I was at an event a few weeks back talking about data governance, and a number of the attendees were from technology or software companies. I used the term "semantic inconsistency" and one of the attendees asked me to provide an example of what I meant.
Since we had been discussing customers, I thought about it for a second and then asked him what his definition was of a customer. He said that a customer was someone who had paid the company money for one of their products. I then asked if anyone in the audience was on the support team, and one person raised his hand. I asked him for a definition, and he said that a customer is someone to whom they provide support.
I then posed this scenario: the company issued a 30-day evaluation license to a prospect with full support privileges. Since the prospect had not paid any money for the product, according to the first definition that individual was not a customer. However, since that individual was provided full support privileges, according to the second definition that individual was a customer.
Within each silo, the associated definition is sound, but the underlying data sets are not compatible. An attempt to extract the two customer lists and merge them together into a single list will lead to inconsistent results. This may be even worse if separate agreements dictate how long a purchaser is granted full support privileges – this may lead to many inconsistencies across those two data sets.
However, slight semantic differences are often overlooked in lieu of the quest for that "single source of truth." In other words, a byproduct of uncontrolled data consolidation is data of poorer quality than what you started with!