Introduction:

When DAX Tricks You Instead of Helping You
If you’ve ever stared at a Power BI visual and muttered, “Why is this number wrong?”, you’re not alone. DAX (Data Analysis Expressions), the core language powering Power BI calculations, is deceptively simple. It looks like Excel. It behaves like code. And it often acts like neither.

That’s what makes it powerful — and dangerous.

DAX is the key to unlocking advanced insights, creating complex logic, and customizing analytics to fit your business model. But misuse it, and you could end up with visuals that lie, slow reports, or worse – totals that make no sense.

This article uncovers the five most common DAX mistakes made by beginners and even seasoned users. These aren’t just errors — they’re traps, misunderstandings, and silent performance killers. We’ll break down each one, show you the wrong and right way, and provide real data samples so you can avoid these pitfalls in your own work.

“The greatest enemy of knowledge is not ignorance, it is the illusion of knowledge.”
— Stephen Hawking

Mistake 1: Treating CALCULATE as a Magic Function

Don’t Use CALCULATE Just Because Everyone Else Does

A lot of DAX beginners use CALCULATE like it’s a wand that fixes everything. But CALCULATE isn’t magical — it’s methodical. Its true power lies in changing filter context.

❌ Bad Example:

DAXCopyEditSales Amount = CALCULATE(SUM(Sales[Amount]))

Why is this wrong? Because it does nothing different from just using SUM(Sales[Amount]) — unless you’re relying on context transition from row context (like in a calculated column or row-level operation).

✅ Good Example:

DAXCopyEditSales Amount North = CALCULATE(
    SUM(Sales[Amount]),
    Sales[Region] = "North"
)

This modifies the filter context explicitly. Now you’re telling Power BI to sum only for a specific region — which is how CALCULATE should be used most of the time.

Sample Data:

Solution: Understand CALCULATE as a context-shaping tool. Use it with filters. And only skip filters when you’re intentionally leveraging context transition (e.g., in calculated columns or when nesting in iterator functions).

Mistake 2: Not Understanding How Totals Are Evaluated

Why Do My Totals Look Wrong?

This is the classic “my measure works perfectly for each row, but the total is off.” It’s not a bug. It’s how DAX calculates evaluation context for totals.

❌ Bad Example:

Imagine this:

DAXCopyEditTop Selling Days = 
IF(Sales[Amount] > 500, Sales[Amount] * 0.10, 0)

Now, when you summarize this in a matrix per month, you’ll see values per day — but the total will be wrong. Why? Because the total isn’t summing up the rows; it’s recalculating the logic across all data.

✅ Good Example:

Use iterators:

DAXCopyEditTotal Commission = 
SUMX(
    VALUES(Sales[Date]),
    [Top Selling Days]
)

Sample Data:

Solution: Use SUMX to iterate over the visible categories and apply the measure row by row. This gives accurate totals that match the visual logic.

Mistake 3: Not Understanding Row Context When Creating Measures

Row Context Isn’t There Unless You Create It

Unlike calculated columns, measures don’t automatically operate row by row. They operate on filter context. So if you write an IF condition in a measure without using an iterator, it won’t work the way you think.

❌ Bad Example:

DAXCopyEditWrong Measure = IF(Sales[Channel] = "Online", Sales[Amount] * 0.9, Sales[Amount])

This will return errors or produce nonsense — because there’s no row context.

✅ Good Example:

DAXCopyEditCorrect Measure = 
SUMX(
    Sales,
    IF(Sales[Channel] = "Online", Sales[Amount] * 0.9, Sales[Amount])
)

Sample Data:

Solution: When you want to calculate per row inside a measure, you need iterator functions like SUMX, AVERAGEX, FILTER, etc., to create that row context manually.

Mistake 4: Expecting Drag-and-Drop Calculations to Work Like Excel Pivot Tables

Power BI ≠ Excel. Granularity Matters.

A common misunderstanding is assuming that aggregation in visuals (like “Average”) works the same as Excel. In Power BI, the granularity of your data affects the result drastically.

❌ Bad Example:

Drag “Units Sold” to a visual, change aggregation to “Average.” You think you’re calculating average daily units. But you’re really getting average per transaction.

✅ Good Example:

Create a measure:

DAXCopyEditAvg Daily Units = 
AVERAGEX(
    VALUES(Calendar[Date]),
    CALCULATE(SUM(Sales[Units]))
)

Sample Data:

Solution: Always control granularity with VALUES() or SUMMARIZE() in your DAX. Use calendar tables for date-based logic and context transition to your advantage.

Mistake 5: Changing Filter Direction to Bi-Directional at the Model Level

It Solves One Problem, But May Create Others

Bi-directional filtering seems like a quick fix. It lets your slicer work. But it can blow up your model.

❌ Bad Example:

You change a one-directional relationship between Sales and Products to bi-directional so that category slicers can filter properly. Now, some other unrelated table is behaving strangely.

✅ Good Example:

Use CROSSFILTER at the measure level:

DAXCopyEditSales With BiFilter = 
CALCULATE(
    SUM(Sales[Amount]),
    CROSSFILTER(Sales[ProductID], Products[ProductID], BOTH)
)

Sample Data:

Two related tables:

• Sales: ProductID, Amount
• Products: Category, ProductID

Solution: Use CROSSFILTER only where necessary and keep your model lean and predictable. Changing model-level filters affects every measure — and not always for the better.

General DAX Best Practices

🧠 Use descriptive variable names (VAR).

📄 Comment your logic using //.

⏱ Optimize for performance using REMOVEFILTERS, KEEPFILTERS, etc.

🔍 Test in small visuals before scaling.

💡 Don’t copy-paste without understanding.

Conclusion: DAX is a Language — Learn Its Grammar

DAX is not a collection of random functions. It’s a language built around context — filter context, row context, evaluation context. The more you understand these, the less likely you are to fall into common traps.

Every mistake listed here has one root cause: misunderstanding context.

“You can’t improve what you don’t understand.”
— W. Edwards Deming

So take time to learn the why behind the what. Watch videos. Build test visuals. Break things on purpose. That’s the real way to master Power BI.

Related Article

👉White paper: Writing DAX Queries
👉Importance of Iteration DAX in Power BI