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Big Data Needs More 'Creative Types'

January 18, 2016   |   5:28 PM
Big Data Analytics
Teradata Articles

I’ve argued before how the field of data science should be populated with artist-explorers and creative people — “data artists” — who can navigate content and find something others don’t see.  I’ll go into more detail here about what I mean by “data artist,” so you know what to look for to solve your next big, big data problem.

Data artist is a designation to recognize the nuance and precision that goes into the job. The data artist blends engineering and statistical know-how with intuition and novel problem-solving abilities to uncover insights and create value from data.  I’d also recommend you follow my Forbes BrandVoice contributor colleague, Bill Franks. He was among the first to champion the data artistcause.

My own interactions with data artists makes me think of those offbeat questions that can come up during tech company job interviews– word associations, logic questions and other HR confections designed to gauge how a job applicant thinks when dealing with ambiguous or challenging situations. If it wasn’t so well known, the curveball I might recommend is the century old nine dots puzzle that arguably gave rise to the term “thinking outside the box.”  That’s because solving it requires suspending and scrutinizing our assumptions about order and structure, and this ability is among those crucial for people who do analytics in today’s era of big data.

“Data Scientist” is a fine job title for those who navigate terabytes of information in search of patterns and relevance, connecting dots to create value and competitive advantage. As a longtime practitioner in the data warehousing field, I’m gratified to see the importance of this work now validated with a widely recognized job title of its own. But I’m also among those lobbying for an immediate title change – to “Data Artist.”

So, What Does a “Data Artist” Do, Exactly?

I’ve made the argument before how, beyond the technical bona fides, there is a level of artistry needed to explore data in ways that are as creative as they are rigorous. This is true whether you’re talking about data mining, predictive model development and scoring, statistical analysis, optimization, Hadoop programming, visualization or any number of related tasks.

Top quality analytics require creativity in determining how to interpret, position and act upon data; and these judgments demand more than just technical skills.  The tactical know-how must be augmented by a strategic sense that involves intuition, business acumen, excellent communication skills and the courage to try new approaches in search of the game changing insights that create value from data.

Ultimately, big data is too pliable to be governed by rote processes and predetermined assumptions, so the data artist must be as flexible with schema and structure as the data he or she is working with. This means you can’t just be a follower of rules. You see this ingenuity at work in other fields like music and art, and it’s actually not such a bad idea to poke around for those types of activities lurking on the back pages of your job candidate’s resume.

To be sure, you need solid engineering skills and a commitment to extracting business advantage from data, but there is also a nuance not unlike what sports psychologists call the “intangibles” among top athletes. People may have similar training, but there are additional layers of creativity and improvisation that go beyond just connecting dots.  The data artist draws new connections that may never have been imagined before.  And, those connections can be leveraged for value and competitive advantage in your business.