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A very powerful analytic technique uses past behavior patterns to make sense of current behavior. Consider this question: Are customers who generate over $1 million in orders receiving better discounts than those who generate $500,000 or less This question uses a fact, order dollars, as a dimension, providing a context for the study of discount dollars. Answering this kind of question requires advanced query development and intense processing. A behavioral question is one that groups or filters facts based on the past behavior of members of a dimension. Behavioral dimensions transform facts into dimensions, enabling powerful analytics without complex queries or intensive processing.
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Converting Facts to Dimensions at Query Time
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Answering a question that groups or filters facts based on past behavior goes beyond the capabilities of the basic SELECT/GROUP BY query introduced in 1. To answer these questions, two major steps are required: 1. Identify past behavior for each member of the dimension in question. 2. Use this information as part of a query studying current behavior. For the preceding question regarding discounts, the first step requires identifying the past order behavior for each customer, classifying them based on order volume. The second step uses this new information about customers in a query that aggregates the fact discount dollars. These steps may be put together programmatically, perhaps within the environs of a reporting tool. They can also be combined through the construction of a correlated subquery, in which case the first step is subordinated to the second. Because of the processing required, queries of this nature do not perform well. To cope, behavioral reports are often scheduled to run during batch windows, so that the report is cached and ready for viewing when the user requests it. This interrupts the normal cycle of question, followed by answer, followed by new question. If each new question requires execution of a behavioral query during a batch window, the cycle may stretch out over days. It should also be noted that an end user cannot be expected to put together a behavioral report. Whether this is done using procedural processing or by using a correlated subquery, the necessary technical skills go beyond even those of junior systems analysts. As a result, requests for these powerful reports often populate the IT backlog, and the data warehouse does not live up to its fullest potential. TIP Using past behavior to study facts requires the construction of new dimensions from old facts. If done at report time, this process is slow and requires the expertise of a developer. The solution to this problem extends existing dimension tables to include behavioral data. The additional ETL processing required pays significant dividends.
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Designing and Using Behavioral Dimensions
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The ability to use past behavior as the context for facts can be supported without a negative impact on query performance. This is achieved by adding columns to dimension tables that track past behavior. These behavioral dimensions shift the processing burden away from query and report development, and move it to the ETL process. Past behavior can be incorporated into a dimension table in three ways, all of which reduce the processing required to answer a behavioral question. A behavioral dimension may capture a past association with another dimension, a fact, or a categorization of a fact.
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Past Association with Another Dimension
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A behavioral attribute can be used to capture the historic association of a dimension row with information that would normally be stored in another table. Without the behavioral dimension, this past association would only be available by querying a fact table. Most often,
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the related dimension is a date, and it is added to the dimension table to signify a past event of importance. For example, analysts in the marketing group may want to be able to use the date of a customer s most recent order to filter queries. Storing this date in the customer table eliminates the necessity to query an orders fact table to find the dates on which each customer last placed an order. Figure 6-13 contains two examples of this technique: first_order_date and last_order_date. Without these behavioral dimensions, the date of each customer s last order would only be obtainable by querying the order_facts table. Using this information to study current orders would require programmatic logic or a correlated subquery, well out of reach of the average user. By placing this information in the customer table, the query is dramatically simplified. The last_order_date can be used to filter the query in the same way that any other attribute would, without any additional processing.
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