By cleaning your data regularly then you may escape from many hurdles. It is essential to clean your data and help to maintain the ability of quick data analysis. Most people work with Power BI. In this blog, we will tell you how to clean your data in Power BI.
Before we describe the process of data cleaning, let’s create a situation where you imported all your data in Power BI from various sources to analyze. Still, when you examine the data, you find that it has some errors and can’t be analyzed. Then you must decide to clear that unprepared data. Before we start this process, let us know why data is spontaneous and can’t be analyzed.
During the examination of data, you may find many errors that are given below:-
- The Column of Employment Status only consists of numerical values.
- Many columns have an error in that.
- Null values in many columns.
- In columns, there are duplicated customers IDs.
- There are street addresses, city, state, and zip codes added in single columns.
Along with the errors when you decide to create reports and visuals, you get wrong results, insufficient data, and incorrect reports every time. These inaccurate data may irritate you, and due to this uneasiness, the user decides to make the correct data so that you can quickly examine and analyze it.
Luckily, when you are using Power BI, you are granted an excellent facility to clean and reanalyze your data using powerful technology.
There are many advantages of cleaning the unusual data in Power BI that are given below:-
- You get the most accurate and organized results and calculations.
- The table becomes more organized where the user can find the exact data that he wants.
- The duplicate files are removed, and the analysis of data becomes more straightforward.
- The produced columns can be used in slicers and filters.
- By cleaning the unusual big data, you will divide the complex data into two simple columns.
- You can add multiple data and combine them into readable form.
Let’s start learning the steps of cleaning the data in Power BI.
Data Import
Data import is the very first step of data cleaning. First, click on the Get Data from Data tab to choose from File and second from Workbook in the menu. There will be a file menu on the screen to navigate the Excel file to import.
After choosing the File that will import will appear with the Navigator window that allows you to select the sales sheet from the Workbook that is laced with the imported data.
While selecting the data, you can see a preview of the data on the right side.
If your data has more than one-sheets that are going to import, click on selecting multiple items.
Now you can choose between the Load and Edit options according to you.
You will clean your data, then click on the Edit button and open it in the Query editor.
Power Query Editor
In this editor, we can start our data transformation to give it a proper tabular format.
Whatever transformation you make in the data, it will appear in the Applied step area. You can click on any steps in the list.
An X step is used to delete the step and an edit option to edit the Column on the right side.
Data in its Column
In this Column, we associate the transforming data in their Column.
We can use this to split our data. To break the data, click on the right option of the Split Column and then choose By Delimiter’s option.
Now select the option of Colon from the delimiter list and press OK.
Now a column with dates is created with the null values till the next date.
Now click on the right side of the new Column and select Fill and then Down.
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Split Name and Employee Number
This step is to split the name of the salesperson and their employee. We will use a delimiter to break this data.
Now select the Split Column then By custom delimiter and the option OK.
Now click the right Column and click on Replace value to remove the remaining pathogenesis.
This will remove our data.
Now we can remove the useless space in data. Click on the Transform and Trim to cut the useless space.
Remove Unnecessary Rows
To remove all the unnecessary rows in one, select click on the Power BI filter icon present on the right side, remove the unnecessary rows and rename our remaining data.
Rename the columns
To rename the data, double click on the selected Column and rename that Column.
Unpivot the region columns
This is the last step of data cleaning. Select the data by holding Ctrl and keep clicking on each heading.
This will give you Attributes and Values. You can also rename these columns.
Now you can change your data by clicking the data type icon on the left and selecting the Currency.
Closing and Loading
To save your data, press the Close and Load option from the home page of the query editor.
When you click on the top, data will be loaded in an Excel tab.
Conclusion We use essential points to explain the process of data cleaning in Power BI. For convenient sharing and collaboration on interactive data visualizations, take a look at our Power BI Pro Consulting Services.
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