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Although no facts are stored in a factless fact table, it does support measurement of the business process. It can be used to answer a wide variety of analytic questions, simply by counting rows in the fact table. Dimensions can be used to filter or group the counts in a variety of useful ways. Using the contact_facts star, it is possible to measure how often a customer is contacted by counting rows in the fact tale. This is done by choosing an arbitrary column in the fact table to count. The number of times John Smith was contacted during the month of January 2009 can be measured by the following query:

pension plans, health-care coverage, post-termination consulting agreements, golden parachutes, loans, special vesting on stock options, company-owned life insurance, gross-ups for taxes, etc While some of these benefits must be disclosed, the rules and the format for disclosure are unclear enough that companies can effectively hide some very substantial benefits and payments Unfortunately many of these payments do not come to light until there is a change in control or some type of transaction In the case of an executive s retirement, they can usually escape disclosure entirely (except in the notable case of Jack Welch, whose extravagant benefits came to light in postretirement divorce proceedings) In fact a company can avoid disclosing substantial payments to one of its top five executives by making the payments after the person has retired and the information no longer has to be reported.

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SELECT COUNT( contact_facts.contact_type_key ) /* Count an arbitrary fact table column */

12,000

FROM day, customer, contact_facts WHERE customer.name = 'John Smith' AND day.month = 'January' AND day.year = 2009 AND day.day_key = contact_facts.day_key AND customer.customer_key = contact_facts.customer_key

The query returns a single number the number of contacts with John Smith during the month. It can be embellished by grouping results by channel from the contact_type dimension table:

To truly make total executive pay more transparent to investors, these disclosure loopholes should be closed 4 Relative Compensation Levels Boards and management must start to take seriously the relative levels of compensation paid within a company They must ask themselves if the CEO should really be paid several hundred times the wage given to the average worker According to industry studies CEOs of major corporations made an average of 419 times the pay of an average worker in 1999, up from 326 in 1997 and 42 in 1980 Until recently I ve always felt that the so-called CEO pay multiple was an irrelevant, socialistic concept with no merit in a capitalistic society where market forces drive the price for all goods and services, including the price of management.

10,000

SELECT contact_type.contact_channel, COUNT( contact_facts.contact_type_key ) . . . GROUP BY contact_type.contact_channel

This version provides the number of contacts for channels such as E-mail and for Telephone. Adding the direction attribute would further group results by Inbound and Outbound. The variations on this theme are limited only by the availability of dimension attributes. Dimension values can be used to constrain the query, sort data, group facts, and so forth. As always, a rich set of dimensions enables a wide range of analytics. TIP The events in a factless fact table can be aggregated by counting rows. Any column in the fact table can serve as the basis of the count.

Earlier in this book, you encountered another example of a factless fact table. The star in Figure 8-2 was used to track the history of changes to an insurance policy dimension. The grain of the fact table was defined as one row per change to a policy. Foreign keys referred to the policy dimension, the effective date, and the expiration date. Like contact_ facts, the fact table contained no facts but could be used for various kinds of analysis. While the policy_change_facts design was easily converted into a time-stamped dimension, this is not an option for contact_facts.

8,000

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