Logical Data Model: Effectively Structure Your Data
The Logical data model (MLD) is a fundamental tool in the management of information and databases.
Unlike the physical model, which focuses on how data is stored, or the conceptual model, which focuses on a more general vision, the logical model is an essential intermediate step.
In this article, we'll explore what an MLD is, why it's important, and how to design it effectively, while also emphasizing the importance of a MDM (Master Data Management) To centralize and manage data.
What is a logical data model?
The logical data model is a Structured representation of information Which allows you to understand relationships between data without worrying about the storage format or the type of database used (relational, NoSQL, etc.).
It details entities, their attributes, and the relationships between these entities.
The objective is to offer a more detailed vision than a Conceptual model, while remaining independent of the physical implementation of the data.
Example:
If you are working on a management system of Product catalog, an MLD will allow you to represent entities as a product, category, order, and their attributes (e.g. product name, price, order date) while clarifying relationships (e.g. a product belongs to a category).
Why is the logical data model critical?
1. Consists of the understanding of data
The MLD offers a more complete vision of Data structure than the conceptual model. It allows technical and non-technical teams to better understand the structure of information, the interactions between entities, and the associated business rules.
2. Basis for the physical data model
The logical data model acts as a bridge between the conceptual model and the Physical Data Model (MPD). It makes it possible to move from a representation of data that can be understood by all to an effective technical implementation.
3. Clarifies relationships between entities
Thanks to the precise definition of relationships, the MLD makes it possible to verify the consistency of the data. This is an essential point in creating a reliable information management system.
4. Centralize data with MDM
One MDM (Master Data Management), like the one offered by SolidPepper, guarantees the centralization of all company data. It makes it possible to effectively manage the entities defined in the MLD, to avoid duplicates, and to maintain the consistency and quality of the data across all channels.
The components of a logical data model
Here are the main elements that you will find in an MLD:
1. Entities
An entity represents an object or concept that has its own existence in the database. Each entity has attributes that describe it.
For example : Product gold Shopper Are entities in an MLD.
2. Attributes
Les Attributes Are the Associated Properties To each entity. They detail the characteristics of the entity.
For example: a product entity may have attributes such as product name, price, inventory quantity.
3. Relationships
Relationships define how entities interact with each other. They can be of different types: one-to-one, one-to-many, or many-to-many.
For example: A one-to-many relationship between category and product means that a category can contain multiple products.
4. Primary and Foreign Keys
Les Primary Keys Are unique identifiers for each entity. Les Foreign Keys Allow entities to be linked together, thus defining relationships.
Example schema for a logical data model
A logical data model schema generally takes the form of entity-association diagram (DEA)). Here's how you could structure a visual example for an e-commerce site:
Main Entities : Product, Category, Customer, Order
Attributes for each entity:
- Product: product ID (primary key), name, price, description, stock
- Category: category ID (primary key), category name
- Customer: customer ID (primary key), name, email, address
- Order: order ID (primary key), date, total, customer ID (foreign key)
Relationships between entities:
- A product belongs to a category (one-to-many relationship)
- A customer places an order (one-to-many relationship)
- An order can concern several products (many-to-many relationships)
5 steps to design a logical data model
- Gather business requirements.
Work with stakeholders to understand the needs and business rules that should be represented by data.
- Identify Entities and Their Attributes.
List all important entities and the attributes that describe them.
- Define relationships between entities.
Describe how entities are connected to each other, and determine the cardinalities (one-to-one, one-to-many, many-to-many).
- Create the schema for the logical data model.
Use a chart tool to graphically represent entities, attributes, and relationships.
- Validate with stakeholders.
Verify that the model meets business requirements before moving on to the next phase of physical modeling.
The tools to create a logical data model
Various tools make it possible to create an MLD effectively:
- Lucidchart : Very visual, it is easy to create diagrams and collaborate in real time.
- Draw.io : Free and easy to use to quickly create entity-association diagrams.
- Erwin Data Modeler : More comprehensive, it is designed specifically for data modeling.
You can also use MDM tools like SolidPepper to manage your information in a centralized and organized manner, which simplifies the design and updating of your data models.
Conclusion
The Logical data model is an essential step in managing business information. It helps to clarify the structure of the data, the relationships between the entities, and establishes the foundations for an effective technical implementation.
By combining an MLD with an MDM tool like Solidpepper's, you ensure optimized management of your data on a large scale, guaranteeing the consistency and reliability of information across all channels.
Try It For Free for 30 days, SolidPepper solutions and start streamlining your business.
FAQS
What is the difference between a conceptual model and a logical data model?
The conceptual model is a global vision of the data without details about the structure. The logical model is more detailed, defining entities, attributes, and relationships.
Does the logical data model depend on the database used?
No, the MLD is independent of the technology used for the database. It is an abstract representation, unlike the physical model.
How do you go from a logical model to a physical model?
Once the MLD has been validated, it is converted into a physical model taking into account the specificities of the chosen database (relational, NoSQL, etc.).
What tools should you use to design a logical data model?
Tools like Lucidchart, Draw.io, or Erwin Data Modeler make it easy to create entity-association diagrams to visualize the MLD.
Why is MDM useful for data models?
An MDM centralizes all information and data management, ensuring consistency and easy updating of information across all sales channels.
