Bill Franks has a love-hate relationship with the term "data science."
"It's way over-hyped," said Franks, author of the recently released Taming the Big Data Title Wave and chief analytics officer for the data warehouse appliance vendor Teradata. "If you look at what people described as
While they may be using a new set of tools on new types of data, such as social, an analytic professional's job description hasn't changed all that much: thought process, analytic goals, deriving value for the business, Franks said, it's all the same thing.
But he also embraces the new title as a label for the analytics professional he tends to seek out: Someone who has an analytical technical background as well as commitment, creativity, intuition, business savvy and presentation skills -- what Franks refers to as "softer skills."
SearchBusinessAnalytics.com recently sat down with Franks to discuss why these non-traditional skills are necessary, how businesses can assess a candidate's "softer skills" during the interview process, and why he's begun calling for data artists rather than data scientists.
You recently started using the term "data artist." Why?
Bill Franks: I don't expect people to literally adopt that term as opposed to data scientist. It's more of me trying to provoke thought about what really makes a good data scientist. An analytic professional could be a data scientist, data modeler or data miner. And the conclusion I've come to is that the technical skills required for the job are important, but they're not what differentiates super successful analytical people from the run-of-the mill or the not-so-successful. Some of the traits are things that are often not specifically associated with hardcore analytics people.
Be more specific: How does something like creativity impact the analytics process?
Franks: As much as you'd like to follow the book and go by what the formulas say, the data is never as complete as you'd like, the data is never as clean as you'd like, and the problem is never as well-defined as it would be in a textbook. The really good analytics professionals and data scientists are those that are able to understand the business problem; they're able to apply creativity and present the results well.
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How does an analytics professional pick up these "softer skills?"
Franks: It's sort of like athleticism. There are people who are athletic and those who are not. And those who are inherently athletic, you can put them on a basketball court or a soccer field, and they'll probably do well at any of them. … You could take someone who just isn't athletic, train them all you want and they probably will never be that good. That's the key. A data artist is someone who has the technical skills and acumen required, but they also have intuition, which is hard to teach. You can help people leverage it, and understand the best ways to use it, but I don't know of a way to teach intuition.
For businesses looking to fill a position of a data scientist, how can they assess whether a candidate has these "softer skills?"
Franks: I can run down the five core areas I look at. Commitment: I'll listen to how they are describing the work they've done. I try to hear insights into how they deal with problems. Do they attack or gloss over the problems? Are they going the extra mile when they have the opportunity?
Creativity: This is a big filter for me. I like to say maybe 15% to 20% of the people whose resumes pass my experience and skills test will pass creativity. I ask about those "oh gosh" moments in their careers [where] something horrible went wrong or they hit a huge barrier they might not have anticipated. And I listen to how they worked around that.
What do you listen for, exactly?
Franks: Someone with a lot of creativity is going to tell me a story. They're going to tell me what they did, why that mattered, [and] the thought process they went through deciding what to do. Someone who's not creative is going to give me a list of the steps they went through one-by-one more from a technical perspective.
So commitment, creativity -- what's next on the list?
Franks: Business savvy: I ask why they made the decisions they did. Particularly for an analytics process, this is important because what I want to hear is not just some technical reasons. I also like to hear practical and business considerations. … I've also seen examples wherein people will focus on information beyond what's necessary. … They give all of these technical details when all the business person really needs to know is, statistically speaking, it's a huge win, so go for it. And if they really want the details, I can provide them. That's part of the business savvy: Knowing how much information to give to the non-technical people to help them make their decision without getting them inundated in the details they may not want to understand or worry about.
Presentation is an easy one. First, I'll look to see if they've written any articles, blogs or white papers. (I'll ask them to provide those.) I'll also ask if they can provide me a PowerPoint deliverable they did. … And I'll ask them to do a specific presentation as part of the interview process. Sometimes, I'll ask them to present a project they worked on. Sometimes I'll ask them to present on their background and why they're qualified for the job. And sometimes I'll just leave it up to them.
Interesting. What do you get out of the presentation portion of the interview?
Franks: I get to see them in action: Are they able to present; do they have confidence; do they look people in the eye; how [do] they handle hard questions. I'll throw in some random questions, which clients tend to do sometimes, that are completely off topic just to see how they react. I can also get some good insight into their creativity, again, through that process because I get to see how they take my somewhat vague guidance and make themselves stand out from the rest of the people we are interviewing.
You mentioned five core areas. What's the fifth?
Franks: The last one is around intuition: This ties to the artistic side and it's hard for me to describe specific criteria. I will ask about their background, if they have one, in things like art or music or some other type of creative area. … [W]hat I have found consistently, and I've heard this is true in other technical fields like physics, is that a lot of the people who are good at applied sciences also tend to have had a pretty strong background in art or music.