What Do Data Queries Do in Power BI?

A great Power BI report begins long before you drag your first chart onto the canvas. It starts with your data, and more specifically, with how you bring it in and prepare it for analysis. This is where data queries come into play - they are the powerful, behind-the-scenes engine that turns messy raw data into a clean, reliable source for your visualizations. This article will walk you through what data queries do, why they are so important, and how you can use them to build better reports.

What Exactly Is a Data Query?

Think of a data query as a detailed recipe for your data. Your raw data source (like an Excel file, a database, or a SaaS platform) is the grocery store full of ingredients. A data query is your shopping list and prep instructions. It tells Power BI exactly which ingredients to get, how to clean them, and how to combine them.

More technically, a data query is a set of transformation steps applied to your data source. These steps are executed in order every time you refresh your report. It's a recorded, repeatable process for:

• Connecting: Establishing a link to the data source.
• Selecting: Choosing which tables or columns you need.
• Filtering: Removing rows that aren't relevant to your analysis.
• Cleaning: Fixing errors, filling in blanks, and standardizing text.
• Shaping: Changing the structure of your data, like unpivoting columns or merging tables.
• Enriching: Creating new columns based on existing data (e.g., calculating profit from sales and cost columns).

Each time you connect to a new data source in Power BI, you are inherently creating a query. The magic happens inside the Power Query Editor, where you define all these shaping and cleaning steps.

Why Queries are the Foundation of Your Power BI Reports

Spending time on your queries might feel like pre-work that delays getting to the fun part of building dashboards, but it's the most critical step for several reasons. Rushing this stage often leads to headaches and unreliable reports down the line.

The "Garbage In, Garbage Out" Principle

If your underlying data is messy, your visualizations will be misleading at best and downright wrong at worst. Imagine trying to create a sales chart where your date is sometimes formatted as "2-2024" and other times as "January 10, 2024." Power BI won't be able to treat these as dates correctly. A query fixes this by standardizing the date column into a single, consistent format before it's loaded into the report, ensuring your analysis is accurate.

Improved Performance and Speed

Your raw dataset might contain hundreds of columns and millions of rows, but you probably only need a fraction of that for any given report. A well-constructed query only brings in the data you absolutely need. By removing unnecessary columns and filtering out irrelevant rows at the source, you dramatically reduce the size of your data model. This makes your Power BI report faster, more responsive, and easier to work with.

Automation and Repeatability

The biggest benefit of the Power Query system is that your data cleaning process is automated. You set up the steps once, and Power BI remembers them forever. The next time you get an updated sales file, you don't need to manually delete the same columns or fix the same formatting errors. You just hit the "Refresh" button, and Power BI runs through every step in your query automatically, applying all the transformations to the new data. This turns a tedious weekly task into a single click.

Meet the Power Query Editor: Your Data Transformation Hub

All of this data shaping happens in a separate window called the Power Query Editor. You can access it by clicking the "Transform Data" button after you connect to a data source, or by clicking "Transform Data" on the Home ribbon in the main Power BI Desktop window.

The editor interface can feel a bit intimidating at first, but it’s logically divided into a few key areas.

1. The Ribbon

Similar to other Microsoft products, the ribbon at the top contains all the available data transformation tools. These are grouped into logical tabs like Home, Transform, and Add Column. This is where you'll find buttons for common tasks like filtering data, splitting columns, or changing data types.

2. The Queries Pane

Located on the left side, this pane lists all the queries in your report. Each query corresponds to a distinct data source or table you're bringing into your model. You can click on each query to see its data and modify its transformation steps.

3. The Data Preview

The main area of the window shows you a preview of your data as it looks after the currently selected transformation step. This live preview is incredibly helpful because you can see the immediate effect of any change you make, whether it's removing a column or replacing a value.

4. Applied Steps

This pane on the right is the heart of your query. It records every single transformation you apply to your data as a distinct, named step. You can see a list like:

• Source
• Navigation
• Filtered Rows
• Changed Type
• Added Custom Column

This is your data recipe, written out step-by-step. You can click on any previous step to see what the data looked like at that point in the process. You can also reorder steps, delete them, or edit them using the gear icon that appears when you hover.

Common Data Query Tasks in Power Query

Let's look at a few practical examples of the transformations you'll be applying in the Power Query Editor.

Filtering Rows

Your dataset may include global sales, but your report is only focused on North America. Instead of loading all the data, you can filter the 'Country' column to only include "USA," "Canada," and "Mexico." This is done by clicking the dropdown arrow on the column header, just like in Excel.

Choosing and Removing Columns

A typical data export from a tool like Shopify or Salesforce can have 100+ columns. If you only need columns for OrderID, SaleDate, SalesRep, Revenue, and Region, you can use the "Choose Columns" feature to deselect everything else. This radically simplifies your dataset and improves performance.

Changing Data Types

Power BI is pretty good at guessing data types, but it's not perfect. A common issue is a numeric column like 'Postal Code' being incorrectly read as a whole number (mistakenly thinking it's something you'd add or average). In the Power Query editor, you can right-click the column header and explicitly set its type to Text to prevent unwanted mathematical operations.

Replacing Values

Inconsistent data entry can plague reports. You might have a 'Status' column with entries like "Complete," "COMPLETED," and "Finished." Your query can fix this by replacing all variations with the single, standardized term "Complete." This ensures that when you create a visual, all entries are categorized properly.

Creating Custom Columns

Sometimes the data you need doesn't exist yet. The "Add Custom Column" feature lets you create a new column from existing data. For example, if you have columns for 'Sales Amount' and 'Unit Cost,' but not for 'Profit,' you can write a simple formula such as:

[Sales Amount] - [Unit Cost]

This will create a new, calculated 'Profit' column. Performing this kind of calculation at the query level is much more efficient than doing it in a DAX measure later on.

Final Thoughts

Data queries are the unsung heroes of Power BI. They transform raw, unstructured data into a clean, reliable foundation for your reports. By learning to master the Power Query Editor, you take control of your data preparation process, ensuring that your reports are both accurate and performant.

Learning Power Query is a valuable skill as data analysis evolves. At Graphed, we recognized this and developed our platform to simplify complex processes. Instead of clicking through menus to clean or filter rows, our intuitive AI assists you in simplifying data transformation, making data preparation a seamless experience.