#91 from R&D
Innovator Volume 3, Number 4
Commonsense Rules for Creative Analysis
Brinkerhoff is a writer living in Burke, Virginia, who specializes
in promoting commonsense solutions to everyday problems.
He is the auther of 101
Commonsense Rules for the Office, 1991, and 101
Commonsense Rules for Making Things Happen, 1993, both
published by Stackpole Books, Harrisburg, PA.
something new isn't easy, and for that reason the ability to
create new things is highly prized.
The nature of creativity is itself something of a mystery.
Analysis isn't inherently creative, since it's the opposite
of design. Design is
inherently a creative process that requires decisions to be made
at each step. Analysis
is intended to tell you what is
going on rather than what can be done or even ought to be done,
and the analytical process per se doesn't necessitate decisions or choices.
Let me illustrate
the difference between design and analysis using an example from
civil engineering. Analysis
of a highway bridge occupied an entire semester of my civil
engineering education. The
class was assigned to rate the capacity of an existing bridge, and
went through each truss, beam, and pin to determine which critical
member established the capacity of the bridge.
The analytical process was tough but straightforward, and
did not require making choices about construction.
Later, while I
learned to design (create) a bridge, the entire process was about
designers, we had to choose the location of the abutments, the
kind of bridge, the size and shape of the members, the placement
of the lateral bracing, and so forth.
Each choice was bounded by the usually competing demands of
aesthetics, cost, and safety. And each choice, once made, foreclosed some options.
Ultimately, our creation was the aggregate of all of those
So, design is
creative, while analysis is not.
But analysis can be a useful part of the creative process
if it provides insights that lead to better choices.
By way of contributing to creativity, I offer four
commonsense rules for using analysis to assist the creative
One: Question the Question
Let's start by
questioning the question, to see if the question you're seeking to
answer is the correct one. Sometimes
the problem to be analyzed is obvious, but often the problem is
selected by an executive who unwittingly poses it in a way that
prejudices the answer. One
of the first things a creative analyst should do is examine the
data to see what the real problem is.
A classic example
of this kind of creative analysis was work on bomber basing done
by the RAND Corporation for the Air Force in the early 1950's.
The Air Force wanted RAND to determine how to build and
maintain air bases overseas at minimum cost.
But the RAND analysts decided after much thought that this
relatively simple logistics problem wasn't the real question.
The real problem was "not one of the logistics of
foreign air bases, but the much broader one of where and how to
base the nation's strategic air forces and how to operate them in
conjunction with the base system chosen."
The resulting broader study led to a decision to base more
of the bombers at U.S. bases. Questioning
the question allowed the analysts to restate the problem and
achieve a solution that refuted the conventional wisdom—but was
adopted by the Air Force with good results and considerable
My advice is to
look at your problem at the outset and assure that you've stated
the problem correctly for your needs.
Two: Rummage Through the Data
often occurs by rummaging through a mass of data more or less
haphazardly just to see what comes to light.
Data has always
been a problem for analysts, and getting data occupies most of the
time and effort for any analytical effort--much more than working
on the data after it's assembled.
This means that most projects are data-limited, and the
data are biased to provide specific results--whether or not this
is a conscious act by the analysts. In the past, most data have been ad hoc in the sense that they were compiled for a specific project.
large amounts of data are compiled in computers, or are
by-products of other processes, residuals from analytical
projects, or sometimes even created for their own sake.
The process of
rummaging through data consists of looking at it with no
viewpoint, no problem to be solved, and no preconceived solution. The goal is to detect patterns, trends, and numerical
relationships that reflect natural phenomena.
This kind of analysis will sometimes reveal truths that are
contrary to the conventional wisdom, truths that are often not
One good example
of this process is the work of Johannes Kepler, an early
seventeenth-century German mathematician.
Using observations of planetary motion made by others,
Kepler spent years calculating the orbit of Mars.
While the data indicated that the orbit was an oval, Kepler
noticed a pattern that generalized into his first law:
The radius vector (the line from a focus to a point on the
orbit) sweeps equal areas in equal intervals of time.
This motion is true only for an ellipse, and so Kepler was
the first to notice that the planets traveled in elliptical orbits
about the sun. This
discovery provided the basis for Newton's later work on mechanics
and opened the door to modern astronomy and physics.
By rummaging through data, Kepler used analysis to create a
while Kepler was founding the science of astronomy, he earned a
living by casting horoscopes.)
My advice is to
use the available data--carefully, systematically, slowly--and see
what results. You
might be surprised.
Three: Note the
mostly boring, particularly now that computers allow us to crunch
so many numbers. Before
computers, a few analysts spent an eternity doing manual
calculations. Many analysts in that pre-computer period resorted to
intuitive generalizations because they had no choice.
The ability to
perform many repetitions has virtue because it allows alert
observers to note the exceptions.
One good example of this was an occasion when the computer
told us something we refused to believe.
I was part of an
analysis shop for the Army running a combat simulation called
theater-level model simulated war between NATO and the Warsaw Pact
in Central Europe; it was organized into 10 corps-sized sectors
running from Denmark to the Swiss border.
Force ratios were calculated in each of these sectors, and
they determined the rate of advance and the casualties for each
side. One of NATO's
advantages was reinforcements from the U.S., and the computer
allocated these forces by a rule that placed them where they were
needed most--in areas where the force ratios were the worst for
NATO. We kept running
the model, and usually the reinforcements were sent to the
"wrong place." There
were complaints that the model was wrong, but it was just
following orders and providing us a useful insight--if we had the
wit to see it.
understood that the model was telling us that our plans to
reinforce in the south were wrong, and that we needed instead to
reinforce the north. Once
this vital insight was accepted, the plan was changed to have some
of the newly arriving forces form a third U.S. corps on the north
German plain, and in the computer, at least, NATO started winning
My advice is to
look for the inexplicable, the dumb answers, the discontinuities,
the blatantly wrong solutions--and take them seriously.
Four: Clarify the Presentation
A lot of
creativity lies in finding things that aren't readily apparent but
that are obvious once discovered.
Much of this has to do with presentation--it isn't enough
to compile or analyze data to discover new insights; we must also
communicate the results.
In my opinion,
the best aid to communication is clarifying the presentation.
Done properly, this will allow even the most complicated
concepts to be understood by outsiders to a discipline.
There is a genuine danger that the truth may be distorted
in clarification, but presenting so much data that it's
incomprehensible doesn't serve truth either.
The trick is to design your presentations to provide the
essence of the truth while doing no damage to the details.
I was able to
clarify the true meaning of some time-series data by deftly
manipulating the range of dates to make my point.
The subject was strategic warning for World War II, and my
hypothesis was that although the United States was tactically
surprised by the Japanese attack on Pearl Harbor, it wasn't
strategically surprised and in fact was well prepared for war.
To demonstrate the extent of mobilization, I prepared
charts with data from 1933 to 1946 showing the numbers of military
personnel, combat aircraft, navy ships, and percent of gross
national product spent for defense.
The results were disappointing because the gigantic efforts
made during the war itself--from 1942 to 1945 --simply overwhelmed
the data for the pre-war period.
On these charts the pre-war data showed no growth at all.
In a fit of
desperation, I made charts including only 1933 through 1941.
These charts clearly showed that the preparation for World
War II started as early as 1933 for some areas, and more
generally, in 1939. While
the pre-war preparations were small relative to the war-time
mobilization, they were substantial compared to the pre-1933
period. This creative
mode of presentation was important to show what really happened.
Try to understand
what exactly you are trying to demonstrate and--without distorting
the facts--use creative means to do so.
Analysis may not
be creative per se, but
it helps to apply creativity to the analytical process. Properly used, analysis can lead to motivation.