A good example of semantic inconsistency


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!


About Author

David Loshin

President, Knowledge Integrity, Inc.

David Loshin, president of Knowledge Integrity, Inc., is a recognized thought leader and expert consultant in the areas of data quality, master data management and business intelligence. David is a prolific author regarding data management best practices, via the expert channel at b-eye-network.com and numerous books, white papers, and web seminars on a variety of data management best practices. His book, Business Intelligence: The Savvy Manager’s Guide (June 2003) has been hailed as a resource allowing readers to “gain an understanding of business intelligence, business management disciplines, data warehousing and how all of the pieces work together.” His book, Master Data Management, has been endorsed by data management industry leaders, and his valuable MDM insights can be reviewed at mdmbook.com . David is also the author of The Practitioner’s Guide to Data Quality Improvement. He can be reached at loshin@knowledge-integrity.com.

Leave A Reply

Back to Top