Top 10 Advanced DAX Functions to Master in Power BI by 2025

And it’s never been more critical than now to have even just a basic understanding of the world of data analytics and the power of being able to speak DAX (Data Analysis Expressions) to change your life—because we live now in 2025, where data is the global language of making such strategic, tactical, and day-to-day business decisions. The basic division function will help you get started, but the advanced division function can help you cover a range of use cases, including hard derivation in Analytical Insights, interactive dashboard visualization, detailed level reporting in any tool (Power BI once more), etc.Regardless of where you fit as a Power BI Pro (advanced or not), analyst, or anyone in the middle and new here, here are the top 10 DAX functions to know for 2025 that are advanced.

1. CALCULATE()

What it is: Alters how data is analyzed.

Why it’s tough: The most fundamental part of almost all DAX formulas and key to flexible row context filtering/modifications.

CALCULATE(SUM(Sales[Revenue]), Region[Country] = "India")

Use case: Benchmark anything against anything programmatically.

2. FILTER()

What it returns: A table that represents a subset of another table or expression.

CALCULATE(SUM(Sales[Revenue]), FILTER(Sales, Sales[Revenue] > 10000))

Use case: When business logic requires advanced filtering beyond slicers.

3. ALL() / ALLEXCEPT()

What it is: A tool cleaner for columns or entire tables.

CALCULATE(SUM(Sales[Revenue]), ALL(Sales))

Use case: Calculating percentages or clearing context filters.

4. RELATED() / RELATEDTABLE()

What it is: Brings in data from associated tables using relationships.

RELATED(Customer[CustomerName])

Use case: Computed fields or measures from joined dimension fields.

5. VAR & RETURN

What it is: Intermediate variables for readable, efficient code.

VAR TotalSales = SUM(Sales[Revenue]) 
RETURN TotalSales * 0.1

Use case: Step-by-step calculations and debugging.

6. SUMX(), AVERAGEX(), COUNTX()

What it does: Row-by-row operations over a table.

SUMX(Orders, Orders[Quantity] * Orders[Price])

Use case: Custom aggregations, e.g., geo-based averages.

7. RANKX()

What it is: Ranks items based on expression values.

RANKX(ALL(Sales[Product]), SUM(Sales[Revenue]), , DESC)

Use case: Ranking KPIs for products, regions, or employees.

8. EARLIER()

What it does: Returns the previous row context.

CALCULATE(COUNTROWS(Sales), FILTER(Sales, Sales[Date] < EARLIER(Sales[Date])))

Use case: Row-level comparisons, running totals.

9. ISINSCOPE()

What it means: Checks if a column is in the current filter context.

IF(ISINSCOPE(Region[Country]), "Country Level", "All Level")

Use case: Responsive visual titles and KPIs.

10. TREATAS()

Its role: Applies a table expression as a filter on other columns.

CALCULATE(SUM(Sales[Revenue]), TREATAS(VALUES(Customer[Region]), Sales[Region]))

Use case: Creating virtual relationships that filter exist.