Saturday, 4 August 2012

What is a FACTLESS FACT TABLE?Where we use Factless Fact

We know that fact table is a collection of many facts and measures having multiple keys joined with one or more dimesion tables.Facts contain both numeric and additive fields.But factless fact table are different from all these.
A factless fact table is fact table that does not contain fact.They contain only dimesional keys and it captures events that happen only at information level but not included in the calculations level.just an information about an event that happen over a period.
A factless fact table captures the many-to-many relationships between dimensions, but contains no numeric or textual facts. They are often used to record events or coverage information. Common examples of factless fact tables include:
  • Identifying product promotion events (to determine promoted products that didn’t sell)
  • Tracking student attendance or registration events
  • Tracking insurance-related accident events
  • Identifying building, facility, and equipment schedules for a hospital or university
Factless fact tables are used for tracking a process or collecting stats. They are called so because, the fact table does not have aggregatable numeric values or information.There are two types of factless fact tables: those that describe events, and those that describe conditions. Both may play important roles in your dimensional models.

Factless fact tables for Events
The first type of factless fact table is a table that records an event. Many event-tracking tables in dimensional data warehouses turn out to be factless.Sometimes there seem to be no facts associated with an important business process. Events or activities occur that you wish to track, but you find no measurements. In situations like this, build a standard transaction-grained fact table that contains no facts.
For eg.

The above fact is used to capture the leave taken by an employee.Whenever an employee takes leave a record is created with the dimensions.Using the fact FACT_LEAVE we can answer many questions like
  • Number of leaves taken by an employee
  • The type of leave an employee takes
  • Details of the employee who took leave
Factless fact tables for Conditions
Factless fact tables are also used to model conditions or other important relationships among dimensions. In these cases, there are no clear transactions or events.
It is used to support negative analysis report. For example a Store that did not sell a product for a given period.  To produce such report, you need to have a fact table to capture all the possible combinations.  You can then figure out what is missing.
For eg, fact_promo gives the information about the products which have promotions but still did not sell
This  fact answers the below questions:
  • To find out products that have promotions.
  • To find out products that have promotion that sell.
  • The list of products that have promotion but did not sell.
This kind of factless fact table is used to track conditions, coverage or eligibility.  In Kimball terminology, it is called a "coverage table."

We may have the question that why we cannot include these information in the actual fact table .The problem is that if we do so then the fact size will increase enormously .

Factless fact table is crucial in many complex business processes. By applying you can design a dimensional model that has no clear facts to produce more meaningful information for your business processes.Factless fact table itself can be used to generate the useful reports.


  1. Very nice explaining

  2. Nice explanation with diagram....:)

  3. Very Good Explanation. Thanks a lot

  4. Thanks ALL for the supporting comments

  5. Great article!

  6. Thank u .very informative!!!

  7. Thanks a lot ....very good explanation

  8. Keep up the good Work

  9. Perfect explanation !!!! Great job..:)

  10. thanks for giving valuable and accurate contents and keep going

  11. Lovely explanation!

  12. explained very well wid example

  13. Great explanation with valid scenarios!

  14. Thank you all ......please check out our latest post on Unix @

  15. Thanks for information, I am new to DWH and want to know why we are calling it factless fact table, why it cant be just dimention table?

  16. Very good explantions. Keep it up..!!!

  17. v.good information

  18. Hi All,

    Just trying to give answer for above question "why we are calling it factless fact table, why it cant be just dimention table?"

    Dimension table - A table having detailed information of an object. Here object can be a product, store, customer, etc. And will have one primary key which will be used in Fact table

    Fact table - A table having facts, which are used to measure the performance of the business using the primary key of the dimension table.
    So, there could be one fact table to capture number of one particular product sold from a Store one a given period. If that particular product is not sold from that store in that given period, then in that table there will be no fact only primary keys of each dimension table will available.

    Since, Factless fact table will have multiple primary keys which are coming from different Dimension table whereas Dimension table will have only one primary key. Hence, Factless fact table can't be called dimension table.

  19. Hi
    I have a quick question. How do we load coverage table with the information that a product did not sell in a perticular period?
    I guess etl process has to execute appropriate sql query each time it loads the table ?
    For event tracking its understandable that an entry gets created to track it, and its simple.

  20. how does a factless fact work with a type 2 dimension? lets say i have a factless fact which associates customers and customer groups (customer can belong to many groups). customer is also type 2, when the customer record gets updated, a new record is inserted with a new surrogate key. at this point, the new customer record has lost the relationship to the customer group via the factless fact. thanks for any help.


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