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Table of contents
1.
Introduction
2.
The architecture of the Inmon Approach
3.
Inmon’s Top-Down Approach
3.1.
First Step
3.2.
Second Step
3.3.
Third Step
4.
Advantages of the Inmon Approach
5.
Disadvantages of the Inmon Approach
6.
Frequently Asked Questions
6.1.
Are Data Mining and Data Warehousing the same?
6.2.
In the context of a data warehouse, what do you mean by a fact table?
6.3.
In the context of data warehousing, what do you mean by dimension table?
6.4.
What exactly do you mean when you say "real-time data warehousing"?
6.5.
What exactly do you mean when you say "active data warehousing"?
7.
Conclusion
Last Updated: Mar 27, 2024

Inmon Approach In Data Warehouse Designing

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Introduction

We live in a data revolution era. A poll of more than 2,200 business and IT leaders worldwide reveals how companies can utilise the data revolution's technologies.

The Data Warehouse plays an even more essential role in this situation because of its unique promise as an integrated enterprise store of data.

Nowadays, Inmon architecture and Kimball architecture are two of the most popular data warehouse architecture models.

This article will discuss Inmon architecture and its pros and cons. Then we will look at Kimball architecture in the 2nd part of our Data Warehouse Designing article.

The architecture of the Inmon Approach

In the perspective of Bill Inmon, the first step should be to create a consolidated enterprise-wide data warehouse. This can be made by connecting numerous databases to the analytical needs of departments, which are later referred to as data marts. As a result, this method is known as the top-down approach.

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Inmon’s Top-Down Approach

Inmon’s Approach to Data Warehouse Designing mainly consists of the following three steps:

Step 1: Specifying the Primary Entities of the Organisation.

Step 2: Develop a detailed Logical Model of each entity.

Step 3: Development of the Final Physical Model.

First Step

Bill Inmon, known as the "Father of Data Warehousing," devised the idea of creating a data warehouse. It begins with creating a corporate data warehouse data model, which specifies the primary topic areas and entities that the company deals with, such as customers, products, and vendors.

 According to Bill Inmon, a data warehouse is a "subject-oriented, nonvolatileintegratedtime-variant collection of data supporting management's choices.”

Let’s quickly understand the terminology of Inmon’s Data Warehouse Definition.

  • Subject-oriented
    The warehouse data is organized to connect all data pieces relevant to the same real-world event or entity.
  • Non-volatile
    Data is never overwritten or erased in a data warehouse. The information is read-only and static once committed so that we can use it for reporting in the future.
  • Integrated
    Data from most or all of an organization's operational applications are stored in the database, and this data is made consistent.
  • Time-Variant
    Changes to the database's data are regularly tracked and documented so that reports may be generated that demonstrate how things have changed over time.

Second Step

After specifying the primary entities, the model develops a detailed, logical model for each primary entity. For example, An analytical model for products is created with all of the attributes associated with that object. 

This logical model could comprise ten different entities under the product heading, including specifics such as business drivers, aspects, linkages, dependencies, and affiliations.

The Bill Inmon design technique builds entity structure using the normalised form, reducing the data redundancy. As a result, business requirements are identified, and data update abnormalities are avoided. 

Furthermore, this top-down database architecture method is resistant to business changes and contains a dimensional perspective of data across data marts.

Third Step

The physical model is then built, which adheres to the normalised structure. The Bill Inmon model establishes a single source of truth for the entire organisation. Because of the model's normalised structure, data loading becomes easier. However, using this architecture for querying is difficult due to the multiple tables and linkages.

According to the Inmon data warehouse technique, Organisations should build data marts individually for each division, including finance, marketing, and sales. The data warehouse receives all of its data in an integrated format. The data warehouse serves as a single data source for numerous data marts to assure integrity and consistency across the company.

Advantages of the Inmon Approach

The following are the main advantages of the Inmon method:

  • Because it is the only source for data marts and all data in the data warehouse is integrated, the data warehouse genuinely acts as the enterprise's single source of truth.
  • Due to the limited redundancy, data update abnormalities are avoided. This simplifies the ETL(Extraction, transformation, and loading) procedure and reduces the risk of failure.
  • Because the logical model represents the distinct business entities, We may easily understand the business processes.
  • Very versatile — As business requirements change or source data changes, updating the data warehouse is simple because everything is in one location.
  • Can handle a variety of reporting requirements within the organisation.

Disadvantages of the Inmon Approach

The following are some of the drawbacks of the Inmon method:

  • As more tables and joins are added, the model and implementation can grow increasingly complicated.
  • We'll need people knowledgeable in data modelling and the business in general. These resources might be difficult to come by and can be rather costly.
  • Management should be aware that the initial setup and delivery will take longer.
  • More ETL work is required as the data marts are developed from the data warehouse.
  • A vast team of professionals is required to manage the data environment efficiently.

Frequently Asked Questions

Are Data Mining and Data Warehousing the same?

No, Data Mining and Data Warehousing are two different concepts. Data warehousing organizes and combines data into a single database, whereas data mining extracts meaningful information from databases.

 

In the context of a data warehouse, what do you mean by a fact table?

A Fact table is just a table in a Data Warehouse system that holds all of the facts or business information that may be accessible for reporting and analysis when needed. These tables contain direct fact fields and foreign fields that connect the fact table to other dimension tables in the Data Warehouse system.

 

In the context of data warehousing, what do you mean by dimension table?

A dimension table is a table in the star schema of a data warehouse. Data warehouses are built using dimensional data models, which are fact and dimension tables. Dimension tables describe dimensions and contain dimension keys, values, and characteristics. It's usually relatively small.

 

What exactly do you mean when you say "real-time data warehousing"?

Real-time data warehousing refers to a system that represents the state of the warehouse in real-time.

 

What exactly do you mean when you say "active data warehousing"?

Active data warehousing refers to the technical capability of collecting transactions as they occur and integrating them into the warehouse and maintaining batch or planned cycle refreshes.

Conclusion

This article has discussed the Inmon approach for Designing the Data Warehouse. We extensively discussed Inmon’s top-down approach. We also ran through the advantages and disadvantages of the Inmon approach in data warehousing.

We hope this article has helped you. You can learn about different Data Warehousing Tools.

Recommended Reading: Instruction Format in Computer Architecture

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Kimball Approach In Data Warehouse Designing
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