Strategies for Efficient Measure Slicing by Categories Across Multiple Fact Tables in Power BI

Greetings Power BI Community,

I’m seeking guidance on handling a common pattern in Power BI reports involving multiple fact tables with various dimensions. I believe this is a scenario many of you might have encountered, and I’d greatly appreciate your best practice recommendations.

In my current setup, I have several dimensions, such as dates and projects, which include a hierarchy of different organizational units. These are connected to numerous fact tables (metrics, risks, revenue, NPS, etc.) on a 1-to-many basis, all sharing the same granularity (date + project).

Each fact table contains specific metrics and pre-calculated categories and statuses within the data warehouse. These categories and statuses typically have limited options (2 to 7). In Power BI, I’ve developed multiple measures to aggregate and calculate insights, and I need to slice and dice these measures not only by the standard dimensions but also by the unique categories and statuses from each fact table.

The challenge is how to structure the data model and write measures that enable this level of detailed slicing without the possibility of using a role-playing dimension due to the distinct nature of categories and statuses across tables.

Currently, I’m storing these categories and statuses within their respective fact tables. To facilitate slicing and dicing by the status attribute from the metrics table, I’m employing measures alongside the SUMMARIZE function. For instance, a measure that helps me dissect the revenue from the revenue fact table by the statuses in the metrics table looks like this:

CALCULATE(
    SUM(Revenue[Amount]),
    SUMMARIZE(
        Metrics,
        Projects[ProjectID]
    )
)

This approach allows me to analyze the revenue figures by various status categories defined in the metrics table. However, managing numerous measures across different fact tables is becoming increasingly complex.

Could anyone share their strategies or suggestions for managing such a data model? How do you handle cross-filtering across different fact tables with distinct categories and statuses? Any insights or examples would be invaluable.

Thank you in advance for your help!

Hi folks,

Does anyone have any ideas or recommendations? Or perhaps my question was not described clearly?

Thanks