🎬Power BI: Unlocking Insights - Analyzing Credit Sales Data Using Power BI

Welcome to another Power BI tutorial! In this tutorial, we`ll dive into the world of analyzing and presenting credit sales data, specifically focusing on transactions with doubtful or bad debts.

We`ve got a robust dataset comprising over 14,000 credit sales transactions. This data includes essential details such as transaction ID, date, sales amount, days past due, and debt classification. Additionally, we`ve provided a second sheet that defines the various debt classifications and a third sheet that maps transaction IDs to store names.

Our journey begins by importing the Excel file using Power BI`s "Get Data" feature. In the Navigator window, select all the tables and press "Load," allowing the data to load. While most of the time, data loads correctly, sometimes issues may arise. In such cases, you can rectify them in the Query Editor by clicking "Use First Row as Header" for the affected tables.

Once your tables have the correct headers, click "Close and Apply," and you`ll have all the fields with proper names. Next, head to the Model view to establish table relationships. Fortunately, Power BI often creates these relationships automatically. However, if they`re missing, you can manually create a relationship between the Transactions and Stores tables based on transaction ID.

Now, switch to the Table view and open the Transactions table. Select the Classification column and create a new data group for all doubtful debt stages. This simplifies the classification, reducing it to three categories instead of five. This is how you can group data in Table view.

To calculate late charges, create a new column. Use the IF formula to assign zero charges to regular debts and calculate charges based on credit sales and days past due for non-regular debts. Now, you`re ready to proceed.

Before creating visualizations, format the amounts properly, using comma-separated numbers without decimal points. Now, switch to the Report view. Temporarily hide the filters and add a slicer for the transaction date, including the quarter field and convert it into a drop-down list with visual borders.

Further enhance your report by adding a text box visual in the top left corner with an appropriate title. Make it stand out by increasing the font size, turning on visual borders, and setting the font to bold.

Now, introduce a table visual to your dashboard, incorporating the Classification, Total, and Late Charges fields. Duplicate this table visual, placing it below the original, and change the Classification Group field to the Classification field. Both visuals can now operate independently without affecting each other.

To maintain this independence, disable interactions between them. With this setup, you can view bad debts during the specified period.

Rename your title to "Bad Debts" for clarity. Add a column chart, using the Store field from the Stores table on the x-axis and credit sales on the y-axis. Duplicate this chart and change it to a 100% stacked column chart.

Expand your dashboard with a scatter chart visual, plotting credit sales on the x-axis and late charges on the y-axis. Don`t summarize either field and turn on visual borders. Optionally, add the Classification field to the legend area and adjust its placement for readability.

Fine-tune the scatter chart for the best appearance. Finally, introduce a histogram, which you can import from an external file. Add it to your dashboard and configure it using the credit sales field for both values and frequency. Adjust the bin size and enable visual borders.

Your dashboard is now complete! Thank you for watching our tutorial; we hope you found it helpful. Don`t forget to like and subscribe for more tutorials and updates.

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