In a world where infographics present everything from the world's biggest data breaches to the history of music media to beer varietals, its clear that data visualization has become kind of a big deal.
But that's not what I want to talk about.
How about an interactive map presenting the age of every building in The Netherlands... or a flavor connection graphic for the more culinarily-inclined... what about a language map of New York City based on recent Tweets...
Nope.
What about Chaomei Chen, a leading authority on information visualization, and her countless contributions to the field of functional infosthetics (see her paper on the top 10 unsolved problems of information visualization)?
Not even that.
For me, even more interesting than how to present information visually, is how to visualize information mentally.
All too often, analysts are given an intelligence requirement which they attempt to answer by searching for a specific answer or number or outcome, what I call working downward. What they forget to do is to take a more macro approach, to zoom out, if you will, and put the question into a broader context - to orient the intelligence requirement in its problem space.
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| Figure 1: Working downward |
Interesting. How does this work?
For this, I turn to Enrico Fermi...
Fermi was a physicist who worked on the Manhattan Project. He loved to ask his students to try to solve seemingly intractable problems using only thought experiments. For example, Fermi would ask, "What is your best estimate for the number of piano tuners in Chicago?" Don't run to the Bureau of Labor Statistics website... as a matter of fact, don't even use a computer.
These broad questions, known as Fermi Questions, are difficult or tedious to answer by only working downward (quick! pull out your phone book, flip to 'musical instruments' and start counting!), and almost impossible to answer without access to the internet (See Figure 1). But by zooming out, orienting yourself within the problem's scope and starting more broadly, you can actually drill down to a reasonable estimate of the number of piano tuners in Chicago in a matter of minutes. Here's how:
- Start with the approximate population of Chicago, 3 million.
- On average, there are four people in a family, meaning that there are approximately 750,000 families in Chicago (3 million / 4) (See how general we are being?)
- Not every family owns a piano, though. A high estimate could be 1 in 5, which means that there are approximately 150,000 pianos in Chicago that need tuning while a low estimate might be 1 in 50 which would mean there are only 15,000 pianos in Chicago.
- If each piano tuner works on 4 pianos per weekday, the average piano tuner will work on approximately 100-1,000 pianos per year.
- Pianos need to be tuned about once a year, therefore there should be approximately 15 to 150 piano tuners in Chicago.
This rough order of magnitude estimate is actually very useful. First, if that is all we need, then we are done. No money, little time, and just a bit of thought and we have an answer that fits our needs.
Second, it helps us identify potentially wrong answers more easily. Say we really need to know and we send someone out to collect this information and they come back as say 10,000 piano tuners work in Chicago. Our Fermi estimate should cause us to question that number and the methods used to derive it.
Finally, it allows us to know where we can get the biggest bang for the buck in terms of collecting additional information. For example, we can get a more precise estimate of the number of people who live in Chicago but unless we are off by millions, it probably wont make much of a difference. Getting more information on the number of pianos sold and to whom, might, on the other hand, really help our estimate.
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| Figure 2: Working on a sliding scale |
Figure 2 presents the mental image of this problem. Think of this mental image as the order of magnitude scale for any intelligence requirement (or any problem, for that matter). The red line is the starting point, or where the problem is oriented within the problem space. See how much you would miss by only working downward?
The first (and most important) step is visualizing where your problem is in the problem space, thereby determining how much you are able to zoom in or zoom out. This oftentimes helps analysts put the problem into both perspective and context. (If you have an MBA, this technique might seem similar to PESTLE or STEEP - analytic methods designed to analyze the macro environment to put a smaller intelligence requirement into context).
Second, it is important to zoom out and think to yourself what is "above" your problem, what is the next step up, conceptually from your problem? For example, if you are analyzing a specific company, you will want to step back and look at the industry as well. This zooming out exercise is also very useful at helping you spot assumptions you are making about your target. For example, it is very easy to make a number of assumptions about the dictatorship in North Korea. Stepping back and looking at China's interests in the region, however, adds a whole new level of nuance.
Finally, Fermi estimates are most helpful at the beginning of an analytic process, when you don't have lots of information, or when gathering the detailed information is expensive and time consuming. But be careful! Fermi estimates are just that - rough order of magnitude estimates that help you orient yourself and focus your collection and analysis activities. If the situation warrants, more detailed estimates based on additional information may be required.
Independent of the intelligence requirement, the zooming technique is a beneficial way to visualize a problem space, identify information gaps, contextualize information, recognize assumptions and, above all, approximate and approximate quickly, a skill highly relevant to an intel analyst in any field.
