Different stages of data warehouse maturity

This article covers the stages of data warehousing from the data mart through the enterprise data warehouse.

This article originally appeared on the BeyeNETWORK.

Looking back across the data warehouse industry for the past decade and having seen many different organizations and their data warehousing efforts, it becomes apparent that there are different stages of data warehousing.

The first stage of data warehousing is building a data mart. Here, the user finds that analytical processing can be done. The results are attractive and the proof that analytical processing can be used effectively in the decision making process is verified by this step.

Soon, the organization clamors for more analytical data. Data marts start to pop up everywhere, and each department has its own analytical processing. At first, the organization is happy with the results of having many data marts. But it dawns on the organization that there is no “single version of the truth.” There is unreconciled and conflicting data everywhere. The organization soon discovers that several data marts are not the same thing as a data warehouse.

The next thing to appear is an application data warehouse. An application data warehouse is not the same thing as a data mart. The data inside the application data warehouse is integrated and granular. Data inside an application data warehouse is built with the next iteration of development in mind. Data is structured with the full knowledge that more data and different types of data are going to be added.

The application data warehouse is restricted to the type of data that can be placed in it. In many ways, the application data warehouse is the first step toward a larger data warehouse. The application data warehouse sets the stage for future growth.

After the application data warehouse comes the functional data warehouse. The functional data warehouse is just an application data warehouse that has been extended to encompass multiple applications in the same functional area. An application data warehouse may be built for sales, marketing, finance, etc. One feature of both the application data warehouse and the functional data warehouse is that the data warehouse sets the stage for future expansion outside the application and/or the function. 

The next type of data warehouse is the Phase 1 enterprise data warehouse. The Phase 1 enterprise data warehouse is a data warehouse for the entire enterprise. The enterprise data warehouse is built in phases. It is often difficult to tell a Phase 1 enterprise data warehouse from an application data warehouse, as they have many common characteristics.

The ultimate data warehouse is a Phase N enterprise data warehouse. The Phase N enterprise data warehouse is the result of many different development efforts, each of which adds to the enterprise data warehouse. The Phase N enterprise data warehouse is a large, detailed, multifaceted data warehouse. 

One of the interesting aspects of this panoply of data warehouses types is that there is no need to start with a data mart and move upward. Indeed, in most cases it is wasteful to start with a data mart. Depending on the information needs of the organization, it usually makes sense to start with an application data warehouse or a Phase N enterprise data warehouse. 

It rarely makes sense to take a big-bang approach to building an enterprise data warehouse. Trying to build an enterprise data warehouse all at once has a terrible track record of success and is for the foolhardy. The proper way to build an enterprise data warehouse is to build it in iterations.

Looking back across the data warehouse industry for the past decade and having seen many different organizations and their data warehousing efforts, it becomes apparent that there are different stages of data warehousing.

The first stage of data warehousing is building a data mart. Here, the user finds that analytical processing can be done. The results are attractive and the proof that analytical processing can be used effectively in the decision making process is verified by this step.

Soon, the organization clamors for more analytical data. Data marts start to pop up everywhere, and each department has its own analytical processing. At first, the organization is happy with the results of having many data marts. But it dawns on the organization that there is no “single version of the truth.” There is unreconciled and conflicting data everywhere. The organization soon discovers that several data marts are not the same thing as a data warehouse.

The next thing to appear is an application data warehouse. An application data warehouse is not the same thing as a data mart. The data inside the application data warehouse is integrated and granular. Data inside an application data warehouse is built with the next iteration of development in mind. Data is structured with the full knowledge that more data and different types of data are going to be added.

The application data warehouse is restricted to the type of data that can be placed in it. In many ways, the application data warehouse is the first step toward a larger data warehouse. The application data warehouse sets the stage for future growth.

After the application data warehouse comes the functional data warehouse. The functional data warehouse is just an application data warehouse that has been extended to encompass multiple applications in the same functional area. An application data warehouse may be built for sales, marketing, finance, etc. One feature of both the application data warehouse and the functional data warehouse is that the data warehouse sets the stage for future expansion outside the application and/or the function. 

The next type of data warehouse is the Phase 1 enterprise data warehouse. The Phase 1 enterprise data warehouse is a data warehouse for the entire enterprise. The enterprise data warehouse is built in phases. It is often difficult to tell a Phase 1 enterprise data warehouse from an application data warehouse, as they have many common characteristics.

The ultimate data warehouse is a Phase N enterprise data warehouse. The Phase N enterprise data warehouse is the result of many different development efforts, each of which adds to the enterprise data warehouse. The Phase N enterprise data warehouse is a large, detailed, multifaceted data warehouse. 

One of the interesting aspects of this panoply of data warehouses types is that there is no need to start with a data mart and move upward. Indeed, in most cases it is wasteful to start with a data mart. Depending on the information needs of the organization, it usually makes sense to start with an application data warehouse or a Phase N enterprise data warehouse. 

It rarely makes sense to take a big-bang approach to building an enterprise data warehouse. Trying to build an enterprise data warehouse all at once has a terrible track record of success and is for the foolhardy. The proper way to build an enterprise data warehouse is to build it in iterations.

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. Bill can be reached at 303-681-6772.

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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