Inmon Methodology

The Inmon approach, developed by Bill Inmon, starts with creating a centralized data warehouse (EDW). It uses a top-down approach, where all data is stored in a normalized format to ensure consistency and eliminate redundancy.

Inmon is better for large, complex businesses that need a centralized system to manage data consistently and at scale. It creates a unified source of truth, which is ideal for organizations with many interconnected systems and diverse data sources.

How It Works

• Data is stored centrally and then distributed to smaller data marts as needed.
• The focus is on integration and long-term scalability.

Benefits

• Provides a single, unified source of truth for the entire organization.
• Scales well as the business grows.
• Best for managing complex data from multiple sources.

Example

A multinational company might use an EDW to integrate data from all regions, ensuring consistent reporting and analysis across the business.

Inmon’s approach requires to use the Third Normal Form(3NF) to:

• Eliminate duplication and save storage space.
• Ensure data consistency across the system.
• Make updating data more efficient by preventing unnecessary changes in multiple locations.
• Querying made more complex as data is spread across multiple tables.

This is why Kimball’s methodology often uses less-normalized structures like star schemas for simpler and faster queries. Many businesses use a combination of both methods. They often start with Kimball’s data marts to quickly meet immediate needs and then build toward Inmon’s centralized data warehouse for long-term growth and integration. This hybrid approach helps businesses balance speed, flexibility, and reliability while addressing both short-term and strategic goals.

Differences Between Kimball and Inmon

Aspect	Kimball	Inmon
Approach	Bottom-up: Starts with data matts.	Top-down: Starts with a centralized data warehouse (EDW).
Focus	Business process-specific, such as sales or marketing.	Organization-wide integration for all data.
Data Structure	Uses dimensional modelling (star or snowflake schema).	Uses normalized structure (third normal form).
Ease of Use	Simple for end users to query and analyze.	Requires technical expertise for querying.
Implementation Speed	Faster to implement for specific needs.	Slower due to comprehensive design and integration.
Scalability	Scales within specific business processes.	Highly scalable for growing and complex data ecosystems.
Best For	Small to medium-sized businesses or targeted projects.	Large organizations with diverse and complex data needs.
Data Integration	Integrates data at the data mart level.	Fully integrates data at the enterprise level.
Example Use Case	Building a sales or marketing data mart first.	Creating a unified system for global business reporting.

In the next section, we will look at DWH schemas such as Star and Snowflake that arise out of Kimball & Inmon techniques.