An analytics professor/practitioner looks at 50

By the time that you read this, I will be 50 years old. The Big Five Oh. Indeed, I am now officially eligible to join the American Association of Retired Persons.

The thing is, I’m not retired. Not even close. In fact, between my day job as a business school analytics teacher, an odd collection of research projects and various other analytics-related commitments, these days I feel like I’m working on a wider variety of projects than ever!

In fact, in approaching this milestone birthday, I have had a chance to reflect on a variety of matters personal and professional. Here, in no particular order, are some of my thoughts upon arriving at the half-century mark.

Update on analytics survey. In early 2013, I asked readers of this column to complete an online survey to support a research project that Accenture’s Jeanne Harris and I were doing. Many of you graciously took the time to respond affirmatively to this request, and thus ended up taking our rather lengthy survey. Survey respondents were asked to answer questions about a wide variety of topics including job titles, educational background, organizational structure and company culture, and the types of data, software and mathematical tools utilized in their work. Other volunteers participated in a series of focus groups with us to provide us with additional, more detailed input for our research.

Well, given that we were studying the world of people who work with large amounts of data, it seems somehow appropriate that we found ourselves with a whole lot of data to analyze. But over the past year or so, we managed to test our long list of research hypotheses and come up with some interesting findings. One discovery was that there were in fact many significant differences between those with the job title “data scientist” and those with more traditional job titles such as business analyst, statistician, industrial engineer and six-sigma black belt. (We also discovered that there were a startling number of job titles and organizational structures that our data-centric survey respondents fell under.)

While some of the findings were not surprising (e.g., “data scientists tend to work with larger data sets integrated across more sources”), there were some interesting insights that emerged (e.g., “data scientists are far more likely to use prototypes to garner support for their projects” and “data scientists are much more likely to be focused on helping their organizations develop a unified view of their customers”). Anyone interested in seeing a summary of these findings should feel free to contact me via e-mail ([email protected]).

As part of this project, we also examined best practices for managing data scientists. Our findings in this area are presented in a paper entitled “Getting Value from Your Data Scientists” that was recently published in the MIT Sloan Management Review. The paper can be accessed online. Feel free to send me an e-mail with your thoughts, reactions and comments.

San Francisco Giants. As I write this, my beloved San Francisco Giants are playing in the World Series, trying for their third championship in the last five years. Like the rest of the orange-clad Giants faithful, I am ecstatic at this year’s post-season success, but I must confess to also being a bit surprised, for this year’s team won only 88 of 162 games (the lowest of any team that qualified for this year’s post-season). Moreover, these Giants finished a distant six games behind Los Angeles Dodgers, their perennial rivals who once again captured the National League Western Division championship. Worse yet, the Giants struggled down the stretch, winning just six of their final 15 games and barely qualifying for the playoffs.

Yet the Giants have thrived once again in the post-season and are, at the time of this writing, just three games away from winning the 2014 World Series. Though I am surprised, Jonah Keri and Neil Paine are not. Their recent article [2] on fivethirtyeight.com states that after analyzing a great deal of historical data, they find that a team’s late season winning percentage is not a significant predictor of post-season success. The Major League Baseball playoffs, it seems, are (at least statistically) a whole new season.

Let’s go Giants!

Learning to translate. I have been a university faculty member for the past 11 years. Prior to that, I spent 11 years in industry after finishing graduate school. On the occasion of my 50th birthday, I find that symmetry to be both amusingly coincidental and oddly appropriate, as I feel as though I’ve been straddling the line between industry and academia for all of my adult life. Since becoming a professor, I have continued to work with start-up companies in a variety of roles.

When considering whether or not to get involved with a company, I typically ask myself three questions:

• Does this company have a reasonably high probability of getting funded, growing and/or ultimately becoming successful?
  
  
• Can I add value to this company by helping them with the technical problems and/or business problems that it is likely to face?
  
  
• Will working with this company give me a chance to learn something valuable that I can share with my students and colleagues?

I recently agreed to serve as an advisor to an exciting new start-up in Silicon Valley. My primary responsibilities are to serve as a sounding board for their lone data scientist and to provide a bridge between this data scientist and the company’s executive team. This role in some form or another is an increasingly common one. As Anil Kaul, CEO of AbsolutData, observed during one of our research focus groups, “We are starting to see a significant increase in the demand for high-level ‘translators’ within data science project teams.”

Somehow it feels like I’ve been preparing for this role all my life.

REFERENCES

1. http://www.youtube.com/watch?v=bnZdlhUDEJo
2. http://fivethirtyeight.com/datalab/the-as-tailspin-might-not-matter-once-the-playoffs-start/