"What do you do?" It's a question you probably get all the time, like I do. Being a QA engineer at Tableau, my usual quick answer is that "I test data visualization software." Simple enough, right?

Well, I've found that different people hear different things out of a statement like that. For some, visualizing data is the last step -- a way of constructing charts of information for documentation or presentation. But for others, visualizing data is the first step -- a way to understand data before analyzing it in detail.

Either way is certainly a valid approach for using Tableau. Personally, I like taking the latter approach, using visualization as a tool for analysis. I think it's because I have a background in the physical sciences, where the idea of using simple visuals to answer complex questions has a long history. After all, if you can come up with the right thought experiment, visualization, or graph, you can get profound results without going through a more costly, time-consuming series of experiments. In the business world, this might be called "efficiency"; in science, it's often called "elegance."

Take physics for instance, the scientific field I'm most familiar with. Physicists throughout history -- notably including historic geniuses like Galileo, Einstein and Feynman -- have taken devious pleasure in deriving profound insights from simple illustrations and reasoning.

This came to mind recently when I came across a great story about Geoffrey Taylor, the great fluid dynamicist. Professor Taylor cracked one of America's most closely guarded secrets -- the yield of the Trinity nuclear test of July 1945 -- using dimensional reasoning and a magazine photograph to get an amazingly close estimate.

data analysis photo of trinity nuclear test

The photo he used was the one above, showing the expanding, nearly-spherical blast wave of hot air following the Trinity explosion. Immediately after the war, pictures like this of the A-bomb's results were considered good for propaganda value, but the yield of atomic weapons was a closely guarded national-security secret. As the sole nuclear power, the US was wary of providing any clues that might help A-bomb development -- or defensive measures -- in other countries.

So how did he estimate the size of the explosion from this picture?

Here is a simplified version of the reasoning he used: Assume that a nuclear explosion releases a finite amount of energy (E) at a single point in space and time. Assume that most of the energy goes into movement (ie, kinetic energy) of the air in the expanding blast wave. Now kinetic energy is a function of mass (density times volume) and velocity (distance per unit time); furthermore, volume is a cubic function of distance.

Put it all together, and the energy scale (E) is logically a function of distance, time, and density scales. Now the most obvious distance, time, and density scales in this estimation problem are the blast-wave radius (r), the time after explosion (t), and the the mean density of atmospheric air (d), respectively. Put it all together and you get E ~ d*r^5/t^2 -- you can derive this algebraically if you want, but it's not really necessary; that's the only dimensional expression for (E) that makes the units work correctly.

When Taylor saw the photo above in Life magazine, he realized he now had all the information he needed to estimate the yield (E). From the picture's distance scale, he estimated r = 140 m; the US government helpfully labeled the time after detonation as t = 0.025 sec. Using d ~ 1 kg/m^3 for air density (a decent mean value), we plug in d, r, and t to get E ~ 86 terajoules, or 21 kilotons TNT. The actual (classified) answer was 20 kilotons. Not bad for just going off a picture. In truth we got a bit lucky that the factors of 2, 3, pi, and so forth tend to cancel out in this case, but we weren't really looking for an exact answer.

When Taylor published this result, many people assumed that he was leaking top-secret information. But there was no leak. Sir Geoffrey had demonstrated the power of images, intuition, and visual analysis, a concept that has roots much older than visualization software.

Elegant analyses like these have a beauty in their own right, but they are also important because they are efficient -- they save time, conserve resources, persuade convincingly, and prevent wasted effort. And maybe -- if we are successful at Tableau -- that's the real answer when someone asks "what we do." Based on your experience with Tableau, have we succeeded? I'd like to hear your comments, pro and con.

You might also be interested in...


This example brought back a memory from the 60's I had such a problem on my comprehensive exam for a PhD. (i passed) The cleverness of taking the minimal and deducing the maximum is a special skill, and encouraged in physics.

Developing intuition about a process as represented by data is essential to any hope of understanding. Einstein was legendary for his physical intuition. The answer to so much was already between his ears - what remained was to complete the representation with what is the ultimate visualization tool, the equations. Of course they are just a bit too succinct to reveal the whole story to most of us.

Einstein talked about the notion of "tutored intuition." You really have to know the stuff before the right chart is going to open any doors. But that process works in reverse. If you fiddle with that chart enough, the right stuff might reveal itself.

Now I am an epidemiologist, and just recently I am finding the public health community finally beginning to really exploit innovative thinking about data, such as the spreading use of Geographic Information Systems. Maps (spatial analysis, spatial statistics, plain old common sense maps) are taking public health practice to a new place.

Tableau-like capability has not arrived on the public health practitioner desk. Most remain stuck in the spreadsheet world but long to escape.

More and more of Ed Tufte's seminar attendees are from health departments. Those day-long seminars are spendy, but budgets get re-worked to have a few people go when Ed Tufte comes to town.

The public health community knows that the old ways of presenting data are not working and are dead. All dimensions that are available with a spread sheet have been looked at. That turnip bleeds no more.

Folks come back from those seminars and are very excited about his ideas. They are all dressed up, they are ready to go to the Ball. But there is no one to go with with. Pushing this over the edge, enter Prince Tableau.

I have seen many times the power of a good map to put people into a different mindset about a problem - instantly. With my first doodles with Tableau (200,000 records, 800+ variables) I got a big rise out of a group of folks whose heads usually hit the table shortly after the first graph appears.

"I never thought about it that way before." When people, who are not particularly quantitatively oriented, get into a animated discussion looking at a bunch of line, bar, and scatter plots and then start making lists of charts they would like to see next - I'd call that a success.

That's wonderful to hear.... I'm sorry I didn't see this comment until now but it is quite profound.

I can see how public health, an important and noble field in itself, could greatly benefit from better visual representations of information -- particularly the use of new mapping techniques, which allow a viewer to make rapid visual connections between parameters. It's so motivating to hear how Tableau, and more broadly the new thinking it represents, is changing things for the better.

I don't think you're alone in what you're seeing by the way. I've been in so many presentations (academic and business) where people have presented a "major advance" that turned out to be merely an existing view of data, only shifted slightly or populated more densely. Truly, though, the most significant advances in any field really come from seeing the same things in a new way, not more intensely the same way.

In the past, shifting perspective like that has been called clever, extraordinary, out-of-the-box thinking. Hopefully someday, with new tools like Tableau, that will be considered routine, not revolutionary.