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heck of a time working through this in Accounting, and we happened to make the front section of the local newspaper this morning Did anyone notice that Any more of these icebergs, and we ll all have a lot of stock in the Titanic of coffee shops! Monica tried to smile at the tension-relieving joke, but it wasn t enough Everyone wanted answers, and would settle for nothing less It was time to stick it to the IT Guy Gordon, you understand the technical issues better than any of us What s your take on this situation It was inevitable, Gordon said His statement was blunt, to the point, and frustrated almost everyone Sooner or later, a server failure was going to cause a problem For over three years, I ve been asking each of you to place more money into performing data protection.

The techniques in this chapter have applicability in a wide variety of industries and across many subject areas. If you want to refine your knowledge, you can learn more about factless fact tables by studying more examples. The following are some places to start. Factless Fact Tables for Activities A factless fact table can be used to capture information about attendance. An example can be found in 6 of The Data Warehouse Lifecycle Toolkit, Second Edition (Wiley, 2008) by Ralph Kimball, Margy Ross, Warren Thornthwaite, Joy Mundy, and Bob Becker. Kimball and Ross update the attendance model to incorporate absences in 12 of The Data Warehouse Toolkit, Second Edition (Wiley, 2002). In this case, the fact table is no longer factless; it includes a fact that contains the values 0 or 1 (absent or present). This can also be achieved with a pair of facts one used to count absences and one used to count attendance. In the same chapter, the authors provide a factless design to track student course registrations. In the insurance industry, a factless fact table can be used to track accident events. Kimball and Ross describe this in 15 of The Data Warehouse Toolkit.

SQL also lets you ask for calculated results. For example, you can ask SQL to calculate the amount by which each salesperson is over or under quota: SELECT NAME, SALES, QUOTA, (SALES - QUOTA) FROM SALESREPS NAME -------------Bill Adams Mary Jones Sue Smith Sam Clark Bob Smith Dan Roberts Tom Snyder Larry Fitch Paul Cruz Nancy Angelli SALES ----------$367,911.00 $392,725.00 $474,050.00 $299,912.00 $142,594.00 $305,673.00 $75,985.00 $361,865.00 $286,775.00 $186,042.00 QUOTA ----------$350,000.00 $300,000.00 $350,000.00 $275,000.00 $200,000.00 $300,000.00 NULL $350,000.00 $275,000.00 $300,000.00 (SALES-QUOTA) -------------$17,911.00 $92,725.00 $124,050.00 $24,912.00 -$57,406.00 $5,673.00 NULL $11,865.00 $11,775.00 -$113,958.00

But, no one seemed to get it! After all, why put money into something when you don t get a good return on investment Based on last year s budget, we were lucky to deploy the servers for Region 7 on time, let alone perform adequate backups for dozens of stores! Gordon was right, and Monica knew it He had tried hard to make everyone understand the importance of proper backup and recovery procedures But everyone was at fault Gordon s data protection cost estimates were astronomical No one wanted to begin to volunteer funds to cover the costs Monica knew she was guilty, as well I should have paid more attention to this issue, and for that I apologize I understand that it was a tough sell for you Gordon I don t think any of us hold you or the IT organization solely responsible for what happened.

When factless fact tables for events are summarized, the resulting aggregates are not factless. This phenomenon is explained in 8 of Mastering Data Warehouse Aggregates (Wiley, 2006) by Chris Adamson. Factless Fact Tables for Conditions A factless fact table used to track marketing promotions supplements a retail sales star in 12 of The Data Warehouse Toolkit. Note that Kimball and Ross refer to factless fact tables that describe conditions, coverage, or eligibility as coverage tables. Another condition that can be tracked by a factless fact table is the availability of a resource. Kimball and Ross make use of one to study utilization of facilities in 12 of The Data Warehouse Toolkit. It is generally not useful to aggregate a fact table that describes conditions, because doing so destroys the meaning of the original data. This phenomenon is explored in 8 of Mastering Data Warehouse Aggregates.