Data Modeling

Introduction

Data modeling is the process of creating a visual representation of an information system's data elements and their relationships. It serves as a bridge between business requirements and technical implementation, enabling stakeholders to understand and communicate the structure of data within an organization. By abstracting complex data systems into simplified models, data modeling facilitates decision-making, system design, and data integration.

The primary objective of data modeling is to define the types of data used within a system, their attributes, relationships, and constraints. This ensures consistency, accuracy, and scalability in data management. Data models are typically created using standardized notations and techniques, making them universally interpretable across different teams and organizations.

Levels of Data Modeling

Data modeling can be categorized into three levels of abstraction: conceptual, logical, and physical. Each level serves a distinct purpose and targets different audiences.

Conceptual Data Model

The conceptual data model provides a high-level overview of the system's data. It focuses on identifying the key entities and their relationships without delving into technical details. This model is primarily used by business stakeholders to ensure that the system aligns with business requirements.

For example

• Entities: Customer, Order, Product

• Relationships: A customer places orders; an order contains products.