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Subject area focus Dimensional or normalized format
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Figure 2-3 A stand-alone data mart
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what is tracked in the accounting system. Instead, the implementation takes a direct route from subject area requirements to implementation. Because results are rapid and less expensive, stand-alone data marts find their way into many organizations. They are not always built from scratch. A stand-alone data mart may become part of the application portfolio when purchased as a packaged application, which provides a prebuilt solution in a subject area. Packaged data marts may also be available as add-ons to packaged operational applications. Prebuilt solutions like these can further increase the savings in time and cost. Even in organizations committed to an enterprise data warehouse architecture, standalone data marts can be found. Sometimes, they are present as legacy systems, in place before the commitment to the enterprise architecture. In other cases, they may be built within user organizations, entirely outside the domain of the IT department. Mergers and acquisitions can bring with them new analytic data stores that have not been integrated into the preexisting architecture. For all these reasons, the stand-alone data mart is a reality for many businesses and organizations. Yet it is almost universally maligned. While often considered a short-term success, the stand-alone data mart frequently becomes a long-term headache. To understand why, it helps to look at what happens when more than one subject area is supported via stand-alone data marts. Figure 2-4 depicts the proliferation of stand-alone data marts across multiple subject areas. While a single stand-alone data mart may appear to be the most efficient path to results, the presence of multiple data marts exposes inefficiencies. In Figure 2-4, multiple ETL processes are loading data from the same source systems. The data marts themselves may be based on different technologies, and the user audiences may be relying on separate query and reporting infrastructures. These characteristics often earn stand-alone data marts the label stovepipe, meant to connote a lack of compatibility. They compound the cost of the total solution, requiring the maintenance of redundant technologies, processes, and skill sets. Even when these technical inefficiencies are minimized, a more serious deficiency may be lurking in the data itself. If each data mart is built to address a narrow set of needs, what happens when these needs expand Lacking a repository for granular data, a data mart may fail to answer a future question that requires more detail than originally anticipated. Similarly, consider what happens when someone wants to compare information from two
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Figure 2-4
Multiple stand-alone data marts
Part I
PART I Fundamentals
or more data marts. If these subject areas do not share consistent definitions of common entities (such as products, departments, or customers), then it may be impossible to compare the information. Worst of all, redundant load processes may apply different rules to source data, leading to systems that provide contradictory results. These issues cause stand-alone data marts to become islands of information. Developed to satisfy a narrow set of needs, they fail to support cross-functional analysis. Extensive rework may be required to adapt them to a deeper or wider set of demands. Short-term savings give way to long-term costs. These deficiencies should not necessarily preclude the implementation of a stand-alone data mart. As long as there is a shared understanding of the potential future cost, a subject area focus may make sense. It keeps costs low and minimizes activities that precede the delivery of some initial capability. Too often, though, the easy route is taken without buy-in from all parts of the business. Stand-alone data marts often employ dimensional design. This is so common, in fact, that the shortcomings of stand-alone data marts are sometimes blamed on the star schema. It has become a common misconception that the star schema is for aggregated data, or that the use of the star schema leads to stovepipes. By now it should be clear that these failures are not the result of the use of dimensional design. Stand-alone data marts may contain aggregated data, and they are likely to exhibit incompatibilities with one another, but this is not a failure of the star schema. Rather, it is a shortcoming of the narrow scope of the stand-alone data mart.
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