Occasionally one may hear that a data model is “over-normalized,” but just what does that mean? Normalization is intended to analyze the functional dependencies across a set of data. The goal is to understand which data elements relate to what other data elements. The context of a normalization exercise is the semantically constructed reality within a chosen organization. There are available industry-based data models, because across an industry there certainly is a degree of semantic alignment, but ultimately each organization has its own unique ways of speaking and thinking. That organizational uniqueness is why industry-based data models only serve as a jumpstart and they are not implemented as-is within any specific business.
