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Data cleaning in Power BI is an important factor for accurate data analysis and visualisation. Before getting into the precise steps of this process, let’s understand why it is necessary and how it can influence your decision-making process.
Businesses deal with structured as well as unstructured data that contains a lot of information about different processes across the organisation. Both forms of data can contain important information as well as unnecessary or inconsistent information.
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If you build reports using the available structured and unstructured data without properly preparing it, you may have to deal with redundant, incomplete, or incorrect data that has little or no use for your analysis.
So, how can you get rid of such data?
Data cleaning in Power BI is the answer that lets you refine, transform, and prepare data before using it for reporting and analysis.
Since enterprises have to deal with huge amounts of irrelevant as well as relevant data, businesses need to filter, prepare, and clean the data to make it useful for making informed decisions.
With refined data, you can build business reports for visual insights and statistical analysis using Microsoft Power BI.
After importing the data, it is important to refine or clean it before creating reports. Before getting into the steps of cleaning data, let’s understand its importance.
Importance of Data Cleaning in Power BI
Importing raw data into Microsoft Power BI isn’t enough to create reliable reports, as the data may contain errors, inconsistencies, duplicate values, empty values, or unnecessary information.
Once you import data from various data sources, you need to examine it before using it to create visuals and reports. During data analysis, you may have to deal with the following issues:
A column containing inconsistent or incorrect data types
Various columns with null values and errors
Duplicate entries in one or more columns
Incorrect demographic or categorical data in a particular column
Unnecessary rows or columns
Inconsistent column names or formats
If you process data with such existing errors, you may get reports containing incorrect values, mismatched information, inaccurate sales numbers, and other data quality issues.
With cleaned data, you can build more reliable reports that can improve your ability to make informed and actionable business decisions.
How to Clean and Transform Data in Power BI?
The process of cleaning and transforming data in Power BI Desktop is straightforward with Power Query Editor. Before transforming the data, follow these steps:
Open Power BI Desktop and click on the Transform Data button available on the Home tab.
The selected data will be displayed in the Power Query Editor.
You can see your queries in the Queries pane.
The transformations you apply are recorded as steps in Query Settings under Applied Steps.
When the query is refreshed, Power Query applies these transformation steps to the source data again.
Once you follow the above steps, it’s time to transform the data into a useful form.
Profile Your Data Before Cleaning
Before making transformations, you can use Power Query’s data profiling tools to understand the quality and distribution of your data.
Go to the View tab in Power Query Editor and enable Column quality, Column distribution, and Column profile. These tools can help you identify empty values, errors, distinct values, unique values, and unusual data patterns before you start cleaning the dataset.
By default, Power Query profiles the first 1,000 rows. If required, you can change the setting to profile the entire dataset.
Streamline Column Header Data
In this case, the first step is to identify and correct the column headers and column names while ensuring that each column contains the right information.
To help you understand better, we are considering two different files here:
A CSV file as a data source
Data imported directly from a data source with irregular column and name data
As you can see in the below image, the sample data is available for sales targets, categorised and sub-categorised by month.
As you can see from the image, the data is unstructured and difficult to understand in its current form.
Now, check the below image, and you will find columns without appropriate names, while the row below contains the required header information. By promoting the appropriate row to headers and removing unnecessary rows, you can create a cleaner data structure.
Delete Irrelevant Rows and Columns from the Spreadsheet
As you can see from the data in the above image, there are a few blank or irrelevant rows and columns that should be eliminated because they are not required for the analysis. Power Query allows you to remove these rows and columns easily.
To remove unwanted rows from the beginning of the dataset, go to the Home tab and select Remove Rows > Remove Top Rows.
Home > Remove Rows > Remove Top Rows
If you want to delete an unnecessary column, you can also do this in Power Query.
Select the required column and use Home > Remove Columns.
Where possible, remove or filter unnecessary data early in the transformation process. This reduces the amount of data Power Query needs to process in later steps and can improve query efficiency.
Change the Names of Columns
As we have seen in the irregular data in the previous example, some columns may not have meaningful names. You can change the column names to make them relevant to the categories and information they contain.
Select Use First Row as Headers when the first row of your dataset contains the correct column names.
Right-click the header that contains an irregular column name and select Rename.
Enter the required name and press Enter.
Using clear and consistent column names can make your Power Query transformations and Power BI data model easier to understand and maintain.
Check and Correct Data Types
Another important data cleaning step is checking whether each column has the correct data type. For example, sales values may need a decimal or currency-compatible numeric type, dates should use an appropriate date type, and descriptive information should normally use text.
Incorrect data types can cause calculation, filtering, relationship, and visualisation issues. In Power Query Editor, review the data type icon next to each column name and change it where required.
Handle Null Values and Errors
Null values and errors should be reviewed before the data is used in reports. The appropriate action depends on what the value represents and your business requirements.
Power Query provides options to remove, replace, or retain errors for further investigation. For example, if a text value appears in a numeric column, converting that column to a number can generate an error. You can investigate the source value and then correct, replace, or remove it where appropriate.
Do not automatically remove every null value or error. First understand why it exists and whether removing it could affect the accuracy of your analysis.
Remove Duplicate Data
Duplicate data can affect totals, counts, relationships, and other calculations in a Power BI report. Power Query allows you to identify and remove duplicate rows based on one or multiple selected columns.
Select the column or columns you want to check.
Go to Home > Remove Rows > Remove Duplicates.
Before removing duplicates, make sure the repeated records are genuinely unnecessary. Some records may appear similar but represent valid individual transactions or events.
Unpivot Columns
Unpivoting transforms data stored across multiple columns into attribute-value pairs, making certain spreadsheet-style datasets easier to analyse in Power BI.
You can use this transformation with data imported from different sources, but it is particularly useful when working with Excel files where values such as years or months have been stored as separate columns.
For example, if your dataset stores revenue for 2018 and 2019 in separate columns, you may want to restructure those columns to make reporting and analysis easier.
To do this, rename the first column to Month, select the 2018 and 2019 columns, go to the Transform tab, and select Unpivot Columns. You can then rename the resulting Attribute and Value columns to Year and Sales Amount, respectively.
Power BI Data Cleaning Best Practices
Along with the above data cleaning techniques, following a few Power Query best practices can make your datasets easier to manage and improve the reliability of your reporting.
Filter unnecessary data early: Remove unwanted rows and columns as early as practical so later transformations process less data.
Use the correct data types: Make sure dates, numbers, text, and other fields use appropriate data types.
Profile your data: Use Column quality, Column distribution, and Column profile to identify data quality issues.
Review errors before removing them: Understand why an error occurs before deciding whether to correct, replace, or remove it.
Check duplicates carefully: Confirm that duplicate-looking records are genuinely redundant before deleting them.
Use meaningful names: Give queries, columns, and important transformation steps clear names so that the Power Query solution is easier to maintain.
Conclusion
Data cleaning in Microsoft Power BI is important, and as the above information shows, it does not have to be complex. Power Query provides tools to clean, structure, profile, and transform data before it is used for analysis and reporting.
Depending on your dataset, data cleaning may involve correcting column headers, removing unnecessary rows, checking data types, handling null values and errors, removing duplicates, or reshaping data with transformations such as Unpivot.
It is possible that you may still have to deal with other data quality or transformation issues while preparing your data. In such cases, Dynamics Square UK can help with Power BI implementation, data integration, reporting, and optimisation requirements.
Contact Dynamics Square UK to discuss your Microsoft Power BI requirements.
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Frequently Asked Questions
Data cleaning in Power BI is the process of identifying and correcting issues such as duplicate records, incorrect data types, missing values, errors, inconsistent formats, and unnecessary rows or columns before using the data for reporting and analysis.
Power Query Editor is the primary tool used to clean and transform data in Power BI. It provides options for changing data types, removing duplicates, handling errors, replacing values, filtering data, renaming columns, and reshaping datasets.
Power Query provides Column Quality, Column Distribution, and Column Profile tools that can help identify errors, empty values, value distributions, and other characteristics of your data.
Correct data types help Power BI interpret values properly for calculations, relationships, filtering, sorting, and visualisation. Incorrect data types can result in conversion errors or inaccurate analysis.
