KIMBALL VS INMON REDUX

KIMBALL VS INMON REDUX

KIMBALL VS INMON REDUX

By W H Inmon

The Kimball vs Inmon debate is decades old. But somehow it keeps coming up.

It has been years since anyone has even asked me about it. The last I heard, Ralph has retired to Hawaii. But that was over a decade ago. I could not tell you where Ralph is today.

You may be surprised to know that I have recommended the Kimball approach many times. You also may be surprised to know that Ralph and I have never had a fight or a bad word. In the first conferences I held, the first person I invited to speak was Ralph. I wrote a forward for Ralph’s first book.

Let me share with you my perspective.

Here is my understanding of the Kimball approach. The Kimball approach takes data from applications and quickly produces a data base structure – a dimensional, star schema structure. The result that Ralph described was good for analytical processing. ?If you want to rescue application data that is trapped and move it into a form that is suitable for analytics, the Kimball approach does exactly that. It is fast, efficient, and well defined.

Kimball is answering the question – how can I get analytical data from my application expeditiously.

The Inmon approach addresses the believability of data across the corporation, not the speed of building analytics. The Inmon approach says that you take data from many applications that have been designed and built in many different forms. Each application designer built their own application in their own way. The problem is that there are a lot of people in the corporation that need to see the application with a corporate wide view of the data, not an application view. You cannot achieve a corporate understanding of data when that data is scattered across many applications.

The data needs to be transformed from the many applications into a singular corporate form. The transition is from application data to corporate data.

ETL came into this world to do exactly that.

To summarize – when you use the Kimball approach you get data fast and fit for analytics. When you use the Inmon approach you end up with corporate data.

Kimball – fast, analytical, application data

Inmon – slow, analytical, corporate data

You may be surprised to hear that work in the building of the Inmon approach never ends. The Inmon approach ends when the business stops changing. As long as the business is changing, you need to keep updating your corporate understanding of data. And businesses are constantly changing. The only businesses that don’t change are those that are dead.

Kimball and Inmon were answering different questions.

So, when you go to select your approach, you have to be clear about what you are asking for. If you want application data, use Kimball. If you want corporate data, use Inmon.

I have tried to depict the Kimball approach as honestly and as fairly as I know how.

Bill Inmon lives in Denver with his wife and his two Scotty dogs – Jeb and Lena. In the summertime Jeb and Lena only go for walks in the early morning. If they go past noon it is too hot for their paws. Sometimes it is so early that Jeb prefers to sleep.

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Zoltán Fekete

Senior Data Warehouse Engineer

8 个月

Do I understand correctly that according to Kimball, consolidation of corporate data is not a goal, or not so easy / feasible?

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Binoy Chidambaram

Product Lead| Financial Services & Insurance

8 个月

Thanks Bill Inmon for this simple and insightful explanation - If you want application data, use Kimball. If you want corporate data, use Inmon. And always know your usecase

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Haytham Mesalam

Digital Transformation | Data & AI Technology Enthusiast | Solutions Architecture | Demand Management

8 个月
Jyotsna Sharma

Enterprise Technology Strategist and Architect | Data and AI/ ML Leader | Business Transformation Enabler | Change Agent

8 个月

Inmon vs. Kimball has been a staple for discussions in the Data world for many years. Bill, (Bill Inmon) would love to hear your perspective on Data Vault Methodology.

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Scott Johnson

?? Transforming Data into Strategic Assets | GCP Certified | AI & Machine Learning Innovator | Master Data Management (MDM) Expert, Google Cloud Innovator, Informatica Partner

8 个月

I was lucky enough to share the podium presenting Informatica and Data Warehousing with Mr Bill Inmon in 2000 in Dearborn, Michigan. What a thrill and how inspirational and kind Mr Inmon is and has been all of these years. Thank you for your newest article here - we all continue to listen and read and learn from you sir!! What a reading pleasure!

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