In that column I proposed that, rather than treat this course in an isolationist way, as an autonomous homeland within which we conduct our business freely but out of the mainstream of the program, we should press outward and assert our centrality. We operate "at the hub, not the rim, of the intellectual and professional development wheel."

Further, I proposed that we create strong, comprehensive courses by creating "a fusion of the essences of what could be, under other circumstances, two or more separate courses." Crafting a clever hybrid of multiple, allied fields can turn the limitations of a one-course model into its key selling point.

The primary conceptual issue in any course is coherence: Is there an ongoing narrative to unite day-to-day investigations into a logical framework? We must avoid the "jackalope" course, a blatant stitching together of unrelated species of thought, immediately recognizable as a fraud. However, a "quantitative methods" thread, woven of technique alone, may tear under the skepticism of an issue- and action-oriented MBA constituency.

There is no universal hybrid, optimal under every condition, but one has served us extremely well at Emory over the 14 years we have been evolving it: Take the key bits of what usually is framed as "management science" (or "quantitative methods"), add the key bits of "business statistics," then radically integrate and reframe as "Decision & Information Analysis" (DIA).

The "jackalope" problem, as well as our goal of asserting the central importance of the course, are both addressed in our framing, starting with the title. DIA is neither about "science" ("statistical" or otherwise) nor a grab bag of "methods." The course is about one thing: making better decisions, using rigorous logic informed by facts.

We reinforce the centrality of DIA by organizing the narrative around the prescriptive question, "What should we do?" This is a universal and compelling question. It addresses the complex, ill-structured, cross-functional problems that all managers and leaders must face. Further, it subsumes the more specialized problem types that students will consider later in their operations, marketing, finance and strategy courses.

Motivating the study of decision-making is easy: simply demonstrate a wide variety of entertaining results from the study of cognition and behavior. Students quickly realize that we are, overall, pretty good decision-makers because we have survived as a species. However, evolution has molded our "human operating system" to solve problems quickly, robustly, with little information ... but only approximately. Worse, our internal rules of thumb lead to common decision traps. It is easy to show that we are not wired to solve the sorts of problems our stakeholders now demand of us, and that we need additional, analytical methods to fortify our natural skills.

We partition the course by decomposing the central question into:
A) "Given what we believe, what should we do?"
B) "Given what we observe, what should we believe?"

Question "A" is about rational action, and it leads to decision analysis under certainty and uncertainty, sensitivity analysis and the value of information. (More specialized parts of the management science "canon," such as optimization, inventory models and Monte Carlo simulation, can integrate nicely here.) Introducing probability — framed as "modeling beliefs about uncertainty" — is more natural here than with its traditional correlate, data analysis.

Our decision models can be driven solely by estimates and judgments, but Question "B" fortifies our beliefs with things we can observe and measure, i.e. quantitative facts. Here we address procedural and analytical data issues — obtaining and organizing it, exploring and summarizing it, and drawing conclusions from it (inferences, estimations from regression or time series, etc.)

This is a very tight fit for one semester, but because all the methodologies clearly spring from the central "What should we do?" question, they integrate compactly and are seen as both relevant and useful.

Students have no idea what to expect in a "DIA" course, unlike those marked "Marketing" or "Finance," and to the extent they have expectations, they're not positive. The key to buy-in is winning the first day.

We open with an original case, a disguised version of the 1986 Challenger disaster. Simplifying for brevity, the discussion generally establishes that smart people in capable organizations can make disastrous mistakes, and that decision-making involves weighing risks, trading off conflicting objectives, wrestling with imperfect information and anticipating future decisions and uncertainties.

Most importantly, students understand the tragedy of the Challenger story: The right people were discussing the right issue at the right time, and with the right data. Nonetheless, proponents of delaying launch were unable to make a persuasive case using principle and experience alone. Indeed, the Challenger's fate was sealed by a chart that failed to connect data with decision issues. Clarity of thinking and communication about "the DIA," not a Ph.D. in modeling or statistics, would have been enough to change history.

There is unanimity from students of every concentration that they are at B-school to become better decision-makers, and to improve both the logic and use of evidence in decision-making. They embrace our premise: "Decisions are a fundamental particle of management." They are on board, ready for the ride.

In the next column, we will conclude with some key content and operational prescriptions in building and operating a hybrid course.



Patrick S. Noonan (patrick_noonan@bus.emory.edu) is an associate professor (clinical) at the Goizueta Business School of Emory University.

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