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.