We frequently speak of definitions in data management,but THEyare often taken for granted. In particular, it seems that everyone knows what a definition is and that everyone assumes producing definitions is easy. Nobody stops to ask what exactly a definition is and if there are any particular considerations about formulating definitions. Perhaps this is because the educational system in the U.S. focuses on learning definitions by rote for the SATs. More likely it is because we are heavily conditioned by our exposure to definitions as they appear in dictionaries, which may be why we find data dictionaries in many enterprises. It also seems to be why definitions in data models resemble dictionary definitions - one-sentence tautologies composed of synonyms. Indeed, the dictionary model of definition rules supreme in data management.
Surprisingly, two major kinds of definitions have been recognized for the past two-and-a-half millennia, and a long and slow war has been fought between them. A real definition fully explains the nature of a concept. It goes beyond providing awareness that something exists, to tell us what it is. A nominal definition explains the meaning of a word or term. For example, the word "thunder" could be defined as "a noise in the clouds." This gives enough information to know what the word "thunder" is referring to, but it does not tell us much about what thunder really is.
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