Logical data modeling is the second of the three stages of data modeling and one of three types of data model.
The three types of data model – conceptual, logical and physical – are used in concert to help teams capture the concepts and structure of new databases and systems. Each stage of modeling adds more detail to the models constructed in the previous stage.
Effective data modeling enables organizations to turn business objectives into viable database designs. A linear approach to progressing through the stages of data modeling is an integral best practice for modern organizations to follow.
It enables organizations to start with abstract business goals and iteratively refine their representation until they can be implemented using a specific database platform.
What is Logical Data Modeling?
Logical models form the link between conceptual and physical data models. In a logical data model, the details outlined in a conceptual model begin to take shape.
Typically created by Data Architects and Business Analysts, the model is less abstract than a conceptual model and helps teams understand the data’s details and the logistics of building and implementing a new system. As with conceptual models, they are platform independent and can therefore be created without committing to an implementation path.
This provides organizations with the opportunity to evaluate their current state, and decide whether they need to invest in new technologies to target the desired future state. It may also become apparent that the “ideal” future state is out of scope, and that objectives need to be revised.
Here’s a quick look at the other types of data models:
Conceptual data models can be defined as high-level models that provide an in-depth view of business concepts. They are useful for identifying key business and system entities and establishing relationships between them.
Conceptual models are platform-independent, delaying the choice of database platform until later in the development process. These models are usually created for a business audience.
Physical data models describe how the information and details identified through the conceptual and logical modeling phases will be implemented using a particular database platform. The physical models define the database schema as well as the design elements such as tables and keys needed to develop a system.
Once the physical model is completed, the database team should have all the information they need to implement the design on the given platform.
Don’t Skip Logical Data Modeling
Some teams may attempt to bypass the creation of a logical data model and move straight from a conceptual model to a physical model and implementation. They may try this due to time or resource constraints. Perhaps they feel it is an unnecessary step and are prepared to develop a database based purely on a conceptual model.
This approach is not recommended and can lead to more work in the long run, obliterating any time savings that were accrued by ignoring the logical model.
Without a logical model that fleshes out the details of the prospective database, the business objectives identified and codified in a conceptual model risk being lost or misinterpreted.
It can be extremely challenging to accurately turn business requirements directly into the constructs required by a specific database platform. This difficulty can result in physical models that do not reflect the needs of the business.
Another reason not to skip the logical modeling phase is that during model creation, it may become apparent that the business concepts attempting to be addressed are out of scope regarding current systems and resources. A logical model can point out systems and processes that need to be modified or improved.
How to Build a Logical Data Model
In the standard data modeling workflow, the creation of a conceptual data model is a prerequisite for building a logical model. During the construction of the logical model, details are added to fully document how the ideas outlined in the conceptual model will be represented in a database.
While the conceptual model is concerned with abstract business requirements, the logical model begins to fill in the detail. It defines the structure of data elements and the relationships between them. Some of the types of information that are defined in logical models are primary, foreign, and alternate keys. Attributes like the format and length of data elements are defined in a logical model.
The way the data elements will be represented in a database is left to the physical model and are not part of logical modeling. Tables, columns, and other physical objects used to implement the original business concepts are not addressed in this phase of data modeling.
Using a dedicated data modeling tool can give teams a big advantage over attempting to complete the process manually. Simple models may be developed manually, but as the complexity ramps up, it becomes increasingly difficult to capture all relevant information and leaves teams open to making mistakes.
Building Effective Logical Data Models with ER/Studio
Organizations looking to improve their data modeling capabilities should evaluate ER/Studio – IDERA’s suite of data modeling applications. The tools will help enterprises get the most out of their data resources. IDERA’s ER/Studio provides businesses with the necessary tools to create the conceptual, logical, and physical data models required for efficient database design. ER/Studio is available in three versions that address all stages of data modeling.
ER/Studio Business Architect
Business Architect is used to create the conceptual data models needed to align business objectives with database design. The tool facilitates mapping the relationships that exist between the people, processes, and data that need to be incorporated into a database. Conceptual models created with Business Architect are easily exported to ER/Studio Data Architect for the creation of logical models.
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ER/Studio Data Architect
ER/Studio Data Architect lets teams take conceptual models and create and manage logical and physical models. The models developed by the tools are suitable for database creation of other data-driven activities like data governance. With Data Architect, teams can create the models required to implement the organization’s business concepts.
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ER/Studio Enterprise Edition
ER/Studio Enterprise Edition helps teams manage and maintain data modeling consistency through the use of a shared model repository. This tool is designed to promote the collaboration that is key to using data models effectively. All stakeholders can be alerted when changes are made to enterprise data models.
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