Data is like the stroke of a paintbrush, shaped by the artist, blended with other strokes, yielding an ensemble worth more than its component parts. Art is all about interpretation; so, too, is data. And data analytics, like art, depends on visualization, storytelling, composition, and communication to reveal its worth.
So it should come as no surprise that an art gallery was the starting point for one data executive’s career path. That was the case for Siegel + Gale’s Brian Rafferty, who began his path toward data and insights with a degree in visual arts.
VML’s Mark Donatelli also studied art and excelled in football before taking on a role in military intelligence. Rosetta’s Ed Falconer was inspired by the world of advertising, his father’s profession. And Wire Stone’s Jon Baker was also inspired by his father—an electrical engineer and physics savant—because of his ability to “take things that were very complicated and explain them in ways people could understand.”
Each followed a unique path to becoming a leader in the world of data insights. And all share some core traits that are critical in the field.
In this third of my three-part series on Data Analytics in 2017, I talked with my panel to better understand what kinds of qualities hiring managers should look for when hiring for a data analytics role.
Despite their differences, certain foundational skills showed up again and again when I spoke with the experts on my panel: mathematics, physics, engineering (mechanical or electrical), statistics, computer programming. Each had a natural aptitude for numbers, for logic, and for systems thinking. Without this baseline, it may be hard to understand basic concepts, formulas, and demands of a data analytics role.
Hard skills wrought through academic study are only part of the equation. Jon notes some of the soft skills that help make data leaders stand apart: love of learning, eagerness to adopt new tools, and problem-solving tenacity. “What I find is it’s a pretty wide range of people who can find themselves in this field. Not just problem solvers but people who like to learn how to use new tools. You’re not necessarily writing software, you’re running tools, you’re learning how to operate things. I think that can be quite applicable to a lot of people who have the tenacity to understand new things.”
Mark highlights another critical soft skill: curiosity.
“For me, it’s a combination of things—they have to be curious, highly intelligent, a complex problem solver, they have to be interested in the ‘why?’ not just the ‘how?’”
James Barbee, an engineer who is self-taught in analytics, points out the need to be resourceful: “Sometimes you have to improvise and be a bit of a MacGyver.” He says it’s a trait nurtured from a young age when learning to navigate outdoors meant learning “how to use what’s at your disposal and to be prepared.”
Resourcefulness in the world of data analytics is especially key, not only because it’s an area that demands constantly adopting new tools and technologies, but, more importantly, because as Jon has seen first-hand: “There just aren’t enough candidates with experience.”
Hiring managers are indeed facing a talent shortage. In fact, a 2013 McKinsey report predicted an impending experience crisis: “…by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and [a shortage of] 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.” David Hardtke, author of the blog “How to Hire a Data Scientist,” clarifies, “There are little to no data scientists with 5 years’ experience, because the job simply did not exist.”
The result is that many data analysts are self-taught or learning on the job.
And that’s a good thing. IBM Big Data “evangelist” James Kobielus makes a compelling case for the auto-didact. “Academic credentials are important but not necessary for high-quality data science. The core aptitudes—curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature—that distinguish the best data scientists are widely distributed throughout the population. We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available…I think [it’s an understatement] the extent to which autodidacts—the self-taught, uncredentialed, data-passionate people—will come to play a significant role in many organizations’ data science initiatives.”
Thanks to what Kobielus refers to as the “democratization of data science,” anyone interested in a career in analytics has a wealth of free resources to turn to, like the Open Source Data Science Master’s resource hub, sites like Coursera, Khan Academy, Stack Overflow, and GitHub, and free online classes offered by top universities like MIT via EdX.org.
Ultimately, success is never about skill set alone. Whether an experienced data scientist, or a data enthusiast considering a career shift, analytics should not be seen as an end in itself, but rather a path to action.
As Ed explains, “Success in this field isn’t so much about churning the data; it’s about asking ‘so what do I do with that outcome?’ Too many analysts focus on the process of building the analytical outcome. What they don’t focus on, and it’s such an easy thing to say but such a difficult thing to do, is focus on generating the insights. You’ve built the predictive model but what does it reveal about the customer? If that’s the insight, what are we going to do about it?”
For hiring managers discouraged by the short supply and for those seeking jobs in data analytics, the question to ask is “what am I going to do about it?” In both cases, the advice is the same: Be insatiably curious, leverage new tools for greater insight, adapt to circumstances by embracing the non-traditional, and tenaciously ask the tough questions.