Skills needed for competitive advantage in analytics (hint, it's not just the math)

I'm a big believer in both/and thinking, so I'll stand squarely in the middle and say that the most important skills for competitive advantage in analytics include a combination of top-notch modeling abilities along with business acumen, critical thinking, and curiosity. I was intrigued by a blog post on this topic from AllAnalytics.com, where editor Beth Schulz began with the provocative title "Quants Not so Necessary for Predictive Modeling?" She takes her cue from a recent TDWI best practices report on "Predictive Analytics for Competitive Advantage," where among other things they shared the results of a survey on the skills necessary to perform predictive analytics. More than two thirds of respondents agreed that knowledge of the business, critical thinking, and understanding of the data are essential. Agreement then begins to diminish, with 41% citing training in predictive analytics but only 34% agreeing on a degree in this field.

Greta Roberts of Talent Analytics has her own angle on this question, based on a study they did of analytics professionals themselves to profile their traits. Beyond the obvious findings their research points to curiosity and creativity, as well as discipline, as top attributes to look for when hiring this kind of talent. But when I am in conversations with our customers I hear varying opinions on this question. Some say these skills don't all exist in one person, which is why they have teams. Others believe that the key is training the quants in the "soft skills."

A solid foundation in the fundamentals of a quantitative discipline provides an excellent start for a career in analytics, and those who come into analytics from other paths will find themselves going back to learn things they hadn't studied in school. It can be a tough slog to try to catch up on linear algebra at night. But the "math" alone won't solve a business problem. It is essential to understand the business context around a problem to formulate its solution. And then this proposed solution must typically be explained to a group of stakeholders that include at least some people without deep analytical training. And implementing most solutions involve collaborating across different business units with diverse background and training. So a much wider set of skills are necessary to go from problem to solution.

To highlight this challenge SAS teamed up with the Analytics Section of INFORMS for the Student Analytical Scholar Competition, which requires applicants to read a case study and submit a Statement of Work explaining how they would address the business problem in the case study. Naturally this requires analytical skills, but those skills alone don't lead to the best applications. In this interview, 2013 winner Alex Akulov talks about his perspective after finishing his bachelor's degree in math (with a minor on optimization). He assumed that when presented with a problem he would formulate it and then be done. "It's an optimal solution, so you present it to the manager and of course, he's going to say, "Yeah, let's do it because it's optimal." But how often does that ever happen?

Communication skills are essential as well, which is why this competition offers students a chance to ask questions as if they were consultants on the job interacting with the "customers." In this discussion forum (open until February 14 at 5:00 pm EST), they can query the individuals involved in the case study who will respond as they see fit. That will give applicants more information to incorporate into their submission, which is due by midnight on February 17. The winner will have their expenses paid to attend the INFORMS Conference on Business Analytics and Operations Researchin Boston March 30-April 1, where they will have a fantastic opportunity to attend sessions given by analytics practitioners and networks amongst them. Alex cited that experience as a great benefit of winning.

LinkedIn discussions are full of students asking what it takes to succeed in analytics. How would you advise them - what do you look for when hiring analytics teams?