What Does Remove Errors Do in Power BI?

Encountering random errors in your Power BI data can be frustrating, especially when they stop your entire report from refreshing. Tucked away in the Power Query Editor is a tempting one-click solution: "Remove Errors.” While it sounds like the perfect fix, using it can silently corrupt your analysis without you ever realizing it. This guide explains what the 'Remove Errors' function actually does, why it can be dangerous, and offers much safer alternatives for cleaning your data.

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What Causes Errors in Power BI?

Before fixing errors, it helps to know where they come from. Inside the Power Query Editor (your data cleaning workshop in Power BI), errors are typically signposts pointing to a conflict between what you have and what Power BI expects. When a step in your data transformation can't be completed, Power Query will flag the specific call with an "Error" value.

Most issues fall into a few common categories:

• Data Type Conversion Failures: This is the most frequent culprit. It happens when you try to force data into the wrong mold. For example, trying to convert the text value "Sold Out" into a number or a piece of text like "January 5th, Two Thousand Twenty-Four" into a valid date format. Power BI columns like consistency, and a failed conversion will create a specific cell-level error.
• Invalid Mathematical Operations: These are errors from calculations that don't make sense, like dividing a number by zero. You might also see this if you try to perform a calculation on a combination of numbers and text (e.g., trying to add 100 + "Not Available").
• Empty or Null-Related Problems: A null value means there is no data. While nulls themselves aren't errors, they can cause errors if they're used improperly in subsequent formulas or calculations.
• Faulty M Code: If you're writing custom columns or steps using Power Query's M language, a typo, incorrect logic, or a reference to a non-existent column can generate an error.

You can easily spot these errors in the Power Query Editor. They appear as yellow cells with a lightning bolt icon and the word "Error" in italics. Clicking next to the word (but not on it) will show you a detailed error message at the bottom of the screen, explaining what went wrong.

What 'Remove Errors' Actually Does (And Doesn't) Do

Here's a critical point that many Power BI beginners miss: the "Remove Errors" function is not a sophisticated repair tool. It doesn't analyze the error, find the root cause, or try to fix the incorrect data. Its method is simple and brutal.

When you click "Remove Errors" on a column, Power Query scans every cell in that column. If it finds even a single cell containing an "Error" value, it deletes the entire row associated with that error.

Think about it like this: Imagine you have a spreadsheet of 1,000 online orders. One of the columns, Shipping_Cost, has a data entry typo for a single order, causing a calculation error. Using "Remove Errors" doesn't just clear the bad Shipping_Cost value - it deletes the entire record for that online order. The customer info, the purchase date, the product sold, the revenue... everything is gone. The tool doesn't fix the mistake, it eradicates all evidence of it.

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How to Use Remove Errors in Power Query

Using the function is straightforward, which is part of its appeal and its danger. It lives inside the Power Query Editor, which is where all data transformation takes place before it gets loaded into your Power BI report model.

Step 1: Open the Power Query Editor

From the main Power BI Desktop window, go to the Home tab on the ribbon and click Transform Data. This will open the Power Query Editor in a new window, showing you all of your imported data queries.

Step 2: Select the Column(s) with Errors

In the data preview grid, find the column that contains the error values. You'll often see a colored bar under the column header indicating the quality of the data, with red representing errors. Simply click the column header to select it. You can also select multiple columns by holding down Ctrl as you click each one.

Step 3: Locate and Click 'Remove Errors'

With the column selected, you have two primary ways to find the option:

• From the Ribbon: Go to the Home tab, find the Reduce Rows group, click the dropdown for Remove Rows, and then select Remove Errors.
• With a Right-Click: Simply right-click on the header of the selected column, hover over Remove Errors, and click it from the context menu.

Once you click it, the rows containing errors in your selected column will instantly disappear. Power Query adds a new step to your "Applied Steps" pane on the right called "Removed Errors." You can click the 'X' next to this step to undo the action if you make a mistake.

When You Shouldn't Use Remove Errors

Quick fixes are tempting, but using "Remove Errors" can have serious consequences on the reliability of your reports. It’s generally a bad practice unless you are 100% sure that the rows containing errors are completely useless and must be removed.

1. You Are Silently Losing Important Data

This is the biggest risk. Removing an entire row just because one column has an error means you're throwing away all the other perfectly good data in that row. If 2% of your sales records have a calculation error in a Tax column, running "Remove Errors" wipes out 2% of your sales from the dataset. Your "Total Revenue" visuals will now be inaccurate, but there will be no warning or indication that data is missing. Your numbers will look correct, but they will be fundamentally wrong.

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2. You're Hiding the Real Problem

Data errors are valuable clues. They are symptoms of a deeper problem, often pointing to issues in your source data, data entry processes, or integration logic. For example, a persistent error in a "Sale Date" column might reveal that a particular user is entering dates in an incorrect format. By simply deleting the error, you're masking this underlying problem. It's like turning off a fire alarm instead of putting out the fire. The problem will continue to happen, and you'll never fix it at the source.

3. It Can Create Inconsistent and Unreliable Reporting

If your source data changes regularly, using "Remove Errors" can lead to inconsistent results. One day, your dataset might have 10,000 rows. The next, after a new set of data with more errors comes through, it might drop to 9,500. This variability makes it hard to trust trends over time. A drop in sales might not be a real business trend but rather an increase in data entry mistakes that are being quietly deleted.

Smarter Ways to Handle Errors in Power BI

Instead of deleting data, the best practice is to handle errors gracefully, preserving as much information as possible while isolating the root cause.

Alternative 1: Replace Errors

The "Replace Errors" function is a far safer alternative. Instead of deleting the entire row, it allows you to substitute the error value in a cell with a value of your choice, like null, 0, or even a text string like "Invalid Data."

How to use it: Right-click the column header, select Replace Values, then choose Replace Errors from the context menu or ribbon. In the dialogue box, enter the value you want to use as a replacement. For numerical columns, 0 or null is often a good choice. This keeps the row intact, ensuring other values from that row contribute to your report, while neutralizing the impact of the problematic cell.

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Alternative 2: Use Conditional Logic ('Try...Otherwise')

For errors that pop up during transformations (like a custom column calculation), you're better off building error-handling directly into your logic. In Power Query's M language, the try...otherwise expression is your best friend. It attempts an operation, and if that operation results in an error, it returns a fallback value that you specify.

For example, if you are creating a "Total Price" column by multiplying [Quantity] * [Unit_Price], some of the [Unit_Price] values might be text, causing an error. You could create a custom column with this logic:

try [Quantity] * [Unit_Price] otherwise null

This formula attempts the multiplication. If it succeeds, it returns the result. If it fails (hits an error), it returns null instead. This is a robust, proactive way to prevent errors from ever occurring in the first place.

Alternative 3: Keep Errors to Isolate and Investigate

If you're serious about data quality, your goal should be to investigate and fix errors, not hide them. A powerful technique is to isolate the problematic rows so you can analyze them. In the same dropdown menu as "Remove Errors," there's an option called Keep Errors.

Here's a great workflow for this:

1. Your query with your source data ("Sales Data," for example).
2. Duplicate your main query (right-click, select Duplicate).
3. Rename the new query something like "Data Quality Issues - Sales."
4. In this new query, select the column with errors and use Keep Rows > Keep Errors. This will filter the table to show only the rows causing problems.

Now you have a dedicated table listing all the bad records. You can examine them for patterns or even export this table to send to the team responsible for data entry so they can fix the issues in the source system.

Final Thoughts

Ultimately, "Remove Errors" is a blunt tool in Power BI that should be used with extreme caution. It deletes entire rows containing faulty cells, which can quietly skew your analysis and hide deeper issues in your data pipeline. For more reliable and accurate reporting, safer alternatives like "Replace Errors," conditional logic, or isolating error rows for investigation are almost always the better choice.

Dealing with unpredictable data errors and manual cleaning steps in different tools is precisely the kind of time-consuming frustration that slows down your ability to get answers. At Graphed, we automate the entire process by connecting directly to your marketing and sales sources. Instead of wrestling with data transformations, you simply ask questions in plain English, and our AI data analyst builds real-time dashboards and reports for you. This allows you and your team to focus on making decisions, not on cleaning data.