Don’t Hire Data Posers

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.

data poserThere 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.