I have a good friend and colleague who is a very accomplished engineering leader. During lunch yesterday, he told me a story about how he solved what at first seemed to be an extraordinarily challenging problem.
The first instances of a new computer processing chip that his team had designed had returned from the factory, and it didn’t seem to be functioning in the way that everyone expected. Immediately, people started to hypothesize about the cause, and, based on very little of the existing data, a consensus had been reached that it was Cause A, which represented a fundamental flaw in the architecture of the chip. Almost everyone involved was convinced that Cause A was the problem. A ton of effort was then deployed to try to find a work-around for Cause A.
Instead of jumping on the bandwagon, my friend examined the overall characteristics of how the problem was presenting, by getting a big picture of the available data. He noticed that there was a systematic pattern to the incorrect functionality, and that in fact you could look at the functionality from a different perspective and it could be perceived as not being broken at all. His thinking was that if Cause A really was the problem, then the observed behavior should have not only been completely wrong, but randomly wrong.
All scientific breakthroughs occur when…
