How do you measure the business value and impact of a factless fact table in a snowflake schema?

1 Benefits of factless fact tables

Factless fact tables can help you answer important business questions that involve many-to-many relationships, such as how many customers visited a store on a given day, or which products were promoted together in a campaign. They can also enable complex analysis and reporting, such as finding patterns, trends, or correlations among events or transactions. By storing only the keys of the related dimensions, factless fact tables can save space and simplify the data model.

2 Challenges of factless fact tables

Factless fact tables also have some drawbacks and limitations. They can be difficult to understand and interpret, especially for non-technical users, because they do not contain any measures or facts. They can also be hard to query and join, because they may require multiple joins or subqueries to retrieve the relevant information from the dimensions. Moreover, they can be prone to data quality issues, such as missing or duplicated records, if the source data is not properly validated and cleansed.

3 Designing factless fact tables

To design a factless fact table, you need to identify the dimensions that are involved in the event or transaction, and use their primary keys as foreign keys in the fact table. You also need to decide whether to use a single factless fact table for all events or transactions, or to create separate tables for different types of events or transactions. For example, you can have one factless fact table for customer visits, and another one for product promotions. You may also need to add some attributes or flags to the fact table to indicate the nature or status of the event or transaction, such as whether it was successful, cancelled, or pending.

4 Querying factless fact tables

To query a factless fact table, you need to join it with the relevant dimensions, and use aggregation functions or filters to obtain the desired results. For example, if you want to know how many customers visited a store on a given day, you can join the factless fact table for customer visits with the dimension tables for date and store, and count the distinct customer keys. If you want to know which products were promoted together in a campaign, you can join the factless fact table for product promotions with the dimension tables for product and campaign, and group by the product keys.

5 Evaluating factless fact tables

To evaluate the business value and impact of a factless fact table, you need to consider several factors, such as how often and by whom it is used, how accurate and reliable it is, how fast and easy it is to query, and how much it contributes to the overall data warehouse goals and objectives. You can use various methods and metrics to measure these factors, such as user feedback, data quality audits, query performance tests, and business intelligence dashboards. You can also compare the results and insights from the factless fact table with other sources of data or analysis, and see if they are consistent and meaningful.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?