Common Power BI Mistakes (And How to Avoid Them)

Power BI is an incredible tool for transforming raw data into interactive dashboards and reports. But if you don’t follow best practices, your reports can become slow, inefficient, and even insecure.

Let’s explore three common Power BI mistakes—performance killers, poor data modeling, and security missteps—and how to avoid them like a pro.

Mistake #1: Performance Killers – Too Many Calculated Columns

What’s the problem?
A common mistake beginners make is overusing calculated columns instead of measures. Calculated columns are stored in memory, making your Power BI model bloated and slow.

🚨 Example of a bad approach:
Let’s say you want to create a “Profit” column:

Profit = Sales[Revenue] – Sales[Cost]

If you do this in a calculated column, Power BI will store the result for every row in memory, even if you don’t need it.

✅ Better approach: Use a measure instead
Measures are dynamic and calculated only when needed, keeping your model efficient:

Profit = SUM(Sales[Revenue]) – SUM(Sales[Cost])

How to avoid this mistake:

• Use measures instead of calculated columns whenever possible.
• Use Power Query to create new columns before data loads into Power BI.
• Only use calculated columns when absolutely necessary (e.g., when you need them as part of a relationship).

Mistake #2: Poor Data Model Design – Not Using Star Schema

What’s the problem?
Many Power BI users create flat tables (one big table with all columns) instead of properly modeling data using a star schema. This leads to:
❌ Slow performance – Too much redundancy and duplication.
❌ Complicated DAX formulas – Relationships aren’t clear.
❌ Difficult maintenance – Hard to scale when new data comes in.

🚨 Bad Example: A single massive table

✅ Better Approach: Star Schema
Instead of one big table, split your data into Fact and Dimension tables:

Fact Table (Sales Data)
| Order ID | Product ID | Customer ID | Region ID | Sales | Quantity | Date |

Dimension Tables

• Products Table (Product ID, Product Name, Category)
• Customers Table (Customer ID, Customer Name, Segment)
• Regions Table (Region ID, Region Name)

With Fact and Dimension tables, your model is:
✔️ Faster (less redundancy)
✔️ Easier to use (simpler DAX calculations)
✔️ Scalable (can handle more data easily)

How to avoid this mistake:

• Follow a star schema design with separate fact and dimension tables.
• Avoid flattening tables unnecessarily—Power BI performs better with relationships.
• Use Power Query to clean and structure your data before loading it.

Mistake #3: Ignoring Data Types and Formatting

What’s the problem?
Many Power BI users forget to set the correct data types (e.g., date, number, text) in their columns. This can lead to:

• ❌ Wrong results in visuals and calculations
• ❌ Sorting issues (e.g., months sorted alphabetically instead of chronologically)
• ❌ Errors in time intelligence or filters

🚨 Example:
If a “Date” column is stored as text, DAX functions like TOTALYTD or DATEADD won’t work correctly.

✅ Better approach:

• Set the correct data type (Date, Decimal, Whole Number, Text, etc.) for each column in Power Query or the Model view.
• Format numbers as currency, percentages, or whole numbers based on their use.
• Use Sort by Column feature when working with custom sort orders (e.g., sorting months by a Month Number column).

✅ How to avoid this mistake:

• Review column data types after loading data into Power BI.
• Use the Model view to adjust formatting and sort order.
• Test visuals and DAX calculations to ensure data behaves as expected.

Conclusion

Avoiding these mistakes will make your Power BI reports:
✅ Faster (by using measures and a star schema)
✅ More efficient (by reducing redundant calculations)
✅ More secure (by properly handling data access)

Power BI is powerful, but bad habits can slow it down or expose sensitive data. By following these best practices, you’ll create faster, smarter, and safer reports.

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