**Data modeling** is a crucial aspect of business analysis that plays a pivotal role in understanding, organizing, and managing data within an organization. It involves the creation of abstract representations of data structures, relationships, and entities to facilitate efficient data management and decision-making processes.

**Types of Data Models**

- **Conceptual Data Model**
  - Offers a high-level view of data entities and relationships;
  - Focuses on business concepts, not technical details;
  - Helps understand the scope of data requirements.

- **Logical Data Model**
  - Defines data structures, entities, attributes, and relationships in a platform-independent manner;
  - Represents data abstractly, irrespective of specific technologies.

- **Physical Data Model**
  - Specifies the physical implementation of data structures, considering database-specific details;
  - Defines tables, indexes, constraints, and storage details.

- **Dimensional Data Model**
  - Commonly used in data warehousing and business intelligence;
  - Organizes data for efficient querying and reporting using facts and dimensions.

Data modeling is an iterative process, involving collaboration with stakeholders, validation of assumptions, and model refinements. Effective data modeling ensures well-structured, accessible data aligned with an organization's strategic objectives, enabling informed decision-making and business success.

## Logical model example

What does a Logical Data Model primarily focus on?

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Data Modeling

Logical model example

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Data Modeling

Logical model example