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Conformed dimensions are the linchpins of the dimensional model. Without conformance, the data warehouse fails to support cross-process analysis, misses out on possible synergies, and is characterized by islands of information. Conformance is, therefore, a central feature of the dimensional design, often documented in a matrix format. With a carefully planned infrastructure of conformed dimensions, it is possible to implement one star at a time without risking the incompatibilities that stand in the way of comparing processes.
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Earlier in this chapter, Figure 5-1 captured the notion of conformed dimensions through a table diagram. The key dimensions of the enterprise appeared as a column in the center of the diagram; they related to fact tables that appeared on either side. This diagram illustrates the central role played by the conformed dimensions in bringing together information from the various fact tables. While this kind of diagram is familiar to any developer of database applications, it is not particularly well suited to the task of documenting conformance. Even as presented, the diagram is difficult to read. Add column level detail, and it becomes impossible to fit on a page. Most implementations will involve a larger number of dimensions, and a much larger set of fact tables. The enterprise-level dimensional diagram quickly becomes a bewildering web of relationship lines. And this diagram fails to convey conformance relationships based on non-identical tables, such as the one between a base dimension and a conformed rollup. A better way to illustrate conformed dimensions is to use a matrix diagram. With columns representing the core conforming dimensions, and rows representing various processes or fact tables, conformance can be illustrated by placing checkmarks in the appropriate intersections. An example appears in Figure 5-7. A conformance matrix can convey the levels of conformance within a dimension by grouping the base dimension with conformed rollups. Figure 5-7 contains several examples. Columns for product and category, for example, are grouped together under the common
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order_facts shipment_facts return_facts inventory_facts receivables_facts sales_goal_facts demand_forecast_facts
Figure 5-7 A conformance matrix
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5 Conformed Dimensions 101
heading product. Similarly, day, month, and quarter are grouped under day; salesrep, territory, and region are grouped under salesrep. These shaded vertical bands represent sets of conformed dimensions; the locus of intersection with facts is indicated by checkmarks. Any two fact tables can be compared where checkmarks appear in the same column. All of the processes can be studied at the quarterly level, for example. All but demand forecast can be studied at the monthly level. Only the first five can be studied at the daily level. TIP Document dimensional conformance across fact tables or subject areas using matrix diagrams. Degenerate dimensions that will serve as the basis for drilling across should also appear on a conformance matrix. While they are not separate tables, these dimensions are a critical link between processes and are characterized by the same importance when it comes to support of cross-process analysis. An example appears in Figure 5-7, where order_line appears as a degenerate dimension. It can be used with orders, shipments, returns, and receivables. A conformance matrix of this sort need not include every dimension that is part of the data warehouse. Instead, it may depict only those that are crucial in linking together different processes or subject areas. The diagram in Figure 5-7, for example, does not include dimensions like shipper, return_reason, or account, which are presumably localized to individual fact tables. In larger implementations, a high-level conformance matrix may illustrate one set of dimensions, as just described. This high-level conformance matrix can be supplemented with additional subject area matrices, each of which includes a wider range of dimension tables. This combination of matrices represents the macro- and micro-levels of process relationships discussed at the beginning of this chapter. Of course, mapping out a set of conformed dimensions on a matrix does not guarantee it will be possible to implement them. Designing conformed dimensions requires a deep look at every process involved, a careful review of available source data, and a written set of requirements that describes how these dimensions map back to available source data. Development and documentation of the dimensional model will be discussed in detail in 18, How to Design and Document a Dimensional Model.
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