t generally admit that I am a Trekkie in public, but I happen to know the answer, so allow me to share it with readers of this fine publication. Basically her right Thumb should be separated and she shouldn’t tilt her head like that (Star Fleet Academy is no cheer leading squad). There is no communicator or badge of rank on her science officer’s uniform, either. In the Vulcan greeting ritual, it is also customary to say “Live long and prosper” along with that famous hand gesture. So, “Live long and prosper” to you (to which you respond “Peace and long life”.)
You hang out with too many marketing people who have adopted these titles from those who actually do them. I’ve been to plenty of conferences and events where it’s obvious that they know what they’re talking about. I’ve made hundreds of contacts, from data scientists, analysts, DBAs, data engineers, data architects, MDM/data governance experts, hadoop/spark developers, etc. But never really found one that was in marketing, and nor have I met marketing people who actually have the background that the others mentioned do. It’s still two very different professions that have yet to be properly merged, but it’s difficult because marketers just lack the technical mindset.
Great article Stephen, you are definitely not a poser or a complicater, and it is always a pleasure to work with you. I agree that fancy toolsets only help solve the problems that people use their head to understand, define and decide to tackle. Take care my friend.
Thanks, John. I still remember some whiteboarding sessions that we ran together. Problem statement first, then data and analytics, not the other way around! Cheers!
Great, great article Stephen –and who else would have written it 🙂
“Caveat Emptor” –I’ll be sending your article to more than a few friends and Clients who’ve been victims of said posers.
Thanks, Mike! Haven’t we encountered a few together? 😉