How to Count Blank Values in Power BI

Dealing with blank values in Power BI can be frustrating, but counting them is a critical first step toward cleaning your data and trusting your reports. Knowing exactly where the gaps are in your dataset prevents skewed calculations and misleading insights. This article will walk you through a couple of simple, effective ways to count blank or null values using DAX, helping you get a better handle on your data quality.

Why Counting Blank Values Matters

Before diving into the "how," let's quickly touch on the "why." Ignoring blanks in your data is like building a house on a shaky foundation. These empty cells can silently sabotage your analysis in several ways:

• Inaccurate Calculations: Functions like AVERAGE can be misleading if blanks are present. For example, if you're averaging customer satisfaction scores out of 5, a blank isn't a zero, but some BI tools might incorrectly treat it that way, dragging down your average. Counting them separately helps you understand the true response rate.
• Misleading Visuals: Charts and graphs can misrepresent reality. A report showing "sales by region" might have a conspicuously low bar for one region, not because sales are bad, but because half the records are missing the region data.
• Process and Data Entry Issues: A high number of blanks can be a red flag for a broken process. Maybe your web form's "Country" field isn't required, leading to incomplete customer profiles, or perhaps a sales team isn't consistently logging follow-up dates in the CRM. Counting these blanks helps you spot and fix the root cause.

In short, counting blanks gives you a clear picture of your data's health, allowing you to make informed decisions about whether to clean it, adjust your analysis, or fix the source of the problem.

Method 1: The Quick and Easy COUNTBLANK Function

The most straightforward method for counting empty cells in a column is using the designated DAX function: COUNTBLANK. It does exactly what its name implies - it goes through a single column and counts the number of cells that are completely blank.

This method is perfect when you just need a quick, high-level count for a specific field and don't need complex filtering logic.

How to Use COUNTBLANK

Let's say you have a 'SalesData' table and you want to find out how many orders are missing a 'Salesperson' value. You can create a simple measure to calculate this.

Step 1: Create a New Measure In the Report view of Power BI, right-click on your table in the 'Data' pane and select "New measure."

Step 2: Write the DAX Formula In the formula bar that appears, type the following DAX expression:

Blank Salesperson Count = COUNTBLANK('SalesData'[Salesperson])

Let's break it down:

• Blank Salesperson Count = This is the name you're giving the new measure.
• COUNTBLANK() This is the function that does the actual counting.
• 'SalesData'[Salesperson] This is the target - the specific column ('Salesperson') within the table ('SalesData') where you want to count blanks.

Step 3: Use the Measure in Your Report Once you press Enter, the new measure will appear in your 'Data' pane. You can now use it in your visuals. The easiest way to see the result is to drag it onto a Card visual, which will display the total count of blank salesperson entries.

Voila! You now have a live number that tells you exactly how many rows are missing salesperson information. It's direct, simple, and takes less than a minute to implement.

Method 2: A More Versatile Approach with COUNTROWS and ISBLANK

While COUNTBLANK is great for simple counts, it has limitations. It only works on a single column and isn't easily combined with more complex filtering logic. When you need more control, a combination of COUNTROWS, FILTER, and ISBLANK is your go-to solution.

This approach might seem a little more intimidating at first, but it unlocks a lot more power and flexibility.

• ISBLANK(): This is a simple query function that assesses a value and answers "TRUE" or "FALSE," making it an ideal engine for filtering data.
• FILTER(): This function gets a table, applies a filter, and returns a refined list of rows - only those that satisfy conditions defined within its rules.
• COUNTROWS(): This part finishes the work by tallying the number of lines coming out from the filtered virtual table. It counts the rows within the prepared section above.

How to Use COUNTROWS and ISBLANK

Imagine we have a 'Customers' table and want to count how many records are missing a 'PhoneNumber'. This is a critical piece of data, and we need to quantify how big the problem is.

Step 1: Create a New Measure In the 'Report' view, right-click 'Data' and select "New Measure".

Step 2: Enter the DAX Formula This is our code snippet:

NullPhoneNumber = COUNTROWS(FILTER('Customers', ISBLANK('Customers'[PhoneNumber])))

Let's crack the workings inside this expression:

• First, you name the measure (NullPhoneNumber), which equals the outcome of the calculation.
• Then FILTER('Customers') tells it to look specifically at your customers and scan columns related.
• ISBLANK('Customers'[PhoneNumber]): This scans through each line, checking for a blank "PhoneNumber". TRUE indicates no entry is available, FALSE points to something registered on record.
• In the final phase, all selected empty rows go for counts (COUNTROWS). It gives you a number representing missing phone number records from the customers' info table.

Visualizing Your Blanks to Gain Deeper Understanding

Merely knowing numbers is part of the job done. Visual aids bring context and gravity. A number without contrast looks isolated - like an incomplete tale. To show context surrounding blank values, you could present the following style formats:

• Card: This is the simplest, most straightforward option displaying an aggregate total number instantly. Just drag the measure onto your 'Card' visual.
• Donut/Pie Chart: Provides a percentage breakdown illustrating the proportion of records with vs. without blank items. To create it, use the calculated column formula HasPhoneNumber = IF(ISBLANK([PhoneNumber]), "No", "Yes"). Now drop it on the legend and in the values of the graph section for effective representation, and you'll observe a clear division.
• Tables or Matrix: This is a great option when doing some hands-on cleaning. It shows individual row details where specific gaps are present, allowing for quick verification. Drag "Customer ID" (or other primary key) and relevant data fields, then filter by your calculated column, focusing on the "No" categories for a precise result.

A More Advanced Scenario

The true power of this combo shines when you start adding conditional logic. Let's say we want to find out how many orders from 'last month' were without any given special code discounts. With a small edit inside the original formulation, you can perform wonders using CALCULATE().

BlankDscntInLstMonth = CALCULATE( COUNTROWS( FILTER('Orders', ISBLANK('Orders'[DscntCode]) ) ), 'Orders'[Date] >= TODAY() - 30 )

CALCULATE() tweaks a regular setting and restricts evaluation specifically for the last thirty-days period, giving a laser-targeted outcome based on what your query required.

Troubleshooting Common Hitch Points: "Empty versus Blank Strings"

Before wrapping up, here's one practical point for those using Power BI. In DAX, BLANK() does not equal an empty text string (""). They appear similar in most views, however, for purpose calculations and functions, they behave totally differently, which can create confusion.

• The function COUNTBLANK along with ISBLANK solely checks for true blanks. They simply won't detect empty text string cases.
• Solution: Either create a new column where all empty text is converted to blanks, or you could enhance the measure itself. For handling both scenarios, you would require some adjustments.

CountMissingNotes = COUNTROWS( FILTER( 'Tasks', 'Tasks'[Notes] = "" || ISBLANK('Tasks'[Notes]) ) )

Using a double pipe character ||, which acts like an "OR condition," allows you to search simultaneously for empty strings or true blank cases within the same field and deliver a much more comprehensive number output, ensuring everything is covered.

Final Thoughts

Counting blank values in Microsoft Power BI is a foundational data cleaning practice crucial for having reliable, insightful stories to tell through graphics. Whether it's for a quick check using the COUNTBLANK() function or utilizing a multi-functional combination of FILTER, ISBLANK, and other DAX functions, getting a handle on your data inconsistencies will always give clearer, better insights.

Of course, manually typing DAX logic is just one element of the overall analysis journey. At Graphed, we aim to simplify this process. You can easily connect different data sources within moments. Just throw a question via simple conversational text like "Show total salespeople with no entry," and our system will bring the required numbers immediately. You'll waste less time with tricky code logic and spend precious hours implementing better actions.