Logical Data Model
Introduction
A logical data model (LDM) is a representation of an organization's data, organized in terms of entities, attributes, and relationships. It is a crucial part of the data modeling process, serving as a bridge between the conceptual data model and the physical data model. The LDM is independent of any specific technology or database management system, focusing instead on the business needs and rules.
Entities
In a logical data model, an entity is a real-world object or concept that can be distinctly identified. Entities are often nouns such as 'customer', 'product', or 'order'. Each entity in the model is unique and is defined by its set of attributes.
Attributes
An attribute in a logical data model is a property or characteristic of an entity. For instance, a 'customer' entity might have attributes such as 'customer ID', 'name', 'address', and 'phone number'. Each attribute has a specific data type, such as integer, string, or date.
Relationships
Relationships in a logical data model define how entities interact with each other. For example, a 'customer' entity might have a relationship with an 'order' entity, indicating that a customer can place an order. Relationships can be one-to-one, one-to-many, or many-to-many.
Advantages of Logical Data Models
Logical data models offer several benefits:
- They provide a clear and comprehensive view of the data and its structure, making it easier for stakeholders to understand the data and its relationships.
- They help in validating the accuracy of the conceptual data model, ensuring that it accurately represents the business requirements.
- They serve as a blueprint for the physical data model, guiding the database design process.
- They facilitate communication between business stakeholders and IT professionals, promoting a common understanding of the data.
Creating a Logical Data Model
The process of creating a logical data model involves several steps:
1. Identify the entities: This involves identifying the key objects or concepts in the business domain that need to be represented in the data model.
2. Define the attributes: For each entity, identify the attributes that define it. Each attribute should be atomic, meaning it cannot be further subdivided.
3. Define the relationships: Identify the relationships between the entities. Each relationship should have a name and cardinality, indicating the nature and extent of the relationship.
4. Validate the model: Review the model with stakeholders to ensure that it accurately represents the business requirements.
Logical Data Model vs. Physical Data Model
While a logical data model represents the business requirements in terms of entities, attributes, and relationships, a physical data model represents how the data will be stored in a specific database system. The physical model includes details such as table structures, primary and foreign keys, indexes, and constraints. It is specific to a particular database management system and takes into account its features and limitations.
Conclusion
A logical data model is a vital tool in the data modeling process, providing a detailed and technology-independent view of the data. By clearly defining entities, attributes, and relationships, it helps ensure that the data structure meets the business requirements and serves as a guide for the physical database design.