Why VALUES and DISTINCT in Power BI? - Purpose & Use Cases

The Big Idea

What if you could instantly see all unique customers without digging through endless rows?

The Scenario

Imagine you have a huge sales list in a spreadsheet with many repeated customer names. You want to find all unique customers to send a special offer. Doing this by scanning and copying each name manually is tiring and confusing.

The Problem

Manually filtering unique values takes a lot of time and is easy to mess up. You might miss some names or accidentally include duplicates. It's hard to keep track when the list changes often.

The Solution

Using VALUES or DISTINCT in Power BI automatically picks out unique items from your data. It updates instantly when your data changes, so you always get the right list without extra work.

Before vs After

✗ Before

Filter each name by hand and copy unique ones

✓ After

UniqueCustomers = DISTINCT(Sales[CustomerName])

What It Enables

You can quickly create reports and visuals that focus on unique entries, making your insights clearer and your work faster.

Real Life Example

A marketing team uses DISTINCT to list all unique product categories sold last month, helping them target promotions effectively without missing any category.

Key Takeaways

Manually finding unique values is slow and error-prone.

VALUES and DISTINCT automatically extract unique items from data.

This makes reports accurate, dynamic, and easy to update.

Practice

1. What is the main difference between the DAX functions VALUES and DISTINCT?

easy

A. VALUES returns unique values including blanks, DISTINCT returns unique values excluding blanks.

B. VALUES returns unique values and blanks, DISTINCT returns unique values including blanks.

C. VALUES returns unique values excluding blanks, DISTINCT excludes blanks.

D. VALUES and DISTINCT are exactly the same in all cases.

Solution

1. Step 1: Understand VALUES behavior

   VALUES returns unique values from a column and includes blank values if they exist.

2. Step 2: Understand DISTINCT behavior

   DISTINCT returns unique values but excludes blank values from the result.

3. Final Answer:

   VALUES includes blanks, DISTINCT excludes blanks -> Option A

4. Quick Check:

   VALUES includes blanks, DISTINCT excludes blanks [OK]

Hint: Remember: VALUES keeps blanks, DISTINCT drops them [OK]

Common Mistakes:

• Thinking VALUES and DISTINCT always return the same results
• Assuming DISTINCT includes blanks
• Confusing the behavior of blank values in both functions

2. Which of the following is the correct syntax to get unique values from the column Product[Category] using DISTINCT?

easy

A. DISTINCT(Product[Category])

B. DISTINCT(Product.Category)

C. VALUES(Product[Category])

D. VALUES(Product.Category)

Solution

1. Step 1: Check correct column reference syntax

   In DAX, columns are referenced with table name and column name inside square brackets, like Product[Category].

2. Step 2: Check function syntax

   Both VALUES and DISTINCT take a column reference in parentheses. The correct syntax is DISTINCT(Product[Category]).

3. Final Answer:

   DISTINCT(Product[Category]) -> Option A

4. Quick Check:

   Correct syntax uses square brackets and function call [OK]

Hint: Use square brackets for columns inside functions [OK]

Common Mistakes:

• Using dot notation instead of square brackets
• Missing parentheses after function name
• Using incorrect column references

3. Given a table Sales with a column CustomerID containing values [1, 2, 2, 3, BLANK, 3], what will be the result of COUNTROWS(VALUES(Sales[CustomerID]))?

medium

A. 6

B. 3

C. 4

D. 5

Solution

1. Step 1: Identify unique values including blanks

   The unique values in CustomerID are 1, 2, 3, and BLANK. That is 4 unique values.

2. Step 2: Apply COUNTROWS on VALUES

   VALUES returns unique values including blanks, so COUNTROWS counts 4 rows.

3. Final Answer:

   4 -> Option C

4. Quick Check:

   Unique values including blank = 4 [OK]

Hint: VALUES includes blanks, so count them too [OK]

Common Mistakes:

• Ignoring blank values in count
• Counting duplicates multiple times
• Confusing DISTINCT behavior here

4. You wrote this DAX measure: UniqueProducts = DISTINCT(Product[ProductName]). When you use it in a card visual, it shows an error. What is the problem?

medium

A. The column name is incorrect; it should be ProductName[Product].

B. DISTINCT returns a table, but card visuals need a single value.

C. DISTINCT cannot be used in measures, only in calculated columns.

D. You need to wrap DISTINCT with SUM to get a number.

Solution

1. Step 1: Understand what DISTINCT returns

   DISTINCT returns a table of unique values, not a single number or scalar value.

2. Step 2: Understand card visual requirements

   Card visuals require a single scalar value, like a number or text, not a table.

3. Final Answer:

   DISTINCT returns a table, but card visuals need a single value. -> Option B

4. Quick Check:

   DISTINCT returns table, card needs scalar [OK]

Hint: DISTINCT returns tables; use aggregation for single values [OK]

Common Mistakes:

• Using DISTINCT directly in measures for visuals needing scalars
• Incorrect column references
• Thinking DISTINCT returns a number

5. You want to create a slicer that shows only unique non-blank customer names from the Customers[Name] column. Which DAX expression should you use to create a calculated table for this slicer?

hard

A. CustomerNames = FILTER(DISTINCT(Customers[Name]), NOT(ISBLANK(Customers[Name])))

B. CustomerNames = VALUES(Customers[Name])

C. CustomerNames = FILTER(VALUES(Customers[Name]), NOT(ISBLANK(Customers[Name])))

D. CustomerNames = DISTINCT(Customers[Name])

Solution

1. Step 1: Understand the need to exclude blanks

   Both VALUES and DISTINCT return unique values, but VALUES includes blanks while DISTINCT excludes them.

2. Step 2: Choose function and filter blanks

   DISTINCT returns unique values excluding blanks, which is perfect for a slicer.

3. Step 3: Compare options

   CustomerNames = DISTINCT(Customers[Name]) directly provides unique non-blank names without needing extra filtering.

4. Final Answer:

   CustomerNames = DISTINCT(Customers[Name]) -> Option D

5. Quick Check:

   DISTINCT excludes blanks for clean slicer [OK]

Hint: DISTINCT excludes blanks automatically [OK]

Common Mistakes:

• Using VALUES without filtering blanks
• Assuming DISTINCT includes blanks
• Using incorrect column references in FILTER conditions