You can’t solve a problem without good quality information. If you recall the reports of unintended acceleration that Toyota experienced a couple years ago, you know that one of their challenges was the inability to duplicate the problem. Therefore, their best source of information was from a driver who was collecting information as they were hurtling down the road trying unsuccessfully to bring their car to a stop. Obviously, not the best frame of mind for information gathering.
The challenge that many people encounter is that they engage in “activity-centered fallacies” of gathering too much data in the hopes that the cause of the problem will emerge. Such was the case in this example where an airline collected mountains of data in an effort to solve a problem. Instead, the cause of the problem was something quite simple and they already possessed the information they needed to solve it. So where does one stop their information gathering efforts when trying to understand a problem? I’ve found that targeting your efforts to answer a few critical questions will provide a good definition of the problem that will often allow the cause of the problem to emerge from all the data. Here are the questions:
◾ What is the item (or person) with the problem? \ What similar item might also be experiencing the problem but is not?
◾ Where is the specific problem occurring? Where other problem(s)can be occurring but is not?
◾ When was the problem first observed? When might the problem have first been observed but was not?
◾ How often is the problem occurring? How often could the problem be occurring but is not?
Answers to these questions provide an excellent description of your problem. When you can answer these questions with reliable data, usually the cause of the problem will quickly become evident.
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