You can explore the answer to this question by raising some hypothetical questions: "If the answer comes back at some high level, what will we do? If it comes back at some low level, what will we do?" Clearly, if the answer to both questions is 'nothing' or 'nothing different', there's no need to do the analysis. Just continue on your merry way of speculation and opinion about the matter, because it's not going to make a difference to how you operate your business.
Here's another test. Suppose your measuring some non-value-added activity, perhaps a poor cost of quality metric, related to an overhead department. You want to know how much time is being spent by everyone in this department (or several departments) related to finding and fixing mistakes. Here's the question that will help you determine just how much time to spend collecting the data: at what point does our response to the information change?
- About $400K
- Roughly $360K
- Approximately $358,000
- Exactly $357,623
If there's no change in the your organization's response to any of the answers, then do the easiest, less expensive method of data collecting to get a ballpark answer. If you think it's worthwhile to know within 10% what the answer is (i.e. the difference between $400K and $358,000), then develop a data collection method that will get you some precision. However, unless you response to the information is going to be significantly different on the basis of 0.X% (i.e. the difference between $358,000 and $357,623), don't let the super-analyticals among you dictate the precision and accuracy of the data collection method.
If you must, go ahead, collect your rough estimate data, and calculate the answer (e.g. $357,623.48) to show them, but spend your time talking in terms of 'almost $400K' in wasted efforts in your organization.
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