Cleaning your Data with Power BI

Dr. Francis Okoye recently exposed us to the Power BI and how the tool transforms data into business reports.  In taking us through the sessions, he emphasised that the first requirement to create high-quality Power BI reports, is to ensure that the data is cleaned up before it is used for analysis. Then, he walked us through how to correctly upload data into the tool and create our reports.

Microsoft describes Power BI as a unified, scalable platform for self-service and enterprise business intelligence (BI). It can connect to and visualize any data, and seamlessly infuse the visuals into the apps you use every day (https://powerbi.microsoft.com/en-us/what-is-power-bi/). The accuracy and how much reliance you can place on the reports depend on the quality of the data you use.

Here are some steps to cleaning up your Power BI data.

Step 1: Identify Problematic Data

Seek out the faulty data. This might involve missing, redundant, or improperly structured data. The Power Query Editor can help you find and resolve these problems.

Step 2: Cleanse your Data

This step would include fixing mistakes, removing duplications, and adding any missing data.  Duplicates in your data might bias your analysis and lead to inaccuracies. Using the Power Query Editor in Power BI, you may get rid of duplicates. You may choose which columns to use as criteria for finding duplicates and then decide whether to maintain only the first or final instance of each duplication. To do this, you may utilize the data cleansing functions in Power BI.

Step 3: Handle Missing Data

Missing data might pose serious issues for data analysis. In Power BI, you can manage missing data by either replacing it with a default value or by estimating missing values using an interpolation approach. In cases when rows with missing data only make up a tiny fraction of your data, you may also opt to remove them.

Step 4: Apply Data Validation Rules

Data consistency and accuracy are ensured with the use of data validation rules. By applying rules to your data, you may utilize Power BI’s data validation tools to make sure that it falls within a given range or that dates are formatted properly, for example.  Some of these rules include dataLength, dateRange, matchFromFile, patternMatch, range, reject, return and validateDBField.

Step 5: Integrate Data from Multiple Sources

When working with data from many sources, it could be necessary to integrate the data to get a coherent perspective. You may combine data from several sources, including databases, cloud-based applications, and Excel files, using Power BI. You may occasionally need to divide your data into many tables or integrate data from various sources. Using the Power Query Editor, Power BI lets you combine and separate data. You can split tables into numerous tables based on particular criteria or combine tables based on common columns.

Step 6: Test and Refine

After your data has been cleaned up and changed, you should evaluate it to ensure that it is correct and comprehensive. The data profiling tools in Power BI may be used to test your data and find any lingering problems. You may improve your cleaning and transformation procedures to solve difficulties after you’ve recognized them.

Step 7: Document Data Cleaning Processes

Finally, it is critical to record your data cleansing procedures so that others in your company can adhere to them. This involves recording the procedures you used to clean up your data, along with any data validation criteria and data transformation techniques you employed. Additionally, documentation makes it simpler to preserve data quality over time.

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