Data Visualization - Stability (Volatility) Chart

I used to be a big Simpson's fan.  However, after season 11, it just wasn't the same.  There was this one episode that I fondly remember.  Homer was having financial difficulties and he was trying to come up with a way to solve this problem.  In a dream sequence, he was told that he came up with a brilliant idea by a group of scientists.  Homer kept on pestering them to see what he had invented.  Just as a scientist was about to open his fist to reveal the invention, Homer woke up.

I had a similar experience, but I was fortunate enough to remember what I dreamed about.  Sadly, it was about data visualization rather than the next million dollar idea.  I call it the "Stability (Volatility)" chart.  The purpose is to quickly see how stable a particular trend over time behaves.  The goal is to have a flat line.  If there are changes above or below the "Stable" line, then there is a problem.  A good example of it's use is the following scenario.

Consider the following Six Sigma - C Chart shown below:
Six Sigma - C Chart

This can be simplified with the following Stability chart, like so:

Stability (Volatility) Chart

When the trend goes above the bounds, then this would be represented as a spike in the Stability chart. When the trend goes below the bounds, then this would be represented as a dip in the Stability chart.

By using the Stability chart, you can at a glance see how stable a trend is. If you need to see the details, you could simply drill down and show the full Six Sigma chart.

Now, you don't have to have 3 values (above, stable, below). You can just have two states [(above, stable) or (stable,below)]. The idea is to show a deviation from the stability. Other good information to provide would be how many times it is unstable and the number of instabilities per period.

As for the data setup, it's quite easy. Just make sure your original values get calculated to: 1 (for above), 0 (for stable), -1 (for below). Enjoy!
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posted @ Friday, January 21, 2011 1:29 PM

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