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At best, the response is frustration over a missed opportunity. At worst, a general distrust of the analytic infrastructure develops. As you will see later in this chapter, dimensions can conform in a variety of ways. While conformance may be conveyed by a diagram like the one in Figure 5-1, such pictures quickly become difficult to lay out and understand. The crucial concept of conformance is often better depicted through alternate means. As the key to long-term success, conforming dimensions are crucial in any data warehouse architecture that includes a dimensional component. Before spelling out the requirements for conformance and their implications, let s take a closer look at how they support, or fail to support, drilling across. Understanding how and why this process breaks down sheds important light on the concept of dimensional conformance.
Dimensions and Drilling Across
Dimensions are the key enablers of the drill-across activity that brings together information from different processes. Drill-across failure occurs when dimensions differ in their structure or content, extinguishing the possibility of cross-process synergy. Dimension tables need not be identical to support drilling across. When the attributes of one are a subset of another, drilling across may also be possible.
Part II
PART II
Multiple Stars
What Causes Failure
Dimensions and their content are central to the process of comparing fact tables. In the first phase of drilling across, dimensions are used to define a common level of aggregation for the facts from each fact table queried. In the second phase, their values are used to merge results of these queries. Dimensional incompatibilities can disrupt this process. The stars in Figure 5-2 are rife with examples. The stars in Figure 5-2 describe two processes: orders and returns. Each has been implemented by a separate department and resides in a separate database. Individually, these stars permit valuable analysis of the processes they represent. Both include dimension tables representing day, customer, and product. Given these commonalities, it is reasonable to expect these stars should permit comparison of these processes. For example, one might ask to see returns as a percentage of orders by product during a particular period. The two drill-across phases, as introduced in 4, would unfold as follows: 1. A query is issued for each fact table, aggregating the respective facts (quantity ordered and quantity returned) by product. 2. These intermediate result sets are merged based on the common product names, and the ratio of quantity ordered to the quantity returned is computed.
DAY SALESREP CUSTOMER PRODUCT DAY SALESREP CUSTOMER PRODUCT
ORDER_FACTS
day_key salesrep_key customer_key product_key quantity_ordered
RETURN_FACTS
day_key salesrep_key customer_key product_key quantity_returned
PRODUCT (Orders Star) product _key 1110 1135 1233 1311 1400 1578 SKU 1111-01 2222-01 3333-01 4444-22 5555-22 6666-22 product 5 7 bubble mailer 8 10 bubble mailer 9 12 bubble mailer Box, Type A Box, Type B Box, Type C category Mailers Mailers Mailers Boxes Boxes Boxes
PRODUCT (Returns Star) product _key SKU 1110 1135 1233 1311 1388 1422 1111-01 2222-01 3333-01 4444-22 4444-22 5555-22
prod_name
prod_cat
type PAPER PAPER PAPER BOARD BOARD BOARD
5 7 BUBBLE MAILERS MAILER 8 10 BUBBLE MAILERS MAILER STANDARD MAILERS MAILER BOX BOX, TYPE A BOX, TYPE B BOXES BOXES BOXES
Figure 5-2 Product dimensions that do not conform
5 Conformed Dimensions 89
A similar process might be followed to drill across various other dimension attributes such as product type or category, or across dimension attributes from the day, customer, or salesperson tables. Unfortunately, several factors prevent these stars from supporting this activity, at least when it comes to products. The problems lie in the respective product tables. Differences in their structure and content get in the way of comparing orders and returns.
Differences in Dimension Structure
The product dimension table in the orders star contains a type dimension; the one in the returns star does not. It may be difficult or impossible to compare orders to returns based on product type depending on other characteristics of the tables. Columns that appear to be the same thing are named differently in the two stars. For example, the column that contains the name of the product is called product in the orders star, and prod_name in the returns star. A similar situation exists for the columns that contain category descriptions. These differences may stand in the way of drill-across operations as well. It can be tempting to dismiss these differences since a skilled developer might be able to work around them. Although product type is not present in the orders star, a developer might be able to match each product from the orders star to a product type in the returns star. The SKU, which is the natural key, might be used to support this lookup process. After these equivalences are identified, orders could be aggregated by type and compared to returns. Similarly, a developer could work around the differences in column names to compare orders and returns by category. Applying his or her knowledge of column equivalencies, the developer groups orders by category, and groups returns by prod_cat. When joining these intermediate result sets, the developer would match the category from the orders query with prod_cat from the returns query. These workarounds are further examples of what 4 referred to as boiling the frog. They range from simple to complex, but each compensates for design-level shortcomings by complicating the reporting process. These kinds of workarounds have many drawbacks: Specific knowledge is required to drill across. It may not be possible for anyone but the most skilled developers to use workarounds to compare the processes. Workarounds risk inconsistent and inaccurate results when applied incorrectly. Workarounds stand in the way of the automated generation of drill-across reports for ad hoc reporting tools. Not every structural incompatibility can be overcome by a workaround. If the two stars have different definitions of a product, there may be deeper difficulties. This might occur if
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