The Logical Data Model

Introduction

A logical data model (LDM) establishes the structure of data elements and the relationships among them. It is independent of the physical database that details how the data will be implemented.

It is a high-level conceptual representation of the data organisation of a database system. It is an abstract description of the data elements, relationships, and constraints that exist within the database. This model is designed to capture the business domain and translate it into a set of data entities, attributes, and relationships, which are defined between data entities to describe how they are related to each other.

The logical data model hides the underlying implementation details and complexity of the database system and it is designed to capture the business domain knowledge and translate it into a way that can be implemented in a database system.

For example, an e-commerce website's might want to implement a customer data model database using a logical data model that will capture information and data about the e-commerce business. This LDM will define data entities like "Customers," "Products," and "Orders" and the relationships between them to track customer purchases and other information.

Benefits

Creating a logical data model before the actual physical data model implementation will have several benefits:

1. It will be easier to understand

2. It is flexible and can be easily modified and corrected

3. It will be easier to maintain

4. It will serve as a bridge between the business domain and the physical database implementation

An LDM is a conceptual representation of data requirements that is used to define and organise the data. It is a blueprint that describes the logical relationships among data elements and the rules that govern those relationships. The LDM is independent of any physical database technology, which means that it can be implemented using a variety of different database systems.

Why use an LDM?

LDMs are used for a number of reasons.

1. First, they help to ensure that the data needed to support the business functions are accurately and completely defined.

2. Second, LDMs help facilitate communication among stakeholders data modelers and developers. By providing a clear and consistent representation of data requirements, LDMs can help ensure that everyone is on the same page and working towards the same goals. This can be especially important in large or complex projects, where there may be many different stakeholders with different perspectives and priorities.

3. LDMs can help identify data redundancies and inconsistencies, which can improve data quality and reduce the risk of errors.

4. They can also help identify gaps in data coverage, which can inform data collection and acquisition strategies.

A logical data model is a conceptual representation of data requirements that is independent of any physical database technology. It describes the logical relationships among data elements and the rules that govern those relationships. A physical data model, on the other hand, is a design for a physical database that is based on the logical data model. It includes details about how the data will be stored and accessed in the database, as well as any performance optimizations that may be necessary.

The physical data model will also include details about the data types and sizes of the columns, as well as any indexes or constraints that may be necessary for performance or data integrity. These details are specific to the chosen database system and are not included in the logical data model.

A logical data model describes the entities and the relationships among those entities, while a physical data model describes how the entities will be implemented in a physical database. This means that a logical data model focuses on the conceptual aspects of data modeling, while a physical data model focuses on the technical aspects of database design. This separation of concerns can make it easier to understand and communicate the data requirements, and it can make the physical data modeling process more efficient.

Reviewing your LDM design

When reviewing a logical data model is important to follow these steps:

1. Completeness: Review the entities and relationships. Make sure that all of the necessary entities have been included, and that the relationships among the entities are correctly described. Make sure that the logical data model includes all of the necessary data, and that the data is defined at the appropriate level of detail for its intended use.

2. Redundancies: Look for any data redundancies or inconsistencies in the logical data model. For example, check for entities that have overlapping attributes, or for relationships that are described in multiple places with different terms.

3. Requirements: Make sure that the logical data model accurately reflects the data requirements of the organization. Check that all of the necessary data is included, and that the data is defined at the appropriate level of detail.

4. Business rules and constraints: Review the logical data model to ensure that it takes into account any business rules that may apply. For example, check that any constraints or rules that are specified in the logical data model are consistent with the organization's business practices.

5. Compliance: Review the logical data model to ensure that it complies with any relevant data standards or regulations. For example, check that the logical data model adheres to any data privacy or security requirements that may apply.

6. Feedback: Share the logical data model with stakeholders, such as business users, data modelers, and developers, and get their feedback. This can help to identify any issues or gaps in the logical data model, and it can ensure that everyone is on the same page and working towards the same goals.

Normalisation

Once a logical data model has been defined, it is important to ensure that normalisation is respected, in particular in terms of the first, second and third normal form, as defined below:

• First Normal Form (1NF): Ensure that each attribute contains atomic values and that there are no repeating groups. If an attribute contains multiple values, consider splitting it into separate attributes or creating a separate entity to represent the multivalued attribute.

• Second Normal Form (2NF): Identify candidate keys for each entity, which are attributes or combinations of attributes that uniquely identify each entity instance. If necessary, decompose entities to remove redundancy and achieve 2NF.

• Third Normal Form (3NF): Eliminate transitive dependencies by ensuring that non-key attributes depend only on the candidate key and not on other non-key attributes. If there are transitive dependencies, decompose entities further to achieve 3NF.

By following these steps, you can review a logical data model and validate its accuracy and completeness. This can help ensure that the data model is a solid foundation for physical data modeling and that it supports the business functions of the organisation.