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Data Warehouses and Data Marts: A Dynamic View file:///E|/FrontPage Webs/Content/EISWEB/DWDMDV.html

Data Warehouses and Data Marts: A Dynamic View

By

Joseph M. Firestone, Ph.D.

White Paper No. Three

March 27, 1997

Patterns of Data Mart Development

In the beginning, there were only the islands of information: the operational data stores and

legacy systems that needed enterprise-wide integration; and the data warehouse: the solution to

the problem of integration of diverse and often redundant corporate information assets. Data marts were not a part of the vision. Soon though, it was clear that the vision was too sweeping.

It is too difficult, too costly, too impolitic, and requires too long a development period, for many organizations to directly implement a data warehouse.

A data mart, on the other hand, is a decision support system incorporating a subset of the enterprise’s data focused on specific functions or actvities of the enterprise. Data marts have specific business-related purposes such as measuring the impact of marketing promotions, or measuring and forecasting sales performance, or measuring the impact of new product introductions on company profits, or measuring and forecasting the performance of a new company division. Data Marts are specific business-related software applications.

Data marts may incorporate substantial data, even hundreds of gigabytes, but they contain much less data than would a data warehouse developed for the same company. Also since data marts are focused on relatively specific business purposes, system planning and requirements analysis are much more manageable processes, and consequently design, implementation, testing and installation are all much less costly than for data warehouses.

In brief, data marts can be delivered in a matter of months, and for hundreds of thousands, rather than millions of dollars. That defines them as within the range of divisional or departmental budgets, rather than as projects needing enterprise level funding. And that brings up politics or project justification. Data marts are easier to get through politically for at least three reasons. First, because they cost less, and often don’t require digging into organization-level budgets, they are less likely to lead to interdepartmental conflicts. Second, because they are completed quickly, they can quickly produce models of success and corporate

–  –  –

constituencies that will look favorably on data mart applications in general. Third, because they perform specific functions for a division or department that are part of that unit’s generally recognized corporate or organizational responsibility, political justification of a data mart is relatively clean. After all, it is self-evident that managers should have the best decision support they can get provided costs are affordable for their business unit, and the technology appears up to the job. Perhaps for the first time in computing history those conditions may exist for DSS applications.

So, data marts have become a popular alternative to data warehouses. As this alternative has gained in popularity, however, at least three different patterns or informal models of data mart development have appeared. The first response to the call for data mart development was the view that data marts are best characterized as subsets (often somewhat or highly aggregated) of the data warehouse, sited on relatively inexpensive computing platforms that are closer to the user, and are periodically updated from the central data warehouse. In this view, the data warehouse is the parent of the data mart.

The second pattern of development denies the data warehouse its place of primacy and sees the data mart as independently derived from the islands of information that predate both data warehouses and data marts. The data mart uses data warehousing techniques of organization and tools. The data mart is structurally a data warehouse. It is just a smaller data warehouse with a specific business function. Moreover, its relation to the data warehouse turns the first pattern of development on its head. Here multiple data marts are parents to the data warehouse, which evolves from them organically.

The third pattern of development attempts to synthesize and remove the conflict inherent in the first two. Here data marts are seen as developing in parallel with the data warehouse. Both develop from islands of information, but data marts don’t have to wait for the data warehouse to be implemented. It is enough that each data mart is guided by the enterprise data model developed for the data warehouse, and is developed in a manner consistent with this data model. Then the data marts can be finished quickly, and can be modified later when the enterprise data warehouse is finished.

These three patterns of data mart development have in common a viewpoint that does not explicitly consider the role of user feedback in the development process. Each view assumes that the relationship between data warehouses and data marts is relatively static. The data mart is a subset of the data warehouse, or the data warehouse is an outgrowth of the data marts, or there is parallel development, with the data marts guided by the data warehouse data model, and ultimately superseded by the data warehouse, which provides a final answer to the islands of information problem. Whatever view is taken, the role of users in the dynamics of data warehouse/data mart relationship is not considered. These dynamics are the main subject of this white paper.





To develop this subject the original three models are first developed in a little more detail. This development is followed with a presentation of three alternative models that consider the role of

–  –  –

feedback from users in the development of data warehouses and data marts. Lastly, an analysis of the usefulness of the six patterns of development is given in light of a particular viewpoint on organizational reality.

Development Models Without Explicit User Feedback

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The top down model is given graphically in Figure One. The data warehouse is developed from the islands of information through application of the extraction, transformation and transportation (ETT) process. The data warehouse integrates all data in a common format and a common software environment. In theory all of an organization’s data resources are consolidated in the data warehouse construct. All data necessary for decision support are resident in the data warehouse. After the data warehouse is implemented, there is no further need for consolidation. It only remains to distribute the data to information consumers and to present it so that it does constitute information for them.

The role of the data marts is to present convenient subsets of the data warehouse to consumers having specific functional needs, to help with structuring of the data so that it becomes information, and to provide an interface to front-end reporting and analysis tools that, in turn,

–  –  –

can provide the business intelligence that is the precursor to information. The relation of the data marts to the data warehouse is strictly one-way. The data marts are derived from the data warehouse. What they contain is limited to what the data warehouse contains. The need for information they fulfill is limited to what the data warehouse can fulfill. The data warehouse therefore is required to contain all the data that the enterprise or any part of it might need to perform decision support. And if users discover any need the data warehouse does not meet, the only way to fix the situation is for the users to get the enterprise level managers of the data warehouse to change the warehouse structure and to add or modify the data warehouse as necessary to meet user needs. The model contains no description or explanation of this process of recognition and fulfillment of changing user needs or requirements. But it is inconsistent with the model to assume that data marts would serve as a means of fulfilling changing user needs without changes to the data warehouse occurring first.

The Bottom Up Model

Figure Two depicts the The bottom-up pattern of development. In the left-hand portion of Figure Two, data marts are constructed from pre-existing islands of information, and the data warehouse from the data marts. In this model the data marts are independently designed and implemented, and therefore unrelated to one another, at least by design. Growth of this kind is likely to contain both redundancy and important information gaps from an enterprise point of view.

–  –  –

While each data mart achieves an integration of islands of integration in the service of the data mart’s function, the integration exists only from the narrow point of view of the business function sponsoring the data mart. From the enterprise point of view, new legacy systems are created by such a process, and these constitute new islands of information. The only progress made is that the new islands employ updated technology. But they are no more integrated and coherent than the old islands were; and they are no more capable of supporting enterprisewide functions.

The right-hand side of Figure Two shows the data mart islands of information being used as the foundation of an integrated data warehouse. A second ETT process supports this integration. It will be needed to remove the redundancy in the data marts, to identify the gaps the process of isolative data mart creation will leave, and to integrate the old islands of information into the new data warehouse in order to fulfill these gaps. The possibility of using older islands of information in this way is not envisioned in this model, which tacitly and incorrectly assumes that the flow from data marts to data warehouse will be adequate to produce a data warehouse with comprehensive coverage of enterprise data needs.

The second model is vague on what happens after the data warehouse is built. Will the data warehouse suddenly become the parent of the data marts, and development proceed according to the top-down pattern? Or will the data warehouse continue to be the "child" of the data marts, which will continue to evolve and lead periodically to an adjustment in the enterprise data warehouse to make it consistent with the changed data marts? The second model doesn’t answer such questions, but instead ends its story with the creation of the data warehouse.

Parallel Development

The most popular pattern of development of the first three is the parallel development model.

The parallel model sees the independence of the data marts as limited in two ways. First, the data marts must be guided during their development by a data warehouse data model expressing the enterprise point of view. This same data model will be used as the foundation for continuing development of the data warehouse, ensuring that the data marts and the data warehouse will be commensurable, and that information gaps and redundancies will be planned and cataloged as data mart construction goes forward. Data marts will have a good bit of independence during this process. In fact, as data marts evolve, lessons may be learned that will lead to changes in the enterprise data warehouse model. Changes that may benefit other data marts being created, as well as the data warehouse itself.

–  –  –

Second, the independence of data marts is treated as a necessary and temporary expedient on the road to construction of a data warehouse. Once the goal is achieved, the warehouse will supersede the data marts, which will become true subsets of the fully integrated warehouse.

From that point on, the data warehouse will feed established data marts, create subsets for new data marts, and, in general determine the course of data mart creation and evolution.

The third pattern begins to treat some of the complexities of the relationship between the data warehouse and data marts. Unlike the first pattern, it recognizes that organizational departments and divisions need decision support in the short-term and will not wait for data warehouse development projects to bear fruit. Thus data marts are necessary and desirable applications for organizations to pursue. Also unlike the first pattern, it sees the data marts as contributing to the data warehouse through evolution in the enterprise data model stimulated by the data marts.



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