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Data Warehousing and Organizational Size

Originally published May 5, 2011

“A data warehouse is made up of all the data marts in an enterprise.” – Ralph Kimball

“A data warehouse is a subject oriented, integrated, non volatile, time variant collection of data in support of  management’s decisions.” – W. H. (Bill) Inmon

Does the size of an organization make a difference as to whether an organization adopts a Kimball approach to a data warehouse or an Inmon approach? The answer is absolutely yes. The size of the organization makes a big difference as to whose approach to data warehousing an organization finds most attractive.

As a rule, smaller organizations find the Kimball approach most attractive and larger organizations find the Inmon approach to be more attractive. There are several reasons for these affinities.

There is no doubt that a Kimball architecture can be built faster, cheaper, and with les complexity than an Inmon architecture. An Inmon architecture requires more time, more resources, and struggling with more complexity than a Kimball architecture. From the perspective of resources to build an Inmon architecture, smaller organizations automatically find the Kimball approach to be more attractive.

But the end result of an Inmon architecture – when built properly – is the creation of the “single version of the truth.” Once an organization has a single version of the truth, corporate reporting is open to the exploration of huge new vistas.

The end result of the Kimball architecture is a bunch of data marts where there is no “single version of the truth.” Instead there are many versions of the truth, with each data mart proclaiming itself as the “real” truth. An end user simply does not know which way to turn in order to find the real, accurate corporate data. Even when Kimball creates a conformed dimension, there are major elements of data for which there is no single version of the truth. Conformed dimensions only address a fraction of the data in a corporation.

But a Kimball architecture is fast and relatively easy to build.

A Single Version of the Truth

So what use is a “single version of the truth”? What sort of organizations find a “single version of the truth” to be attractive? The answer is that the larger an organization is, the more attractive the “single version of the truth” becomes. Large organizations have lots of people, lots of data, lots of ways of looking at the data, and lots of decisions to make. And in a large organization, the lack of integration of data is a large obstacle to proper and effective decision making. So large organizations find that they really need a single version of the truth in order to make decisions effectively.

But with smaller organizations it is less likely that there is a need for corporate integration of data. Consider an entrepreneur who runs his/her business on a single personal computer. Is a data warehouse attractive to a single individual? A data warehouse is attractive to a single individual because of the need to keep information in an organized fashion and to manage data historically (even individuals have a need to look at data historically). But does an individual entrepreneur need to integrate data? Hardly. There is no need for integration of data when one person is running the business. The single individual does not need anyone to sort out the “single version of the truth” for the individual.

So data warehousing is attractive to organizations of different sizes for very different reasons. The larger the organization is, the more an organization needs a single version of the truth. And consequently, the larger an organization is, the more attractive the Inmon architecture becomes. Conversely, the smaller an organization is, the more attractive a Kimball style architecture becomes. Smaller organizations simply do not have much of a need for determining the “single version of the truth.”  

SOURCE: Data Warehousing and Organizational Size

  • 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!

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