---
title: Inmon Methodology
description: Learn about the top-down approach and a centralized Enterprise Data Warehouse (EDW) to build an integrated, consistent data architecture.
---

import { Aside, Steps } from "@astrojs/starlight/components";

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.

<Aside type="note" title="Example">
A multinational company might use an EDW to integrate data from all regions, ensuring consistent reporting and analysis across the business.
</Aside>

<center>
<img src="/images/chapter3-Images/inmon-approach.png" width="800" alt="Bar chart" />
</center>

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.