Do you know if your data is unstructured or unmanaged while importing to Power BI, either it may give an error or may not deliver inaccuracy in your reports and analytics? Here, the need for Data Cleaning in Power BI takes place.
So, this blog will give a complete overview of how to perform data cleaning and data transformation in Power BI to get accurate and actionable data to support the decision-making process within your organization.
Why Data Cleaning in Power BI
Power BI enables everyone in your organization to make instant and growth-driven decisions with its powerful visualization and data analytics. When your data is irrelevant or confined at any level, this will not help to develop accurate reports. Eventually, no users will be able to make productive decisions with compromised data or reporting capabilities.
Now, let’s move forward and understand the ways to transform the data in Power BI:
Ways to Clean & Transform the Data In Power BI
Data Transformation Through Power Query
When it comes to cleaning and structuring your data, all you need to start by selecting ‘Transform Data’ on the home page of your Power BI desktop. To understand visually, a reference image is given below:
Source Image Source
Whatever queries you apply, the leveraged data in your select query will be demonstrated in the middle part of the Power Query Editor and available queries can be easily seen in the queries setting pane. All the data changes and steps are taken to transform the data are seamlessly recorded in the Query Setting Pane.
Data Structuring for Column Headers & Names
Now, the next step is to check whether the Column Headers & Names are in a structured manner or not. You are required to evaluate your data and start transforming accordingly. Make sure that each column is at the correct place. Let’s understand it with sample images. Here we have two images. One is a CSV file – a data source and another is Power BI.
The first image is showing sample data for sales targets wherein you can see product categories along with sub-categories for months. So, the result is in front of you. It’s clearly visible how data is imported in case you have not managed column headers and names in the second image. It’s not imported as anticipated. You can see, it’s difficult to even understand the data, forget about analyzing it. Here you need to reorganize your data.
Removing Irrelevant Rows
When you drive data cleansing to get accurate Power BI reports, there could be blank or meaningless rows that are not required for your reports. For more clarification, consider the sample image. You can see it contains a blank row below the column header, which can be removed by applying Power Query:
Home tab-> ‘Remove Rows’-> ‘Remove Top Rows.
There is another row that contains data, if you think that data is not relevant for your reporting visuals, simply remove them using the same query mentioned above.
Similarly, for column data, you can remove that too.
‘Home tab-> ‘Remove Columns’-> ‘Remove Columns’.
Renaming the Columns
Next, you need to check column headers. Make sure that no column contains wrong headers, misspelled data, or compromising in the header name series.
Now, let’s understand how to solve this part through the below-given image. It seems irrelevant when column names are column1, column2, and subsequently. Here, we need to correct the column names by selecting ‘Use First Row as Headers.
To rename this column header, you are required to right-click the header-> select ‘Rename’-> ‘edit’ the name->press ‘Enter’.
After applying the Power Query, here is how it will look:
Unpivoting the Columns
Unpivoting is the essential feature in Power BI for data cleansing. This feature can be used while importing your data from your data sources especially when it comes to importing data from your Excel files.
For Example, we have data in Excel File. See reference image:
Now, when this data is imported to Power Query, see how it looks in the given image:
The above image is showing a year-wise sales report. Now, if you want data preparation demonstrating the sales report combining both the years, simply rename the first column to month. Thereafter, you need to highlight the year 2018 and 2019 columns. Lastly, select the ‘Transform’ tab and then ‘Unpivot’.
To prevent data issues and to achieve accuracy in your reports, data preparation is always required. Once you are done with data sanitization, your system is ready to deliver actionable insights helping your workforce to make timely and productive decisions. In this blog, we covered all the basic tactics in terms of Power BI data cleansing. We hope this blog will help you adequately cleanse and structure your data. Further, we are always ready to provide you with more relevant information for your business. Contact Dynamics Square to avail of Power BI consultation, Implementation, or maintenance services.
See Also – Power BI Architecture and Its Components Explained