We use cookies and other similar technologies (Cookies) to enhance your experience and to provide you with relevant content and ads. By using our website, you are agreeing to the use of Cookies. You can change your settings at any time. Cookie Policy.


The Beautiful Data Model

Originally published October 6, 2011

Behind door 1 there is this gorgeous data model; behind door 2 there is another beautiful data model; and behind door three there is a cute and flirty data model. Which data model is the one you pick?

The “beauty” of a data model is a subjective thing. So who is better looking – Miley Cyrus, Heather Locklear, or Sophia Loren? It all boils down to taste. If you want young and fresh, you probably would pick Miley Cyrus. If you want worldly and experienced, you would probably pick Sophia Loren. However you slice it, all three women are attractive under anyone’s definition, but which one is the most attractive is strictly a matter of taste and style.

It is the same with data models. Is there ever anything such as the “right” data model? Is there ever a case where one data model is better than another data model? Does subjectivity ever turn into objectivity when it comes to a beauty contest for data models?

The answer is yes – there are some objective criteria by which to judge data models.  It turns out that there are different kinds of data models.
First there is the classical operational data model. The classical operational data model is based on such things as current operating procedures, up-to-the-second accuracy of data, update of records, the capture of data as a result of transactions being executed, and so forth.  The operational data model looks at data at the clerical level and tracks data based on the day-to-day business of the organization.

Then there is the data warehouse data model. The data warehouse data model is one that looks at data abstractly and historically. In addition, the data warehouse data model requires the data inside the data model to be integrated. As data passes into the domain that is governed by the data warehouse data model, the detailed data is transformed into a single integrated format and structure. In some cases, this integration is simple and straightforward. In other cases, this transformation is elaborate and complex.

There are many other kinds of data models. There are data models for compliance, for governance and for the archiving of data. There are data models for the finances of an organization. In fact, there are data models for about every kind of activity and every kind of usage of data that is found in the corporation.

Which is the “best” or the “most beautiful” data model? The answer is that it depends on the context of the data model. Trying to use an operational data model for a data warehouse is a mistake that has been made many times. And vice versa – trying to use a data warehouse data model for the shaping of operational systems is also a mistake.

So – unlike a beauty contest where a subjective opinion is everything – there are some broad guidelines that are objective when it comes to judging a data model. The broader context of the data model must be taken into account when judging the appropriateness of a data model.

  • Bill InmonBill Inmon

    Bill is universally recognized as the father of the data warehouse. He has more than 36 years of database technology management experience and data warehouse design expertise. He has published more than 40 books and 1,000 articles on data warehousing and data management, and his books have been translated into nine languages. He is known globally for his data warehouse development seminars and has been a keynote speaker for many major computing associations.

    Editor's Note: More articles, resources and events are available in Bill's BeyeNETWORK Expert Channel. Be sure to visit today!

Recent articles by Bill Inmon

 

Comments

Want to post a comment? Login or become a member today!

Be the first to comment!