Applying Data Preparation Best Practices and Troubleshooting Common Problems

Data preparation is not simply about removing unwanted information. It also involves creating a repeatable process that preserves the meaning of the original data.

Data Preparation Best Practices

1. Keep the original source unchanged

Whenever possible, use Power Query to prepare the data instead of manually modifying the source file. This preserves the original information and makes the transformation process easier to reproduce.

2. Use meaningful query and column names

Names such as Sales_2026 and TotalSalesAmount are easier to understand than generic names such as Query1 and Column3.

3. Apply data types early

Assigning the correct data types helps identify conversion problems before the data is used in calculations or relationships.

4. Remove only unnecessary information

Do not delete records simply because they look unusual. Check the business context before removing outliers, missing values, or duplicate-looking records.

5. Review Applied Steps regularly

Use the Applied Steps pane to understand the transformation sequence. Clear, logical steps make queries easier to troubleshoot and maintain.

6. Validate important totals

Compare row counts, sales totals, and other key metrics before and after transformations. Unexpected differences may indicate that records were removed or interpreted incorrectly.

7. Prepare data for the intended model

Use consistent column names, compatible data types, and suitable identifiers. This makes it easier to establish relationships between tables in the next module.

8. Consider query performance

Avoid importing unnecessary columns and rows when they are not needed. For supported data sources, query folding may allow some transformations to be processed by the source system rather than locally. Not every transformation or connector supports query folding.

Common Power Query Problems and Solutions

Problem 1: Data type conversion errors

A column contains values that cannot be converted into the selected data type.

Solution: Inspect the problematic values, verify the source format, and correct or handle invalid records before applying the conversion.

Problem 2: Incorrect date interpretation

Dates appear in an unexpected format or produce conversion errors.

Solution: Check the source date format and use the appropriate locale when converting text to dates.

Problem 3: Duplicate records remain after cleaning

The selected columns may not uniquely identify a record, or the apparent duplicates may contain differences in other fields.

Solution: Determine the correct business key and review the relevant columns before removing duplicates.

Problem 4: Unexpected rows disappear

A filtering step may exclude records that are required for analysis.

Solution: Review the Filtered Rows step and verify the filtering conditions.

Problem 5: A query fails after the source changes

A source file may have renamed columns, changed its structure, or moved to another location.

Solution: Check the Source, Navigation, and other affected steps. Update the connection or transformation logic to match the revised source.

Problem 6: Refreshing produces unexpected results

The source data may have changed, a transformation may no longer be valid, or the refresh may be using a different file or connection.

Solution: Confirm the source location, inspect the Applied Steps, and compare the refreshed results with the expected output.

Why Power Query Matters in a Power BI Project

Power Query establishes the foundation for the next stages of Power BI development.

Consider a sales reporting project. The analyst first connects to Excel and database sources, cleans the information, standardizes column names, and verifies the data types. The prepared tables can then be organized into a model with relationships between Sales, Products, Customers, and Calendar.

This sequence makes it easier to create accurate calculations and meaningful visualizations.