Editing Data modeling (section)

=== Modeling methodologies ===
{{See also|Model-driven engineering}}

Data models represent information areas of interest. While there are many ways to create data models, according to [[Len Silverston]] (1997)<ref name="SIG97">Len Silverston, W.H.Inmon, Kent Graziano (2007). ''The Data Model Resource Book''. Wiley, 1997. {{ISBN|0-471-15364-8}}. Reviewed by [http://www.tdan.com/view-book-reviews/5593 Van Scott on tdan.com]. Accessed November 1, 2008.</ref> only two modeling methodologies stand out, top-down and bottom-up:  
* Bottom-up models or View Integration models are often the result of a [[reengineering (software)|reengineering]] effort. They usually start with existing data structures forms, fields on application screens, or reports. These models are usually physical, application-specific, and incomplete from an [[enterprise architecture|enterprise perspective]]. They may not promote data sharing, especially if they are built without reference to other parts of the organization.<ref name="SIG97"/>
* Top-down [[logical data model]]s, on the other hand, are created in an abstract way by getting information from people who know the subject area. A system may not implement all the entities in a logical model, but the model serves as a reference point or template.<ref name="SIG97"/>
Sometimes models are created in a mixture of the two methods: by considering the data needs and structure of an application and by consistently referencing a subject-area model. In many environments, the distinction between a logical data model and a physical data model is blurred. In addition, some [[Computer-aided software engineering|CASE]] tools don't make a distinction between logical and [[physical data model]]s.<ref name="SIG97"/>