What is the process for determining fact table granularity?

1 Identify the business processes

The first step is to identify the business processes that generate the facts and measures that you want to analyze. A business process is a set of related activities that produce a specific outcome, such as sales, inventory, or customer service. You should map the business processes to the source systems that capture the data and understand how the data is structured, stored, and updated.

2 Choose the lowest level of detail

The next step is to choose the lowest level of detail that you need to support the analysis requirements. This is also known as the atomic level, as it represents the smallest and most granular unit of measurement. For example, if you want to analyze sales by product, customer, and date, the atomic level would be a single sales transaction. Choosing the lowest level of detail gives you the most flexibility and accuracy, as you can aggregate and slice the data in any way you want.

3 Consider the trade-offs

The final step is to consider the trade-offs between granularity and performance. The lower the granularity, the more rows and columns you will have in your fact table, which will increase the storage space and query time. You may also have to deal with more complex data quality and data integration issues, such as duplicates, missing values, or inconsistent formats. Therefore, you should balance the benefits of granularity with the costs of performance and maintenance.

4 Apply dimensional modeling techniques

Once you have determined the fact table granularity, you can apply dimensional modeling techniques to design your data warehouse schema. Dimensional modeling is a method of organizing data into facts and dimensions, where facts are numerical measures and dimensions are descriptive attributes. You can use star or snowflake schemas to link your fact table with your dimension tables, and use surrogate keys, conformed dimensions, and degenerate dimensions to ensure consistency and simplicity.

5 Test and validate your design

The last step is to test and validate your design by loading some sample data and running some queries. You should check if your fact table granularity supports the analysis needs of your users and if your data warehouse performs well under different scenarios. You should also verify if your data quality and data integration processes are working properly and if your data warehouse conforms to the business rules and definitions.

6 Review and refine your design

The process of determining fact table granularity is not a one-time activity, but a continuous one. You should review and refine your design periodically, as your business requirements and data sources may change over time. You should also solicit feedback from your users and stakeholders, and monitor the usage and performance of your data warehouse. By doing so, you can ensure that your fact table granularity remains optimal and relevant for your data warehouse.

7 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?