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Understanding Data Types in Power Query (Power BI & Excel)

In this blog post, we delve into the world of data types in Power BI and Excel, their importance, and how to manage them effectively.

Note

screenshots of Power Query in this blog are from inside Power BI, but the user interface in Excel is similar.

Data analysis and visualization are essential aspects of modern business intelligence, and Power BI has become a popular tool for handling these tasks efficiently. One critical factor that significantly impacts the performance and accuracy of your Power BI reports is the data types assigned to your columns in Power BI or Excel.

Why Data Types Matter in Power BI

The correct data types in Power BI and Excel are crucial for proper analysis and visualization. When you load data into Power BI Desktop, the software automatically attempts to convert the source data into the most efficient data types for storage, calculations, and visualizations. Using the right data type not only ensures better performance but is also crucial for accurate calculations using Data Analysis Expressions (DAX) later in your report.

Let’s understand the importance of data types in Power BI and Excel with an example. Suppose you have a date column, but Power BI has assigned it a text data type. If you try to create a DAX measure that requires a date data type, it will not calculate properly. Correct data types are also essential for optimal report performance. For instance, if you assign a decimal data type to a column that only contains whole numbers, it will use more storage space unnecessarily.

Exploring Data Types in Power BI

Power Query is extremely flexible in the number of data types it can handle.

Decimal Number

The most common number type, and can handle numbers with fractional values and whole numbers.

Percentage

Represents Decimal Number, which is displayed in the Percentages format. Range from 0 to 1.

Whole Number

Represents a 64-bit (eight-byte) integer value. Because it's an integer, it has no digits to the right of the decimal.

Date/Time

Represents both a date and time value. The underlying value can be converted between the two types.

Text

A Unicode character data string, which can be letters, numbers, or dates represented in a text format.

Time

Represents just a time with no date portion. A Time converts into the model as a Date/Time value.

There is a wide variety of data types in Power BI and Excel beyond the ones presented above, including Binary and True/False data types. For a comprehensive list of data types in Power BI and how to use them effectively, check out this article from the Microsoft Power BI Documentation.

When you load your data into Power BI, you can easily see the data types assigned to each column. Power BI displays small icons at the top left corner of each column in the Power Query Editor, indicating the data type. By clicking on these icons, you can explore and understand the assigned data type.

Data type icons in Power Query

Changing Data Types in Power BI

It’s essential to ensure that Power BI has correctly assigned data types to each column. If needed, you can easily change the data types in a few simple steps.

Method 1: Changing Data Types from Data Preview

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Method 2: Changing Data Types from the Home Tab

Method 3: Detecting Data Types

In some cases, Power BI might not automatically detect the correct data type for a column. In this case, you will see an “Undefined Data Type” icon – which will display as ABC 123 – next to the column name. You can use the Detect Data Type option to help Power BI understand the data.

The Wrap Up

Data types play a crucial role in Power BI’s performance, accuracy, and report creation. Ensuring that the correct data types are assigned to each column in your datasets will lead to better overall performance and accurate calculations. Power BI provides multiple ways to change data types, giving users the flexibility to manage their data effectively.

Remember to review and validate data types before loading your data into Power BI to avoid any unexpected issues in your analysis and visualization journey.

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