A Guide to Data Modeling in Power BI
Source: Dev.to
Data visualization is the pretty part of Power BI, but data modeling is the engine under the hood. If your model is messy, your reports will be slow, your DAX will be overly complex, and your numbers might even be wrong.
Cardinality
Most relationships should be One-to-Many (e.g., one customer has many orders). Avoid many‑to‑many relationships whenever possible, as they lead to ambiguity.
Cross‑filter Direction
Keep this setting to Single by default. Setting it to Both can create circular dependencies and significantly slow down your report.
The Date Table
Never rely on Power BI’s Auto Date/Time. Always create a dedicated calendar dimension. It ensures your time‑intelligence functions—such as year‑over‑year growth—work correctly.
Why Good Modeling Is Critical
You might be tempted to just throw one giant flat table (like an Excel sheet) into Power BI. Don’t. Modeling matters for several reasons:
- Performance: Power BI’s engine is optimized for star schemas. Structured data compresses much more efficiently.
- Accuracy: Poor modeling can cause double counting or incorrect aggregations when filtering across different categories.
- Usability: A well‑modeled model is intuitive for end‑users. When dimensions are clearly separated, users can easily drag and drop fields to find insights without needing a degree in data science.