Subjugation to the Sigmas

No doubt you've heard about the tyranny of the 9s in reference to computer system availability. You're probably also familiar with the phrase six sigma, either in the context of manufacturing process quality control or the improvement of business processes. As we discovered in the recent Guerrilla Data Analysis Techniques class, the two concepts are related.

Nines	Percent	Downtime/Year	σ Level
4	99.99%	52.596 minutes	4σ
5	99.999%	5.2596 minutes	-
6	99.9999%	31.5576 seconds	5σ
7	99.99999%	3.15576 seconds	-
8	99.999999%	315.6 milliseconds	6σ

In this way, people like to talk about achieving "5 nines" availability or a "six sigma" quality level. These phrases are often bandied about without appreciating:

IBM Introduces the Cognitive Chip

Last week, in the GDAT class, we were discussing performance visualization tools as requiring a good impedance match between the digital computer under analysis and the cognitive computer of the analyst—AKA the brain.

GDAT 2011 in Review

As usual, the Guerrilla Data Analysis Techniques (GDAT) class was a total blast. Motivated students always guarantee that. It would really help our scheduling, however, if people didn't wait until the last nanosecond to register for the class. But given the crazy economic climate, I'm more than happy to do whatever it takes to make GDAT fly.

Guerrillas in the data mist [Photo: P. Newsom]

Some course highlights that you missed:

Q-Q Plots and Power Laws in Database Performance Data

I'm in the process of putting together some slides on how to apply Quantile-Quantile plots to performance data. Q-Q plots are a handy tool for visually inspecting how well your data matches a known probability distribution (prob dsn). If the match is good, the data should line up more or less diagonally in the Q-Q plot. A common usage is to verify normality, i.e. how well the data matches a Normal or Gaussian dsn. In fact, this usage is so common that R even has a separate function called qqnorm() for doing just that.