Orthogonal Skills

27 February 2009 by Sanjay Saigal.

While upgrading a portfolio planning model implemented in Microsoft Excel, the need arose to validate a complex simulation involving log-normal distributions. The log-normal probability distribution is obtained by taking the logarithm of exponentiating (UPDATED: 3/1/09) the ubiquitous normal distribution. Its applications are common enough for some researchers to propose that the log-normal deserves at least equal billing, if not center stage.

The need was to back-calculate the mean and variance of the log-normal distribution using certain other known measures. This required non-trivial nonlinear manipulation, an activity that reminded me of the time lapsed since I last engaged in open-ended mathematical discovery. Which brought me to the question: mathematical sophistication is clearly necessary for Analytics, but is it significant? And if not, what skills are required in an Analytics practitioner?

The business pundit Peter Drucker wrote (h/t to J. D. Meier):

When “operations research” first came in, several of the brilliant young practitioners published their prescription for the operations researcher of tomorrow.  They always came out asking for a polymath knowing everything and capable of doing superior and original work in every area of human knowledge.  According to one of these studies, operations researchers need to have advanced knowledge in sixty-two or so major scientific and humanistic disciplines.  If such a person could be found, he would, I am afraid, be totally wasted on studies of inventory levels or on the programming of production schedules.

Though exaggerating for effect, Drucker is asking a good question about feasibility. Few of us can be Feynman! As Analytics moves from being a “nice to have” to a “must have”, the appropriateness of the training of its frontline soldiers becomes increasingly critical. As described elsewhere here, and elsewhere (the issue is elegantly covered at a high level here by Jim Orlin), the INFORMS professional society is stepping to the challenge.

Like many, perhaps most, Analytics professionals I add value through the modeling and implementation of decision-support techniques, not by creating new mathematics. However, my training was almost entirely mathematical. Even now, graduate training tends to over-focus on mathematical technologies. But for professional success, the Analytics practitioner requires a broad set of divergent skills beyond math.

Some skills – facility with optimization and simulation techniques, statistics, certain aspects of software engineering – can be effectively channeled through higher education’s delivery model. But other, equally important, skills cannot. For instance, project management cannot be taught, only learned through the process of doing. (It especially cannot be taught by academics with zero IT project experience.) Critical skills such as business communication, spreadsheet-based analysis and team effectiveness are best left to the in-service setting. (See this online version of the 2007 Interfaces article by Sodhi et al for a list of commonly expected OR skills.)

The academic part of the profession has focused on core technical skills. Is it the role of Analytics professionals to fill in the picture?

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