The biggest pitfall that I have seen is not verifying the data. We've all had teachers/professors to tell us that sometimes we need to do a "sense check" on our answer. Ask yourself, "Does this make sense?" I mentioned a situation in my article where we had pricing codes that included information about the type of product we made. We were in "discovery" discussions with a vendor when we learned that our own data was incorrect - we were accusing the vendor of supplying parts for a product that their part (or their competitor's part) did not even go in. The analogy of suggesting ink toner cartridges are causing your car to stall. Basically, our data suggested that we were replacing ink toner cartridges in automobiles.
It is absolutely critical that you trust the data. Enough cannot be said for data validation... and when the volume of data increases, it becomes more critical (espectially when the bill for a lawsuit is approaching millions of dollars).
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