Clean and Transform data with Power BI Query

In this section 10, we are going to learn about the Cleaning & Transforming the data with Power BI Query || Automate Data Tasks in Power Query Editor.

• Why Do We Need To Clean / Transform The Data
• Live Demo Of Transforming The Data
• Best Practices For Naming Tables, Columns, And Values
• Evaluate And Change Column Data Types
• Change The Column Data Type in 2 Ways

Its very important to understand the way you clean & transform your data which will help you to do effective analysis and efficient decision making.

Having a incorrect or messed up data will cause ineffective decision making and in turn costs you.

Why Do We Need To Clean / Transform The Data:

• Measures and columns produce more accurate results when they perform aggregations and calculations.
• Tables are organized, where users can find the data in an intuitive manner.
• Duplicates are removed, making data navigation simpler.
• It will also produce columns that can be used in slicers and filters.
• A complicated column can be split into two, simpler columns.
• Multiple columns can be combined into one column for readability.
• Codes and integers can be replaced with human readable values.

Power Query Editor in Power BI Desktop allows you to shape (transform) your imported data.

To start shaping your data,  •open Power Query Editor -> by selecting the Transform data option on the Home tab of Power BI Desktop.

Live Demo Of Transforming The Data:

I have covered the below in the live demo video and also few tips to keep in mind while shaping your data.

• 1.Identify column headers and names 
• 2.Promote headers
• 3.Remove top rows 
• 4.Remove columns 
• 5.Remove Other Columns
• 6.Unpivot columns
• 7.Pivot columns
• 8.Replace values
• 9.Replace null values
• 10.Remove duplicates 

Hope you found the post informative. Your valuable feedback, question, or comments about this post are always welcome by leaving me message on  contact form is truly appreciated.

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