What is the meaning of data model?

A data model identifies:

• Data components — The elements that make up the data, including raw data, processed data, and associated metadata.
• Elements' relationships — How the data components are connected and interact with each other.
• Data usage — The intended purposes and potential future applications of the data.

It facilitates collaboration among different teams (IT, business analysts, management) by providing a shared understanding of data requirements and relationships. By defining the data structure and its intended use, data models ensure data consistency, accuracy, and efficient utilization across the organization.

Essentially, data models bridge the gap between business needs and technical implementation.

What are the different types of data models?

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There are several types of data models. Let's elaborate on them with key elements:

Conceptual data model

• Purpose: Defines the business perspective of data. Most used for communication between business users and IT teams.
• Focus: Entities and their relationships at a high level.
• Example: Entity-Relationship (ER) diagrams.

Physical data model

• Purpose: Specifies how data is physically stored. Directly translates to database creation scripts.
• Focus: Table structures, columns, primary and foreign keys.
• Example: Database schema.

Relational data model

• Purpose: Organize data into tables with rows and columns. The most widely used data model for traditional databases.
• Focus: Structured data and relationships between tables.
• Example: SQL databases.

Non-relational data model

• Purpose: Handles unstructured or semi-structured data flexibly. Suitable for big data applications and real-time processing.
• Focus: Scalability and performance.
• Example: NoSQL databases.

Dimensional data model

• Purpose: Supports data analysis and reporting. Used in data warehouses.
• Focus: Measures and dimensions for multidimensional analysis.
• Example: Star schema, snowflake schema.

Enterprise data model

• Purpose: To provide a unified view of data across an organization and serve as a foundation for enterprise information systems.
• Focus: Data consistency and integration.
• Example: Enterprise data warehouse.

Examples of data modeling

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Data modeling is used across various industries and domains. Above, we briefly described all models and possible use cases. Let's take two of the most commonly used models—relational and dimensional data models—and provide specific examples from real life.

Relational data models are designed to capture and manage current data. They are typically used in operational systems like banking or healthcare, where data is constantly updated.

For example, a bank would use a relational model to store information about its active customers. In contrast, a clinic would use it to maintain a list of patients with upcoming appointments.

On the other hand, dimensional data models focus on storing historical data for analysis and decision-making. They are the backbone of data warehouses, providing a clear and efficient way to explore past performance. This model revolves around fact tables containing numerical measurements (e.g., sales, revenue) and dimension tables providing context (e.g., customer, product, time). Star and snowflake schemas are common structures for dimensional models.

In a star schema, dimension tables directly connect to a central fact table. Let's consider a simple e-commerce scenario.

A snowflake schema introduces additional layers of normalization, breaking down dimension tables for better efficiency in some cases. Let's expand the customer dimension table into a snowflake schema for better normalization: