How to Create a Logical Data Model?

Creating a logical data model (LDM) involves a series of steps to represent the data requirements and relationships within a system or organization. Here's a breakdown of the process:

Steps to Create a Logical Data Model

The creation of a logical data model involves several key stages:

1. Add Datasets with Facts and Attributes: This involves identifying the core entities (datasets) that your model will represent. For each dataset, you'll need to define its attributes (characteristics) and the facts (data points) associated with it.

   • Example: Imagine you're modeling a sales system. Datasets might include Customers, Products, and Orders. Attributes for Customers could be CustomerID, Name, Address, and Email. Facts are the actual data stored for each customer.

2. Create Relationships Between the Datasets: Define how the different datasets relate to each other. Common relationships include:

   • One-to-one: One record in Dataset A relates to only one record in Dataset B.

   • One-to-many: One record in Dataset A relates to multiple records in Dataset B. (e.g., One customer can have many orders.)

   • Many-to-many: Multiple records in Dataset A relate to multiple records in Dataset B. (e.g., Many products can be in many orders. This often requires an intermediary table.)

   • Example: Customers can have multiple Orders (one-to-many). Each Order relates to one Customer (many-to-one from the Order perspective). An order will contain order details and products.

3. Add a Date Dataset: Incorporate a Date dataset to manage time-related data effectively. This is crucial for time-series analysis, reporting, and filtering data based on specific dates or periods.

   • Example: The Date dataset might include attributes like Date, Year, Month, Day, Quarter, etc. This dataset is often related to other datasets like Orders to track order dates.

4. Map the Datasets to Source Tables: Link the datasets in your logical model to the actual source tables in your databases or data warehouses. This step establishes the connection between the logical representation and the physical data storage.

   • Example: You would map the Customers dataset to the Customers table in your database, specifying which attributes in the dataset correspond to which columns in the table.

5. Publish the LDM: Once the model is complete and validated, publish it to make it available for use by reporting tools, data analysis platforms, and other applications.

Example: Sales Data Model

Here's a simplified example demonstrating the concepts:

Dataset	Attributes	Relationships
Customers	CustomerID, Name, Address, Email	One-to-many with Orders
Orders	OrderID, CustomerID, OrderDate	Many-to-one with Customers, Many-to-many Products (via OrderDetails)
Products	ProductID, ProductName, Price	Many-to-many with Orders (via OrderDetails)
OrderDetails	OrderID, ProductID, Quantity	Bridges Orders and Products (many-to-many)
Date	Date, Year, Month, Day	One-to-many with Orders

Best Practices

• Keep it Simple: Start with a basic model and add complexity only as needed.
• Involve Stakeholders: Collaborate with business users and data experts to ensure the model accurately reflects requirements.
• Document Thoroughly: Document all datasets, attributes, relationships, and mapping rules.