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The conformance matrix serves as a blueprint for implementation. It shows where all the fact tables connect to one another. This allows each fact table to be implemented individually, with the confidence that, as each is brought online, it will work together with those that came before it. With a dimensional framework in place, incremental implementation can proceed without fear of incompatibilities. Figure 5-8 illustrates a series of implementation projects, which together represent numerous enterprise processes. If these projects are implemented without a dimensional framework, the milestones labeled T2 and T3 will probably fail to deliver cross-process capability. Incompatible dimensions will get in the way of drilling across. Equally undesirable, there may be redundant processes loading similar dimensions in each subject area. The final result will be disappointing in terms of capability, while consuming undue amounts of IT resources. If, on the other hand, this incremental implementation is preceded by the development of a set of conformed dimensions, this framework will avoid the pitfalls of nonconformance.
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Receivables H.R.
T1 First Production Availability
T2 Sales Subject Area Complete Analyze activities, orders, shipments (Micro-level cross-process capability)
T3 Profitability analysis (Macro-level cross-process capability)
Figure 5-8 Incremental implementation
In addition to achieving analytic synergies at T2 and T3, the infrastructure may be streamlined. The day dimension, for example, need only be developed once, during the first project. In fact, the later projects may require the construction of far fewer dimension tables than the earlier projects. Of course, this requires some upfront analysis, a luxury that is not always afforded.
Architecture and Conformance
The relative emphasis on conformance varies with the data warehouse architecture. Because it is founded on the dimensional model, conformance is a central feature of Kimball s dimensional data warehouse. Inmon s Corporate Information Factory does not rely on the dimensional model to integrate enterprise data and therefore places a reduced emphasis on conformance. The stand-alone data mart does not have an enterprise context by definition. While it may include dimensions that conform internally, it is likely to exhibit incompatibilities with other data marts. This section sorts out these various approaches to dimensional conformance.
Dimensional Data Warehouse
The dimensional data warehouse architecture, as described in 2, Data Warehouse Architectures, relies on the star schema as an integrated repository of atomic data, drawn
5 Conformed Dimensions 103
from all parts of the enterprise. Data marts are either subsets of this dimensional repository, dimensional structures derived from it, or some combination of the two. Conformed dimensions are the key to enterprise scope, serving as the infrastructure that integrates subject areas. This means that the dimensional design, including a conformance plan, must be conducted as a strategic, upfront process.
Strategic Planning Includes Conformance Design
In a dimensional data warehouse, dimensional design is a strategic activity, rather than a design-stage activity. It is conducted upfront, before any implementation projects begin. The dimensional design may be developed as a stand-alone project, or it may be incorporated into a strategy project, which also includes activities to establish technical architecture, select tools, and set implementation priorities. The conformance framework of the dimensional model is a top-level focus of dimensional data warehouse design. Kimball and Ross refer to the conformance framework as the conformance bus. It allows the model to meet the needs of each individual subject area, while also preserving the ability to compare subject areas. This makes it the key to supporting enterprise scope, allowing process comparison at the micro- and macro-level. The development and documentation of the dimensional design are fully explored in 18. Documentation will include enterprise-level conformance matrices that map the key conformed dimensions to individual subject areas and to individual fact tables. An example of the latter appears in Figure 5-7. Base dimensions and their derived rollups are also clearly highlighted, using illustrations similar to the one in Figure 5-5. Because the dimensional model will serve as an integrated repository for atomic data, the design process must also include tasks that identify the source system and processing rules for each conformed dimension. This ensures that integration of data from disparate source systems is feasible. Without this step, the dimensional design is likely to represent wishful thinking; implementation projects will discover that the model does not mesh with how information is gathered and that the actual data stores cannot support the dimensions as designed. During upfront design, all available data sources for each conformed dimension must be identified. Processing rules must be developed for the consolidation of this information into the set of attributes that make up the dimensional model. This nontrivial task is one of the largest of the design process, as it must reconcile conflicting views of key entities and take into account the quality of source data. The identification of source data is discussed further in 18. TIP In a dimensional data warehouse, dimensional design is a strategic activity. Conformed dimensions are a central feature of the design, providing enterprise capability. Once the dimensional design and conformance framework are complete, implementation projects can begin in earnest. Each implementation project builds around the conformance framework established during the upfront planning process. As each subject area is brought online, it will interlock with previously implemented components through the conformed dimensions. Implementations may also take place in parallel, as shown in Figure 5-8. This approach allows the dimensional data warehouse to deliver on the synergies described at the start of this chapter. Each successive implementation empowers insight into a new business process and also allows that process to be compared with those that
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