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.
Register or login for access to this item and much more
All Information Management content is archived after seven days.
Community members receive:
- All recent and archived articles
- Conference offers and updates
- A full menu of enewsletter options
- Web seminars, white papers, ebooks
Already have an account? Log In
Don't have an account? Register for Free Unlimited Access