What are the benefits of using a factless fact table for data modeling?

1 What is a factless fact table?

Sometimes, you may encounter a situation where you need to model a business process that does not have any numerical facts, but only dimensions. For example, you may want to track the attendance of students in a school, the events that customers attend in a marketing campaign, or the products that are browsed on an e-commerce website. In these cases, you can use a factless fact table, which is a fact table that does not contain any measures, but only foreign keys that reference the dimension tables. A factless fact table acts as a bridge or a junction between the dimensions, and records the occurrence or existence of an event or a relationship.

2 Why use a factless fact table?

A factless fact table may not seem necessary due to its lack of storing meaningful data, but it has several benefits for data modeling and analysis. For instance, it simplifies the star schema and avoids creating complex or redundant dimension tables. This can be seen when modeling the attendance of students in a school, as a dimension table combining student and class attributes would result in a large and sparse table that would be difficult to maintain and query. A factless fact table can link these dimensions with just their primary keys instead. Additionally, it enables the analysis of many-to-many relationships between dimensions, such as analyzing the events customers attend in a marketing campaign. It also supports the calculation of derived measures or facts, allowing for the analysis of products browsed on an e-commerce website without numerical facts. These derived measures can provide valuable insights into customer behavior and preferences.

3 How to create a factless fact table?

Creating a factless fact table is similar to creating a regular fact table, except that you do not have any measures to populate the table. You only need to identify the dimensions that are involved in the business process, and create foreign keys that reference the primary keys of each dimension table. You may also need to create a surrogate key for the factless fact table, which is a unique identifier that does not have any business meaning, but helps to maintain the integrity and performance of the table. A surrogate key can also be used to join the factless fact table with other fact tables, if needed.

4 How to query a factless fact table?

Querying a factless fact table is similar to querying a regular fact table, except that you do not have any measures to aggregate or filter the data. You only need to join the factless fact table with the dimension tables, and use the attributes of the dimensions to group, sort, or slice the data. You may also need to use some functions or expressions to calculate the derived measures from the factless fact table, such as count, distinct, or ratio. Here is an example of a SQL query that uses a factless fact table to analyze the products that are browsed on an e-commerce website:

-- Assume that we have a factless fact table called ProductVisit, which has the following columns: 
-- ProductVisitID (surrogate key), ProductID (foreign key), CustomerID (foreign key), VisitTime (date and time of the visit)
-- Assume that we have two dimension tables called Product and Customer, which have the following columns: 
-- ProductID (primary key), ProductName, ProductCategory, ProductPrice
-- CustomerID (primary key), CustomerName, CustomerAge, CustomerGender
-- The query below calculates the number of visits, the number of unique visitors, and the conversion rate for each product category
SELECT p.ProductCategory, COUNT(*) AS Visits, COUNT(DISTINCT v.CustomerID) AS UniqueVisitors, 
       COUNT(DISTINCT v.CustomerID) / (SELECT COUNT(DISTINCT CustomerID) FROM ProductVisit) AS ConversionRate
FROM ProductVisit v
JOIN Product p ON v.ProductID = p.ProductID
GROUP BY p.ProductCategory
ORDER BY Visits DESC;        

5 What are the challenges of using a factless fact table?

A factless fact table is a useful and flexible data modeling technique, but it also has some challenges that you need to be aware of and address. For instance, it can consume a lot of storage space and affect the performance of the data warehouse. This is because it records every occurrence or combination of the dimensions, making the table grow rapidly and take up a lot of disk space. Additionally, it can be difficult to understand and interpret the data, as it does not have any measures that indicate the value or importance. To overcome these challenges, you may need to apply some compression, partitioning, or indexing techniques to optimize the storage and performance of the factless fact table. Additionally, you may need to provide some documentation, metadata, or visualization tools to explain and illustrate the data and the calculations. Lastly, you may also need to validate and verify the data quality and accuracy of the factless fact table.