Visualized Correlations

One interesting approach to root cause analysis is to correlate descriptive variables about errors with one another.  I created this correlogram to visualize every possible combination of correlation coefficients among observations from a large information system.  At the intersection of two numbers is a square that represents the correlation of those two variables across hundreds of observations.


Blue shows a positive correlation, red represents a negative, and darker saturation signifies a stronger relationship.  What trends that might give insights to the root causes?  I chose to explore variables 14 (vertical blue trend), 25 (horizontal), and 27 (horizontal).

The analysis was performed in Excel and also in R using the correlogram package.