Data modeling - IBM Mainframe

The two main purposes of data modeling are to assist in the understanding of the meaning semantics of the data and to facilitate communication about the information requirements. Building a data model requires answering questions about entities, relationships and attributes. In doing so, the designers discover the semantics of the organization's data, which exist whether or not they happen to be recorded in a formal data model. However, this meaning may remain poorly understood until they have been correctly documented.A data model makes it easier to understand the meaning of the data and thus we model data to ensure that we understand:

  • Each user's perspective of the data
  • The nature of the data itself, independent of its physical representations
  • The use of data across application areas
  • Data model can be used to convey the designer's understanding of the information requirements of the organization.If both parties are familiar with the notation used in the model, it will support communication between users and designers.

Increasingly organizations are standardizing the way that they model data by selecting a particular approach to data modeling and using it throughout their database development projects. The most popular high-level data model used in database design is the Entity-Relationship (ER) model. An optimal data model should satisfy the criteria listed below:

  • Structural Validity - Consistency with the way the organization defines and organizes information.
  • Simplicity - Ease of understanding by IS (Information Services) professionals and non-technical users.
  • Expressability - Ability to distinguish between different data, relationships between data, and constraints.
  • Non-redundancy - Exclusion of extraneous information; in particular, the representation of any one piece of information exactly once.
  • Shareability - Not specific to any particular application or technology and thereby usable by many.
  • Extensibility - Ability to evolve to support new requirements with minimal effect on existing users.
  • Integrity - Consistency with the way the organization uses and manages information.
  • Diagrammatic Representation - Ability to represent a model using easily understood diagrammatic notation.

However, sometimes these criteria are not compatible with each other and tradeoffs are necessary. For example, in attempting to achieve greater expressability in a data model we may lose simplicity.


All rights reserved © 2018 Wisdom IT Services India Pvt. Ltd DMCA.com Protection Status

IBM Mainframe Topics