Even though
the entire point of creating a fact table is to store some sort of measures,
there do exist some fact tables without measures. These are called as Factless Fact table. There are
situations in which an intersection of dimensions makes sense in data
modelling. They are used to capture information and not perform any
calculations. Factless fact tables do not capture any numeric or textual facts.
They are
usually used in two kind of situations:
To record an
event
Factless fact tables are used to track some sort of event
such as student attendance or registration or tracking accident related events.
These are events or activities that we wish to track but there are no
measurements to record. In these scenarios, the grain of the fact table will be
the event that occurred. If we track student attendance, the fact tables and
dimension tables will look like this:
This dimensional schema can be used to answer many questions such as:
• Which classes were the most heavily attended?
• Which teachers taught classes in facilities belonging to other departments?
• Which facilities were the most lightly used?
• What was the average total walking distance of a student in a given day
To record certain conditions
These are mainly for negative analysis report. It is easy to track products that were sold and revenue generated on those products because there is an easily identifiable grain or transaction. But if we want to track the products that were not sold for promotion purposes, we might need Factless tables. It is often referred to as coverage tables.
The fact table can help us identify these details:
• Number of products that have promotions
• Number of products that have promotions that sell
• Number of products that have promotions that did not sell
These are mostly for administrative purposes.
References:
http://www.kimballgroup.com/1996/09/02/factless-fact-tables/
http://dwhlaureate.blogspot.com/2012/08/factless-fact-table.html
The Data Warehouse Toolkit Third Edition - Ralph Kimball & Margy Ross
This dimensional schema can be used to answer many questions such as:
• Which classes were the most heavily attended?
• Which teachers taught classes in facilities belonging to other departments?
• Which facilities were the most lightly used?
• What was the average total walking distance of a student in a given day
To record certain conditions
These are mainly for negative analysis report. It is easy to track products that were sold and revenue generated on those products because there is an easily identifiable grain or transaction. But if we want to track the products that were not sold for promotion purposes, we might need Factless tables. It is often referred to as coverage tables.
The fact table can help us identify these details:
• Number of products that have promotions
• Number of products that have promotions that sell
• Number of products that have promotions that did not sell
These are mostly for administrative purposes.
References:
http://www.kimballgroup.com/1996/09/02/factless-fact-tables/
http://dwhlaureate.blogspot.com/2012/08/factless-fact-table.html
The Data Warehouse Toolkit Third Edition - Ralph Kimball & Margy Ross
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