Logical dimensional data models

A logical data model is a model that is not specific to a database that describes things about which an organization wants to collect data, and the relationships among these things.

A logical model contains representations of entities and attributes, relationships, unique identifiers, subtypes and supertypes, and constraints between relationships. A logical model can also contain model objects or reference one or more models. After the logical objects and relationships are defined in a logical data model, you can use the workbench to transform the logical model into a database-specific physical representation in the form of a physical data model.

Logical model objects are always contained in a root package object. There is always one root package, but you can add additional packages under the root package to group similar objects together.

Logical data models address the following areas of interest:

Logical data models create a single view of all data. You can create a logical data model to address performance, consistency, and redundancies in your data. You use the logical data model to create a physical data model that accesses the data.

When you create a logical data model, you use the following steps:
  1. Identify entities, attributes, and relationships:
    1. Review documentation for the project. You should define the scope of the project and information about the source system where you are getting your data. Define business requirements, process models, profiles, architectural designs, and data models.
    2. Create general categories that represent the information that you will store in the data warehouse. Make sure that interested business analysts and subject matter experts are involved. These categories should represent business concepts, not just attributes or subsets of data.
    3. Identify the entities. Entities generalize the concepts, involved parties, products, arrangements, locations, or events that will be stored in the database. Entities can be objects in the database or categories that you created above.
    4. Determine the relationships between the entities. Entities can have multiple relationships with other entities, but only one relationship exists between two entities. When you create relationships, create them from the point of view of the business. Create names for each side of the relationship.
    5. Identify the cardinality of each relationship.
    6. Identify the attributes and characteristics of each entity. You should define primary keys during this step. A primary key is a subset of attributes that uniquely identify an entity.
    7. Create text descriptions for entities and attributes. The description should represent the objects from the point of view of the business.
  2. Merge the functional model with the logical data model.
    1. Create, read, update, and delete attributes in the entities.
    2. Maintain the relationships and cardinalities in the logical data model and the values of the attributes.
  3. Validate the logical data model against the requirements of the business. Make sure that the following information is in place in the logical data model:
    • All necessary business process are documented through entities
    • All necessary data is included in the logical data model
    • All entities are named, and all entities have primary keys, attributes, and relationships with other entities in the logical data model
    • The cardinalities between objects reflect their proper relationships
    • Each entity and attribute is found in the data warehouse and are related to functions or process that occur in the data warehouse

Review your data model throughout the process. Keep in mind that you need to stay within the scope of the needs of the business, and you should modify the model as you learn more about the needs of the business. After the data model has been completed, continue to revise and enhance the model to get the most benefit from the data that is available for your business.


Feedback