My previous post about root causes in complex systems, in retrospect, looks a little bit like a rant. That doesn't bother me too much, really... but I wish I had included the following info: it is one way to go about resolving the mess that complex systems can make of your root cause analysis.
Basically, when you need to analyze a problem or event characterized by complex system interactions, you should start by investigating (to the extent possible) using your normal event sequencing or causal tree methods. At some point, the event/cause model that you're creating with your investigation may become unmanageable or chaotic; that is a sign that you are zoomed in too close to the situation. This can make it very difficult (or even impossible) to know what you're looking at; such a condition has many names or descriptors in colloquial English.
- "drowning in data, starving for information"
- "can't see the forest for the trees"
- "missing the big picture"
- "mired in details"
- "lost in minutiae"
- "off in the weeds"
Zooming out or taking a step back will let you get a better sense of the system's overall structure and pattern. Further analysis should be possible at this macro scale, and may give you enough information to understand the general flow of state changes that took place during the problem or event. This macro scale data can then make it possible to zoom back in to the situation and help you understand the micro scale chaos you were looking at previously. Root cause analysis still wins the day! 😀
by Bill Wilson