I Imported the Same Excel Sales Data Into Power BI 4 Different Ways: What Changed?
Most Power BI tutorials start with a clean Excel file and then explain how to create charts and dashboards.
I wanted to test something different.
What happens when the same Excel sales data is brought into Power BI in different ways?
Instead of only reading about Power BI, I decided to test it myself using the same sales data.
I tested the data in different situations, including loading the original Excel data directly, opening it through Power Query, converting the Excel range into an Excel Table, and then correcting some of the data before using it in Power BI.
I also created a simple dashboard from the corrected data.
I captured screenshots during each test so I could compare what I actually saw.
Note: This experiment was performed using my own test data and my own Power BI setup. Results may be different depending on the Excel file, Power BI version, data structure, and other factors.
The Excel File I Used for the Experiment
For this experiment, I used a small sales dataset containing information such as:
- Date
- Product
- SKU
- Salesperson
- Quantity
- Amount
I intentionally started with the original data instead of immediately cleaning everything.
The purpose was to see how Power BI handled the data before I made corrections.
[INSERT SCREENSHOT 1 HERE]
Figure 1: The original Excel sales data used for my Power BI experiment.
Test 1: Loading the Original Excel Data Directly
For my first test, I imported the Excel file into Power BI without making the basic corrections first.
I wanted to see what the data looked like when I brought it into Power BI in its original state.
I used the Excel connector and loaded the data.
After loading the data, I checked the table inside Power BI.
What I Observed
The data was available inside Power BI, but the original Excel file contained some things that I wanted to investigate further.
For example, the original dataset contained a blank row and an inconsistent salesperson value.
This made me curious about what would happen if I looked at the same data through Power Query.
Test 2: Opening the Same Data in Power Query
For the second test, I used the same Excel file but opened it through Transform Data instead of immediately loading it.
This opened the data in Power Query.
I did not immediately clean the data. First, I wanted to see how the original information appeared inside Power Query.
Figure 2: The same Excel data opened in Power Query before making corrections.
What I Observed
Power Query gave me a separate environment where I could inspect and transform the data before sending it to the Power BI model.
This was different from simply loading the data and working with it afterward.
It made it easier to see where I could make changes before creating visuals.
Test 3: Converting the Excel Data Into a Table
For the third test, I changed the structure of the Excel file.
Before importing it into Power BI, I converted the data range into an Excel Table.
I then saved the file and imported it into Power BI again.
Figure 3: The Excel data converted into a Table before importing it into Power BI.
What I Observed
Power BI recognized the Excel Table as a separate table available for import.
This gave me another way to prepare the same dataset before bringing it into Power BI.
The important point for my experiment was that I was still using the same sales information. The difference was how the Excel data was structured before Power BI received it.
Test 4: Correcting the Excel Data
For the fourth test, I created a corrected version of the dataset.
I made some basic corrections to the original data, including:
- Removing the blank row
- Correcting the inconsistent salesperson value
- Keeping the data structure more consistent
I then imported the corrected data into Power BI.
Figure 4: The corrected Excel data imported into Power BI.
What I Observed
The corrected dataset was easier to work with because the basic data issues had already been addressed.
This was an important part of my experiment because it showed me that the quality of the source data matters before creating reports.
Test 5: Creating a Simple Power BI Dashboard
After preparing the corrected data, I used it to create a simple dashboard.
I kept the dashboard simple and created three main visuals:
- Total Sales
- Sales by Product
- Sales by Salesperson
Figure 5: My simple Power BI dashboard created from the corrected sales data.
What I Observed
Once the data was prepared, creating basic visuals was much more straightforward.
Instead of spending time dealing with obvious data issues, I could focus on presenting the information.
This was probably the most practical part of the experiment for me because it showed the connection between data preparation and reporting.
My Comparison
After completing the tests, I compared the different approaches.
| Test | Method | My Observation |
|---|---|---|
| Test 1 | Direct Excel import | Data could be loaded directly into Power BI |
| Test 2 | Power Query | Data could be inspected and transformed before loading |
| Test 3 | Excel Table | Power BI recognized the structured Excel Table |
| Test 4 | Corrected data | The source data was easier to work with |
| Test 5 | Dashboard | Corrected data could be used to create the report |
The biggest difference I noticed was not simply the import method.
The condition and structure of the source data made a noticeable difference to the reporting process.
What I Learned From This Experiment
Before doing this experiment, I thought the main challenge would be importing the Excel file into Power BI.
After testing the same data several ways, I realized that importing the file is only one part of the process.
The more important question is:
“What condition is my data in before I start building the report?”
A file can be imported successfully and still need attention before it is ready for analysis.
Power Query also became more useful to me after this experiment because I could see it as a place to prepare data rather than simply another Power BI feature.
I also learned that using an Excel Table can make the source data more structured before it reaches Power BI.
The Approach I Would Use
After performing these tests, my preferred workflow would be:
Excel data → Check the source data → Make necessary corrections → Use Power Query when transformation is needed → Load into Power BI → Build the dashboard
I would not automatically assume that every Excel file needs the same preparation.
The amount of cleaning depends on the actual data.
Final Thoughts
This experiment changed the way I look at importing Excel data into Power BI.
Instead of thinking only about “How do I get my Excel file into Power BI?”, I now think about:
“Is my Excel data ready for analysis?”
Testing the same data in different ways helped me understand the difference between simply importing data and preparing data for reporting.
The screenshots in this article show the actual steps and observations from my experiment.
I plan to continue testing Power BI with different Excel datasets and document what I find.
Frequently Asked Questions
Can I import an Excel file directly into Power BI?
Yes. Power BI can connect to Excel files and import data for analysis.
Is Power Query necessary for every Excel file?
Not necessarily. The amount of transformation required depends on the condition and structure of the source data.
Should I clean Excel data before using Power BI?
It depends on the dataset. Checking the data first can help you identify blanks, inconsistent values, duplicates, incorrect data types, and other issues before building a report.
Is an Excel Table useful for Power BI?
An Excel Table provides a structured way to organize data in Excel and can be selected as a table when importing the workbook into Power BI.
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