[Power BI] Two Faces of the DAX ALL Function: 5 Practical Patterns You Can Use Right Away


In the world of Power BI, the decisive turning point that separates beginners from pros is the ability to leverage the ALL function. While many beginners understand ALL simply as an "eraser that wipes away filters," pros use it as a "strategic tool to redefine the foundation of calculation." Today, as a data analyst, I will perfectly summarize the five core patterns of the ALL function that can be applied immediately in the field.

 

1. The Essence of the DAX ALL Function: Filter Modifier vs. Table Provider


Inside the engine, the ALL function possesses two powerful identities:
  • Filter Modifier: Inside a CALCULATE function, it ignores existing filters (such as slicers) and redefines the baseline for calculation.
  • Table Provider: It re-summons the original data that was filtered out and supplies it as a virtual table to serve as the raw material for calculations.
When these two identities meet practical business scenarios, the following powerful analytical metrics are born.

Related Post

[Power BI] The True Identity of the DAX ALL Function: How is Filter Ignoring Actually Accomplished?

If you wish to build a solid foundation by understanding the internal mechanics of the ALL function from the ground up, we highly recommend reviewing the post above first!

 

2. Why is the ALL Function Important?


The metrics we frequently create in Power BI reports include:
  • Share against the total
  • Company-wide rankings
  • Cumulative performance (YTD, MTD)
  • Sales rates and SKU coverage
  • Gaps against benchmarks
At the heart of all these calculations lies the need for a "global baseline independent of selected values." And the key tool that creates this baseline is ALL.

 

3. Five ALL Function Patterns for Practical Use (Best 5)


I have categorized the roles of ALL based on the most frequent scenarios in the field. Each item is a core topic that will lead to more detailed future guides.

case scenario for All's role and practical focus

 

4. Performance Optimization Tips (Cautions) for Using ALL


The ALL function is extremely powerful, but if used incorrectly, it becomes the main culprit in slowing down your reports. Keep these three points in mind:
  • Memory Management: ALL internally generates virtual tables, so if the data is large, the "bundle" becomes heavy.
  • Minimize Scope: Rather than the entire table (ALL(Table)), specify only the necessary columns (ALL(Table[Column])) to reduce the load.
  • Consider Alternative Functions: Instead of unconditional filter removal, choosing the right function for the situation—such as REMOVEFILTERS, ALLSELECTED, or ALLEXCEPT—is a professional skill.
    • ALL(Table): Removes filters from the entire table.
    • ALL(Table[Column]): Removes filters only from that specific column.
    • ALLEXCEPT(): Keeps only specific filters.
    • ALLSELECTED(): Maintains the range based on current selections.


 

5. Deep Dive: Go to Detailed Guides


If you are curious about the specific DAX formulas and practical usage methods for each pattern, please check the detailed pages below:
  • Part 1: Share Analysis – Combining CALCULATE and ALL to find % of Total. (coming soon)
  • Part 2: Ranking Analysis – Creating a global rank that doesn't change even when filters are applied. (coming soon)
  • Part 3: Cumulative Analysis – Implementing running totals with ALL without time intelligence functions. (coming soon)
  • Part 4: Performance Management – Non-sales analysis techniques to find items that haven't sold. (coming soon)
  • Part 5: Fixed Baselines – How to set benchmark indices, the core of comparative analysis. (coming soon)

 

Wrapping up


The DAX ALL function is not just an eraser. It is a design tool that reshapes the data landscape for calculation. By accurately understanding these five patterns, you can design and optimize over 90% of business dashboards yourself.

If this post was helpful, please leave a comment! I will return with the next series to take your Power BI skills to the next level.







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