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sum(order_dollars) / sum(goal_dollars) as percent_of_goal
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This simplified SQL is much easier to write and understand, reducing the skill level required to perform process comparisons. This may be a nice to have feature, but there are other factors that may make the merged fact table a must have.
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In addition to simplifying queries, a merged fact table can greatly improve performance. In the case of the goal versus actual comparison for the orders process, the benefit may be minimal. Drilling across two data sets can be completed with great efficiency, particularly when a small number of shared dimensions are in the result set, and those dimensions take on a relatively small number of distinct values. The performance advantage of a merged fact table is more pronounced when data sets are larger, or when comparing a large number of processes. In 4, for example, a sales report was described that pulled together data from four fact tables. Sales calls, proposals, orders, and revenue were combined into a single report, organized by quarter and region, as shown in Figure 4-13. In this case, performance may be significantly improved by a merged fact table.
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Profitability analysis is another common example where a merged fact table can provide significant performance improvements. Studying profitability requires the comparison of revenue and costs, which are generated by separate processes represented by separate stars. A manufacturer of consumer products, for example, may have stars that capture sales, shipments, returns, commission payments, marketing promotions, manufacturing costs, and other overhead activities. Profitability analysis brings these disparate processes together over a set of common dimensions such as time or product. Bringing together more components can create a finer picture of profitability but also impacts performance. A merged fact table for profitability analysis removes this bottleneck.
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Many packaged query and reporting tools can be configured to recognize situations that require drilling across and then automate the process. Using some tools, it is also possible to assemble a drill-across report manually. Three methods of drilling across were illustrated in Figure 4-14. As noted in 4, some business intelligence tools cannot be used in any of these manners. Others present configuration challenges that prevent such use. When the data warehouse architecture does not include tools that can be used to drill across, the merged fact table becomes an essential part of the dimensional design. Merged fact tables must be designed and implemented to support all cross-process reporting. The drill-across activity is performed during the ETL process; the resulting merged fact tables support cross-process analysis without requiring the use of multiple stars. TIP A merged fact table consolidates facts from multiple fact tables by precomputing the drill-across activity. This makes cross-process analysis queries easier to write and can improve performance. If the available reporting tools do not support drilling across, a merged fact table will enable comparisons that would otherwise be impossible.
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The drill-across fact table should not be thought of as a replacement for the first-level stars on which it is based. When studying a single process, you should direct queries to the appropriate process-specific star not the merged star. There are two reasons for this guideline. The most obvious reason the process-specific star is still required is that the merged star does not always contain the most granular data possible. In the case of orders, for example, the merged goal_vs_peformance star does not include product or customer detail. It also lacks day and salesperson-level detail, summarizing them by month and territory. When you are studying orders, use of the original order_facts star will allow access to the most granular detail possible. There is another reason not to use the merged fact table when studying a single process. Since the merged fact table contains information from more than one process, it may record zero-valued facts. For example, suppose there is a sales goal for the Eastern territory for the month of March but no orders. The merged fact table goal_vs_performance_facts will contain a row for the Eastern territory for March. The order_dollars for this particular row will contain the value 0. If this table was used to produce a report showing orders by territory, the Eastern territory would be included in the result set despite the fact that there are no orders. This is akin to the problem of zero-valued rows resulting from the recording of orders and shipments in the same fact table, as discussed in 4. As in that example, the proper solution for single-process analysis is the use of a process-specific fact table.
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