• Let your hypothesis determine your analysis.
• Get your analytical priorities straight.
• Forget about absolute precision.
• Triangulate around the tough problems.
Let your hypothesis determine your analysis. Once you start to
plan your analyses, you have to balance intuition against data. His-
torically, the McKinsey problem-solving process left no place at
all for intuition, although there are indications that, in the New
Economy, even McKinsey has come to rely on gut instinct when
blazing completely new trails. In contrast, many decision makers
prefer to rely almost exclusively on their intuition, especially when
time is short. As one McKinsey alumnus noted, “People under-
stand that forming a hypothesis means being results oriented: fig-
ure out where you want to go, and determine whether you’re on
the right track. Often, however, they don’t want to take the time to
do the little checkoffs to make sure they have the right solution.”
Although we understand why this is, we believe intuition and data
complement each other. You need at least some of each to have a
solid basis for your decisions.
The key to striking the balance is quality over quantity. In the
words of James G. Whelan at L, G, & E Energy, “Focused analy-
sis is more important than volume, and this stems from good initial
problem framing.” As we stated in Chapter 1, if you have correctly
designed your issue tree, then you should already know what
analyses you need to perform. You should have broken down the
problem into issues and the issues into subissues. At some point—
it may be two levels down the tree or maybe a dozen—the issues
will have resolved themselves into a set of questions that can be
answered yes or no (e.g., Will the product make money? Do we
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