A data model is a graphical means of documenting data requirements and defining database design specifications. It is used by information modelers to:

• Confirm understanding of data requirements with business owners; and
• Provide direction to technical teams who must ultimately built the database structures

What is structure and design?

The structure and design discipline focuses primarily on information modeling.  The goal is to provide direction on standards, best practices, and the use of conceptual, logical, and physical models, naming standards, and tools.

What types of models are required?

The following models are required to support business intelligence:

• Conceptual model, is generally created at the information management strategy stage.  It contains key entities and relationships and presents a high level look at all of the entities within an organization.

• Logical model, is a fully attributed entity relationship diagram (ERD), which shows each entity, its relationship to other entitles and specifies the applicable business rules;

• Dimensional Model, represents facts and dimensions, which are used for reporting and analytical purposes.  There is no logical representation of a dimensional model. It is a “physical” model but sometimes it is shown in a logical version in  the modeling tool with English like names as opposed to physical names; and

• Physical Model, is the final representation of the data base structures that will be generated from the model. It contains the detailed specifications for the database design and, in a model driven environment, the modeling tool will generate the data definition language (DDL) that is used to create the database structure.

When are data models created?

• The conceptual model is created at the information management strategy stage;

• The logical model, and conceptual dimensional model are created during the requirements analysis phase; and

• A physical model and dimensional model are created during the architecture and design phase.

Do we always need a logical data model?

A logical data model is a mandatory requirement for an on-line transaction processing system. It is required to show the correct business rules that need to be applied to the information.

A logical model may not be required if the project only involves information movement.  These types of information management projects might move data from several sources and consolidate it into one target system. Although we may have interim data storage, it is transient in nature and sometimes there is little to be gained by creating a logical model.

What standards and best practices are required?

• An enterprise conceptual model should be created as part of the information management strategy. This model is part of the business model and shows what classes of information are required to support management and business intelligence purposes; 

• An enterprise logical model (EDM) should be created as part of the information management framework. This model should eventually contain all entities and their relationships and a complete set of documentation;

• An application logical model should be created during project requirements analysis phase;

• This should be based on the enterprise conceptual model created in the information management strategy phase; and
• This model should show what information is required to support the specific application. If new entities or definitions are discovered during project requirements analysis, they should be added to the EDM; 

• Application physical data models should be created during the project architecture and design phase and should be:

• Based on the logical model;
• Created for each environment that will be used by the application; and
• Used to generate data definition language (DDL) for each database

• Environment models should be created for each data base environment; and

• The model should generate the data definition language (DDL) used to create database schema's and objects.

Summary...

Standards and best practices are required as part of the business intelligence to ensure rapid project delivery and optimal return on information management investment.