Key attributes for analytics professionals

In a recent column, Nicholas Kristof of the New York Times decries the growing isolation of college and university faculty members [1]. Notably, he quotes Will McCants, a Middle East specialist at the Brookings Institution, as saying “Many academics frown on public pontificating as a frivolous distraction from real research.”

Well, I have a long track record of public pontificating, and that I’m a big fan of both real research and frivolous distraction. Indeed, this column has now been in every issue of this magazine for the last four years. In addition, I will be speaking at the upcoming Predictive Analytics World 2014 Conference, which will be held on March 17-18 at the Marriott Marquis Hotel here in San Francisco [2] (and I’d love to see you there!).

This public pontificating is particularly satisfying when people respond to your ramblings (hint, hint). Last month’s column was about a few odd interactions with some technically oriented colleagues about what “real” analytics actually was. In response, I received a very thoughtful response from Fredrick Odegaard, a former supply chain analyst and consultant who is now on the faculty at the Ivey School of Business. Fred first proposed his own definition of analytics (“combining sources of information to create valuable insight that is not readily apparent from the data alone”) and then added, “for me, ‘descriptive statistics’ is NOT analytics. Yet looking objectively at all the advertisement and public manifestations of ‘analytics’ it is 99.9 percent descriptive stats with either (A) fancy charts or (B) tables with a gazillion descriptive statistics.”

Later in his e-mail, he made another very interesting observation: “The hardest part about analytics is not, as most people think, the math. In fact, the math might actually be the easiest part. Analytics require a lot of thinking and a lot of creativity, ingredients that require time and persistence, both of which are in short supply in today’s world. Most managers (and definitely students) cannot and do not want to spend more than a few minutes (or is it a few seconds?!) before receiving gratification. Which means that too often they will take a pie chart or a summary table and rave about their analytics!”

I often hear this kind of thing from analytics managers. Patience, persistence and the ability to function effectively even under a wide variety of pressures (including a shortage of time) just might be the most important attributes for successful analytics professionals. Given some foundational programming and mathematical capability, the knowledge of a particular coding language or a specific statistical technique can be acquired more quickly and more cheaply than ever before; however, there are as yet no effective massively open online courses for the business effectiveness skills (including problem framing, relationship management, effective communication with non- and less-technical stakeholders) that often determine how big an impact is made.

But don’t get me wrong; I’m not trying to minimize the importance of what some of my colleagues call “technical chops.” The complexity of both the data sources being integrated and the business problems being addressed under the banner of analytics is continuing to grow, and the breadth of capabilities needed to implement effective solutions is often a very real challenge. With most of my MBA students, I feel like there is a clear ceiling on how much of the “solution stack” they will ever truly be able to understand, and I am frankly unclear on what career limitations they may face as a result.

On this note, a company recently contacted me because the number of data scientists on staff had grown substantially since we had last spoke and these people had been identified as key corporate assets to be developed and retained. As part of this initiative, a few analytics leaders within the organization had sketched out competencies and job titles for two distinct career paths – one that led to senior analytics management roles and the other culminating in a highly esteemed (and very well compensated) senior data scientist title.

When asked for my feedback, I had two immediate responses. First of all, the very existence of such imperfect but constructive proposals for data science careers was itself a huge, positive signal. Too often, business organizations view analytics people as high-priced commodities to be acquired when clearly needed and discharged casually when not. Knowing this, the skilled professional is compelled to make sure that their own financial and intellectual needs are taken care of, even when that means leaving the company for better opportunities (and there are typically many opportunities available to skilled data scientists). In such cases, a data scientist ends up leaving a relatively good situation largely in order to feel appreciated, while the company finds that a unique collection of broad analytics skills and hard-earned domain knowledge has just walked out the door.

Secondly, I was struck by just how many different technical competencies their proposed plan required, even for people who wanted to pursue managerial and leadership roles in data science. When we discussed this, they were adamant about the need for this broad and deep set of capabilities, both in order to be skilled in creating and assessing sources and to be credible within the data scientist community.

Not long after this discussion, a former MBA student of mine came to visit me. “Richie” had taken several courses with me and had landed an interesting job as data analyst for a large global organization. After a year and a half on the job, he turned down a good opportunity to move into a line management position. Instead, Richie had decided to go back to graduate school again, this time to get a master’s degree in analytics. “My company doesn’t know how much it is leaving on the table,” he told me, “but I do. I just need more technical capabilities to be a real hero in this kind of environment.” His five-year goal, however, was a senior analytic leadership role, and both he and I were confident that he would get there, because of the broad background from his MBA and also because of his strong commitment to learning and growing on all fronts.

When someone with that sort of attitude gets enough technical chops, look out!

OK, I’m done pontificating for now. More next time.

Notes and References

1. Kristof, Nicholas D., “Professors, We Need You,” New York Times, Feb. 15, 2014.
2. See http://www.predictiveanalyticsworld.com/sanfrancisco/2014/agenda_overview.php for the complete agenda for PAW 2014 SF.