Messy data = messy results.
Whether you’re analyzing sales reports, customer feedback, or financials — clean data is the foundation of good decisions.
In this blog, we’ll show you how to clean data using Excel and Power BI — even if you’re just starting out.
🧹 What Is Data Cleaning?
Data cleaning is the process of fixing or removing incorrect, incomplete, or duplicate data.
It helps you make sure your reports are accurate, reliable, and insightful.
🔧 Common Data Cleaning Issues:
- Extra spaces or blank rows
- Duplicates
- Missing values
- Incorrect formats (like text instead of numbers)
- Inconsistent categories (e.g. “India”, “india”, “IND”)
- Wrong date formats
📊 Data Cleaning in Excel – Tricks That Work:
| Problem | Solution |
|---|---|
| Extra spaces | =TRIM(A1) |
| Inconsistent text | =UPPER(), =LOWER() or =PROPER() |
| Remove duplicates | Use “Remove Duplicates” tool |
| Missing values | Use IFERROR() or highlight blanks with Conditional Formatting |
| Date issues | Use TEXT() or “Text to Columns” |
Use Power Query (built into Excel) for even more powerful data transformations.
📈 Data Cleaning in Power BI – Best Practices:
Power BI’s Power Query Editor helps automate data cleaning with just a few clicks:
- Remove columns or rows
- Fix data types (text, date, number)
- Split columns (e.g., names into first & last)
- Merge tables
- Replace values
- Remove duplicates or nulls
All steps are saved in the Applied Steps pane, so your cleaning process is fully trackable.
⚙️ Bonus Tip: Automate It!
- In Excel, use Macros or Power Query refresh
- In Power BI, schedule data refresh from connected sources
👨💻 Real-World Use Cases:
- A sales report with mismatched dates
- Customer feedback with missing fields
- HR attendance data with name duplicates
- GST invoice data with wrong formats
📌 Need help with data cleanup or automation?
📞 Contact us at AnalyticalHawk.com — we clean your data and build ready-to-use dashboards in Excel & Power BI.