methodologies,

Modeling methodologies

Data models represent information areas of interest. While there are many ways to create data models, according to Len Silverston (1997)^[6] only two modeling methodologies stand out, top-down and bottom-up:

• Bottom-up models are often the result of a 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 perspective. They may not promote data sharing, especially if they are built without reference to other parts of the organization.^[6]
• Top-down logical data models, 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.^[6]

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. Unfortunately, in many environments the distinction between a logical data model and a physical data model is blurred. In addition, some CASE tools don’t make a distinction between logical and physical data models.^[6]

^{reeingineering nmpk mcm xbagus utk SDI.. kn?}

^{sbb xpromote data sharing, specific n etc.}

^{so ape method nk gune ni?}