Those Who Can, Teach

Weighing the opportunity to teach Analytic Decision-Making to executive MBA students, I realized that the value in such a project is essentially internal. The financial compensation, to not put too fine a point on it, was low. But the chance to explore the tools of my profession with highly motivated and skeptical, yet captive, mid-career professionals was too attractive to pass up.

I’m teaching something called a “breadth” course. Not quite a part of the core curriculum, but necessary to fulfill requirements of the specialization, e.g., Technology Management or Business Analytics & Technologies. The catalog describes breadth courses as specially focused on relevance. That led to my first surprise: the course description includes Optimization and Decision Analysis (DA), but no mention of Simulation or Queuing! (Forecasting is covered in another breadth course, and further in an elective. Data Mining and Business Intelligence are covered in an entirely separate data management elective, as if they were not part of the same Analytics continuum.)

The absence of Simulation in the syllabus initially struck me as astonishing. In the ill-structured fuzz of real-world business decision-making, Monte Carlo Simulation is the inescapable Swiss Army knife. However, as I began evaluating alternative texts, I had an epiphany! Textbooks purporting to teach quantitative decision-making to business students are typically adaptations of texts originally designed to teach Operations Research (OR) to engineering and other technical majors. For instance, a widely-used text has an entire chapter dedicated to Minimum Spanning Trees, an artifact I have not had to explain to a non-technical client in seventeen years! Authors also persist in continuing to sacrifice thickets, if not entire forests, explaining theoretical notions such as duality and sensitivity to students who would much rather be off discussing consumer product strategies!

After rapidly filtering through a number of OR-centric treatments, my choice reduced to two. The text I did not choose, though only by a hair, is Data, Models, and Decisions, by Bertsemas and Freund (both at MIT). I was nearly swayed by the authors’ smarts in simplifying very complex material, and a near-perfect pedagogic progression – starting with DA, proceeding through probability and statistics to Simulation and Regression, and finally to Optimization. The exposition is case-oriented and jargon-avoiding. However, its immersive use of mathematics raises the possibility that a treatment designed for the very technical Sloan School students might not work as well for more diverse audiences.

The text I chose is Decision Making with Insight, by Sam Savage. For business school use, this text has three key advantages: First, its DNA is that of practical business decision-making, not OR technology. Its spare style makes it practically useful as a cookbook (after the class is over) in the vein of the Numerical Recipes series. Second, Savage – a thought leader in spreadsheet-based Analytics – almost exclusively uses Excel as his computational sandbox. He reasons that while spreadsheets have easily-reached limitations in analytic use, they are the business analyst’s tool of choice, which guarantees a faster uptake than an unnatural specialized environment like, say, Lindo. Finally, the exposition is playful and exploratory. It elevates insight derived by computational experimentation over traditional mathematical development. The business school classroom requires business engineering, not business mathematics.

The main weakness of Savage’s book relates to its Excel-centrism. The book is designed to turn the reader into a self-motivated desktop analyst. But Savage doesn’t even hint at the vast and valuable world of specialized analytical tools and applications that power large-scale systems such as airline revenue management to power plant operations. Since there are few pointers on the possibilities and limits of Analytics, the book elides the equally important role of the reader as a potential consumer of hardcore Enterprise-level OR technologies. (Bertsimas and Freund do a better job in this respect.) Fortunately, given my background in large-scale system development, supplementing the text should be straightforward. So this lacuna does not concern me.

Tune in again after Q1 of 2009 for a postmortem on my choice!

This entry was posted on 2 January 2009 at 19:11 and is filed under Teaching, Business Intelligence, Operations Research. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response or trackback from your own site.