A ROOKIE MISTAKE

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A ROOKIE MISTAKE

By W H Inmon

In sports a rookie mistake is an error made by someone who has just started playing the game. In football it may be an offside penalty. In baseball it may be a dropped fly ball. In basketball it may be a missed dunk shot.

There are all kinds of rookie mistakes. Once the player becomes a regular, he/she won’t make rookie mistakes again.

The other day I sat in on a data modelling exercise. The data modelers were building a data model for the purpose of creating an enterprise vision of where data and technology in the organization should go. I congratulated them on not just building a data model for the sake of building a data model. That was one rookie mistake that they did not make.

But then I looked at what they were doing. They were trying to include every element of data in the corporation into the data model built for the purpose of integration. At the rate they were going, they would be finished in the next millennium. They were NEVER going to finish the data model. They were trying to gather and incorporate EVERY scrap of data found in the corporation into the data model.

The problem was that they thought that is what an enterprise model should contain. Big rookie mistake! The truth of the matter is that the enterprise data model only should contain the data that needs to be integrated across the corporation, not every detail of data that exists.

By including only the data that needed to be integrated across the corporation, the model could be finished in a short, finite amount of time.

But how does the data modeler know what data needs to be included in the data model? The simple answer is you ask the end user what data do they need. An experienced end user analyst can tell you in a flash what is needed. It is as simple as that.

And what if you don’t include an element of data that was needed into the model? Simple answer – you go back and add the data element that is needed into the data model. ?Data models are not cast in concrete. Business circumstances change and the data model must change with the changes in business. Stated differently, data models that are effective are not cast in concrete.

Of course you should try to create the best data model that you can at the outset. But perfection is the enemy of progress (an old saying from General George Patton who was known for his data models and tanks). If you get the model 95% complete, that is probably good enough to allow you to proceed to the process of implementation.

Rookie mistakes!!!

Bill Inmon lives in Denver with his wife and his Scottie dog, Jeb. Jeb is in love with Penelope, a lhasa apso. But Jeb doesn’t get to see Penelope very often. So Jeb has a new girlfriend – Moochie, who is a poodle about Jeb’s size.?Ah, young love!

Danie Minnaar

Senior Data Architect at InfoBuild (Pty) Ltd. | Expert in Data Warehousing

2 年

Thank you, Bill, for yet another brilliant article! I like it that you don't just write articles to say something. ?? Your articles add so much value!

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Stefan Frimann Franck

CTO and Software Developer

2 年

I would love a saturday morning cartoon featuring General Patton and Jeb the Dog designing action packed data models. There would be no rookie mistakes but there absolutely would be lots of data related dad jokes and high fives.

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Larry Burns

Data and BI Architect at Fortune 500 Manufacturer

2 年

Bill, I totally agree. Here's a couple of approaches I've used to help avoid this "rookie mistake": One way is to start with a high-level Conceptual model that encompasses the most important aspects of an organization's Mission and Vision. In my last company, I created a Business Partner Conceptual model that showed, at a high level, our company's interactions with all of its business Stakeholders and consumers of our products and services. This helps identify the data entities that are most important to the company's most essential business processes. Another way is to organize the Enterprise data model around a value-generating initiative that is central to the organization's Mission. Again in my last company, we organized everything around a Digital Strategy initiative whose intent was to use cutting-edge technology to radically transform our company's ability to expand markets, develop new products and services, and manage relationships with our customers and other Stakeholders. Once we had identified the business processes we wanted to transform, we were able to identify the critical data assets needed to support those transformations and figure out what we needed to do to manage them most effectively.

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Sudhanshu Jha

Consultant - Enterprise Data Solutions

2 年

Thank you Bill for sharing realistic tips. ??

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Paul Ormonde-James

Business Intelligence and analysis specialist

2 年

Great to see Bill remains practical and focussed. Great work Bill

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