Why You’re a Data Scientist…But You Just May Not Know It Yet
TL:DR; Marketers of the future will need the skills of data scientist. Fortunately, technology will operationalize much of the heavy lifting, putting the onus on the individual to think differently.
When I first started developing the thesis that later would become the CMO Primer for the Blockchain World, one of the key questions I asked was:
What NEW skills will a marketer of the future need that today s/he doesn’t need?
The book listed some hard and soft skills such as curiosity and understanding of behavioral economics.
A recent article in HBR The Democratization of Data Science offers an additional viewpoint as to the growing need for marketers (and really any occupation) to understand how to think like a data scientist.
There was one quote that really stood out.
Jonathan Cornelissen writes that the noteion that “data science” is the domain of a few key experts is…
“a mistake — and in the long run, it’s unsustainable.
Think of it this way: Very few companies expect only professional writers to know how to write. So why ask only professional data scientists to understand and analyze data, at least at a basic level?”
Understanding and applying data science is going to be table stakes in the future.
As a father of 3, I think about this a lot. How do I get my kids ready for this future?
Great data scientists will tell you that it’s not about the tech or even the data, it’s about the ability, really the curiosity, required to ask great questions.
What’s more, we’re entering a world where, eventually, people will get paid for their data (as the Economist recently explored).
But hey, I am already living in that world. I earned 500 KIN and redeemed it for a $5 Amazon gift card by answering survey questions on the Kinit app (android only at the moment) and Presearch pays me for search intention data.
We’ve talked about how the new “pay for data” and open systems will impact the competitive environment for firms. Admittedly, it will take some time with the dominance of the FANGs.
That’s step 1 though.
The second step is the democratization of data science so that anyone can access it from anywhere.
Once upon a time, a deep reference search required a library and a librarian. Today, we use Google.
Today, we need million-dollar data scientists and months to get an insight.
Tomorrow, we will have “Google for Predictive Analytics” (as Endor has been called) and others that make this immense power available at our fingerprints.
The question for each of us is: will we know the questions to ask?