The advent of data models has been the impetus for enormous progress in data management. Conceptual advances have provided a framework for elaborating database design, and wonderful tools have enabled data professionals to design databases in practice. Today, data modeling is viewed as a necessary skill in data management, and rightly so. Underlying this, there is the assumption that a data model can capture all the information about the design of a database. This assumption is rarely questioned, but is it true? This is not just a question of whether different data modeling approaches yield different levels of accuracy about how an enterprise sees the information in a particular subject area. Rather, it is about whether any data model can truly specify all the design information for a database. I would submit that there are real limits to what data models can do, and failure to understand these limitations can result in data management problems at a numbers of levels.

Data modeling and data administration in general are usually focused on the "logical" level. In many ways, this is a good thing. We want a logical data model to represent how the business truly views its data. There can be arguments for why we have to denormalize a data model to implement it as a particular database. Typically, such arguments are based on the need for system performance, and we can argue about how valid they are. However, a logical data model is still required to understand how the business sees the data.

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