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How to Select All Fields in Power BI

Cody Schneider••7 min read

Trying to quickly display every field from a data table in a Power BI visual can feel strangely difficult. You look for a "select all" checkbox that seems like it should be there, but it's nowhere to be found. This guide gets straight to the point, showing you the fastest way to get all your columns into a view and, just as importantly, explaining when you should - and shouldn't - use this approach.

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The Obvious but Slow Way: Manually Checking Every Box

Before we jump into the fast solution, let's acknowledge the method everyone tries first. You head over to the Data pane (previously called the Fields pane), find the table you want to use, expand it, and start clicking the checkbox next to every single field name. One. By. One.

If your table has five columns, this is no big deal. But if you're working with a table from a CRM or an e-commerce platform that has 50, 75, or even over 100 fields, this process is incredibly tedious and prone to error. You might miss a field, your wrist will start to hurt, and you'll be left wondering why such a basic feature is missing. While you can do this, it's absolutely not the most efficient way to work.

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The Fastest Method: Dragging the Entire Table onto the Canvas

Here's the trick you’re looking for. The quickest way to display all columns from a single table is to drag the entire table directly onto your report canvas.

This simple action tells Power BI you want to create a new visual that includes every column from that data source. It’s perfect for creating a raw data table for quick analysis or validation.

Follow these steps:

  1. Make sure you have a blank area on your report page.
  2. In the Data pane on the right, locate the data table you want to use. You're looking for the parent name of the table, the one with a small grid icon next to it.
  3. Click and hold on the table's name.
  4. Drag it over the blank report canvas and release the mouse button.

Instantly, Power BI will create a new Table visual on your report and populate it with every single column from the table you dragged. No manual check-boxing required.

Example: Let's say you have a 'Shopify Orders' table loaded into your Power BI model. Instead of clicking 'Order ID', 'Customer Name', 'Order Date', 'Product SKU', 'Quantity', 'Price', and so on, you can simply find 'Shopify Orders' in the Data pane, drag it onto the canvas, and all those fields will appear in a neat table visual automatically.

Managing Which Fields Are Available in the First Place

While dragging and dropping is great for your report view, the best practice starts before you even reach that stage. A powerful Power BI principle is to only load the data you actually need. Including dozens of unnecessary columns in your data model can slow down your report's refresh speed and performance.

This is where Power Query Editor comes in. It’s the data transformation layer of Power BI, and it's the right place to be selective about your data from the start.

Using 'Choose Columns' in Power Query

Before you load your data into the model, get in the habit of trimming it down in Power Query. Here's how:

  1. From the Home ribbon in Power BI Desktop, click Transform data. This opens the Power Query Editor.
  2. In the Queries pane on the left, select the table you want to modify.
  3. Navigate to the Home ribbon within Power Query and click the Choose Columns button.
  4. A dialog box will appear with checkboxes for every column in your data source. This is your chance to un-check any fields you know you'll never need for your analysis. For example, columns like 'note_attributes', internal IDs, or timestamp fields you don’t plan to use.
  5. Click OK. Power Query will now add a "Removed Other Columns" step.
  6. Once you're done, click Close & Apply.

By doing this, you're building a leaner, more efficient data model. The fields you removed won't even appear in your Report view, decluttering the Data pane and improving performance. This proactive step means that when you later use the "drag and drop table" method, you're only pulling in the "all fields" that are actually relevant.

A Word of Caution: Performance and Usability Concerns

Just because you can select all a table’s fields doesn't mean you always should. Displaying tables with a huge number of columns can negatively impact both the report's performance and the user's experience.

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The Performance Hit

Every time you add a field to a Power BI visual, Power BI has to generate and run a DAX query in the background to retrieve that data. A table visual with 5 fields runs a relatively simple query. A table with 75 fields runs a much more complex query, which requires more processing power and memory.

If your report feels sluggish or takes a long time to load, oversized tables are often a primary culprit. In many cases, it’s far better to create multiple, focused visuals with fewer fields than one giant table as a “data dump.”

The User Experience Aspect

Visualizations are meant to provide clear, digestible insights. A well-designed bar chart comparing sales by region tells a story at a glance. A table with 80 columns doesn't tell a story — it’s just a wall of data that’s overwhelming to navigate and impossible to scan for insights.

Before adding all fields, ask yourself: “What question is this visual supposed to answer?" Build your charts, matrices, and tables with only the necessary data to answer that specific question clearly.

So, When Should You Use the 'Select All' Method?

With those warnings in mind, there are several legitimate scenarios where dropping an entire table into your report is the right move.

1. Initial Data Validation and Exploration

When you connect to a new data source for the first time, it’s a great idea to view all the fields in a raw table format. This helps you:

  • Confirm that all the data has loaded correctly.
  • Check data types and formats (e.g., are dates formatted as dates? Are numbers formatted as numbers?).
  • Get a feel for the data you have available before you start modeling and building visuals.

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2. Creating a Detailed View for Exporting

Sometimes, business users need a way to see the raw, underlying data behind a chart. A common design pattern is to have a primary dashboard with summary visuals, and then a separate drill-down page or a pop-up with a detailed table. Users can go to this page, apply filters, and then export the detailed data to Excel using Power BI's built-in export features. The drop-and-drag method is perfect for quickly creating these "data export" tables.

3. Debugging Your Data Model or DAX Measures

If a relationship in your model isn’t working as expected or a DAX measure is returning a strange result, it can be extremely helpful to pull the raw tables into a visual. Placing two related tables side-by-side on your canvas can help you spot missing keys, formatting mismatches, or (Blank) values that are causing your model to break.

Final Thoughts

To sum up, the fastest way to add all fields to a visual in Power BI is to drag the entire table from the Data pane onto the report canvas. This automatically creates a table visual for immediate data exploration. However, remember to use Power Query to remove unneeded columns upstream and use this "select all" approach thoughtfully to maintain report performance and clarity.

Wrangling columns and manually building reports in BI tools can often feel like you're fighting the software. Here at Graphed , we aim to eliminate that struggle entirely. We built Graphed so you can create dashboards and get insights simply by asking questions in plain English. Imagine asking, “Show me a table of all Shopify orders from last month,” and our AI generates it for you in seconds, automatically connected to your live data. It turns hours of clicking into a 30-second conversation, letting you focus on answering questions, not building reports.

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