Data Scientists Should Think More Like Artists (It’s Sexier)
Working in data is the sexiest job of the century. Apparently.
You wouldn’t expect to see “sexiest” and “data” often written in the same sentence, but over the last six months, I’ve being reading it more and more. As the hype around data increases, so does that sentiment.
How did analytics geeks end up with the sexiest job of the entire century? Two of my esteemed colleagues, analytics expert Thomas H. Davenport and data scientist DJ Patil, have a little something to do with it. In 2012, they wrote an article in the Harvard Business Review that started this whole data/sex appeal trend. The article identifies data scientists as “new key players” in organizations, with “the training and curiosity to make exciting discoveries in the world of big data.”
Davenport and Patil say that if you store multiple petabytes of data, if the information most critical to your business resides in forms other than rows and columns of numbers, or if answering your biggest question would involve a “mashup” of several different analytical efforts, you’ve got a big data opportunity ripe for cracking by data scientists.
The term “data scientist” was coined in 2008, but a small pool of experts had been quietly working in and around data in various fields for some time. Now, you’ll find thousands working at both start-ups and well-established companies wrestling with information that comes in varieties and volumes they’ve never encountered before.
Sure, there is a growing need for taming complex data analytics, but does that make it sexy? The famous French artist Edgar Degas once said, “Art is not what you see, but what you make others see.”
Currently, many organizations still see data analysis as more science than art. Data scientists are hired to interpret row upon row of spreadsheets to ferret out small gains with cold, logical Spock-like precision. And they seem to be on a never-ending quest to cram all of their findings into elaborate charts and dashboards that leave most recipients on the email chain scratching their heads in confusion.
This strictly science-based approach leaves out the important role of creative storytelling — the kind of storytelling usually ascribed to artists. But to truly harness the power of big data, we’ve got to change the way we try to make others see.
I would argue that the most useful data scientists are really more like data artists. A data artist doesn’t just see data as a series of numbers or equations that need to be organized in the proverbial spreadsheet. Instead, they see the compelling stories to be told around that data and give considerable consideration to their audience when they tell those stories.
Some are already leading the way in the development of story-based data reporting tools. One company in particular, Narrative Science, is a data analysis firm out of Chicago using complex algorithms that analyze large data sets and turn them into prose. The Narrative Science team responsible for creating their algorithms is partially comprised of experts in journalism; so, they understand what it takes to tell a compelling story — replacing confusing charts and dashboards with interesting and digestible narrative.
What kinds of organizations benefit from a story-telling approach like Narrative Science’s?
Well the CIA for one. They have large amounts of complex data that must be processed hourly. Which is why, through its venture capital arm, In-Q-Tel, the CIA has invested in Narrative Science. No doubt, they see value in being able to traverse large data sets to provide simple, digestible reports fast.
Data scientists will continue to build complicated dashboards that will be understood by only a select few. But data artists will opt to invoke the three-act story structure to break down those analytics into compelling and actionable narrative. Now, that’s sexy