Logical Data Modeling Tutorial

Logical Data Modeling:

This is the actual implementation and extension of a conceptual data model. A Logical data model is the version of a data model that represents the business requirements (entire or part) of an organization and is developed before the physical data model.

As soon as the conceptual data model is accepted by the functional team, development of logical data model gets started. Once logical data model is completed, it is then forwarded to functional teams for review. A sound logical design should streamline the physical design process by clearly defining data structures and the relationships between them. A good data model is created by clearly thinking about the current and future business requirements.
Logical data model includes all required entities, attributes, key groups, and relationships that represent business information and define business rules.

In the example, we have identified the entity names, attribute names, and relationship. For detailed explanation, refer to relational data modeling.

Example of Logical Data Model:

In the example, we have identified the entity names, attribute names, and relationship. For detailed explanation, refer to relational data modeling.

 

What you can learn in our Logical Data Modeling Training?

  • How to normalise the logical data model?
  • How to group entities?
  • How to add attributes to entities?
  • How to create constraints?
  • What is cardinality? How to connect different entities with relationship lines?
  • What is subject area? Why subject area is needed?
  • What kind of presentations are required to speak with business analyst and subject matter experts?
  • How to compare different versions of a Logical Data Model?

 

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