Power Query is an incredibly powerful tool for data preparation and transformation, widely used in both Excel and Power BI. But to truly harness its potential, it’s important to follow some best practices that’ll help you work smarter, not harder.
By adopting these strategies, you can optimize your workflow, ensure cleaner queries, and avoid common pitfalls that can slow you down. Let’s dive in!
1. Keep Your Source Data Raw
Always keep the source data intact to maintain its integrity.
- Avoid Direct Changes: Never modify your original source file. Instead, perform all data transformations inside Power Query. This keeps your original data clean and untouched, allowing for easier updates later.
- Example: If you’re importing data from an Excel file, don’t delete columns or adjust values directly. Use Power Query to clean and transform the data.
- Reference Instead of Duplicate: Instead of duplicating queries, use the “Reference” option to create new queries based on an existing one. This not only saves memory but also ensures that updates to the original query automatically propagate to the referenced queries.
- Example: Create a query called “Sales Data” and reference it to create queries like “Sales by Region” or “Sales by Product.” This keeps your workbook lighter and more efficient.
2. Use Descriptive Names for Queries and Columns
Readable and consistent naming makes your work more understandable.
- Rename Steps with Clear Descriptions: Rather than generic names like “Renamed Columns,” give each step a clear, descriptive name reflecting what you’ve done.
- Example: Instead of “Renamed Columns,” name it “Renamed Date and Sales Columns” if those are the specific columns you renamed.
- Be Consistent: Adopt a consistent naming convention for your queries and columns. For instance, always start step names with an action (e.g., “Filtered,” “Grouped,” “Renamed”) followed by a description of the change.
- Example: Instead of “Filtered Rows,” name it “Filtered Sales Data for 2023” for clarity.
3. Set Data Types Early
Setting the correct data types early prevents errors later.
- Set Data Types Early: As one of your first steps, specify the correct data types for each column. This helps avoid errors when performing calculations or other transformations.
- Example: If you have a column with dates, set its data type to “Date” right after importing the data to avoid issues when filtering by date.
- Avoid “Any” Data Type: Power Query often assigns “Any” as the default data type, which can lead to errors or inconsistencies. Be sure to manually assign the correct data type for each column.
- Example: After importing a sales dataset, change a column like “Amount” from “Any” to “Decimal Number” for accurate calculations.
4. Avoid Hard-Coding Values
Hard-coded values make maintenance difficult and prone to errors when data changes.
- Use Parameters: For values that change frequently—like file paths or filter criteria—use parameters. This makes your queries adaptable and easier to update.
- Example: Instead of hard-coding the file path for your sales data, create a parameter for the file path. This way, if the location changes, you only need to update the parameter instead of every query.
5. Filter Early, Sort Late
Efficient queries are fast queries. Prioritize filtering early and sorting at the end.
- Filter Early: Apply filters early to reduce the amount of data you’re working with. This speeds up processing, especially with large datasets.
- Example: If you only need data for 2023, filter your data as one of the first steps.
- Sort Last: Sorting data consumes a lot of resources, so leave it as the last step. This way, you avoid slowing down your query with unnecessary sorting earlier in the process.
- Example: After cleaning and filtering your dataset, sort it by “Sales Date” as the final step.
6. Reduce Column and Row Count
Minimizing the amount of data processed improves performance and reduces memory usage.
- Trim Columns and Rows: Eliminate unnecessary columns and rows early on in the query to keep things efficient.
- Example: If you only need sales data for a specific region, remove columns like “Customer Address” that aren’t relevant to your analysis.
- Review Data Regularly: As your dataset evolves, some fields might no longer be necessary. Periodically review your queries to remove irrelevant data fields and keep your queries lean and fast.
- Example: After each quarterly analysis, revisit your query and remove any columns or rows that aren’t needed anymore.
7. Regularly Refresh and Test Queries
Testing and refreshing your queries ensures that they continue to perform well as your data evolves.
- Refresh Frequently: Regularly refresh your queries during development to catch issues early.
- Example: After major transformations, click “Refresh Preview” to check for errors or unexpected issues in your data.
- Test with Diverse Samples: Test your queries with various data samples, including edge cases, to ensure they can handle different scenarios without breaking.
- Example: If your dataset contains missing values, make sure to test how your query handles blank or null values and adjust your transformations accordingly.
8. Boost Readability with AI Assistance
Make your Power Query code more readable and efficient with the help of AI.
- Neat Formatting and Clear Step Names: Use AI tools like ChatGPT to reformat your M code, suggest better step names, and improve the overall readability of your query.
- Example: After completing a query, copy your M code into ChatGPT for suggestions on improving formatting and step descriptions.
- Optimize Your Workflow: AI tools can help you optimize and streamline complex transformations by making your code more concise and clear.
- Example: If you’re unsure about how to structure a complex transformation, use ChatGPT to suggest cleaner, more efficient M code.
9. Document Your Work
Keep track of your transformations for clarity and future reference.
- Maintain External Documentation: Keep a notebook or an external file where you document the transformations, logic, and key decisions made during your Power Query process. This is particularly helpful in collaborative projects.
- Example: Use tools like Notion or Google Docs to create a project notebook that outlines the purpose and logic behind each query.
Why These Best Practices Matter
Following these best practices will make your Power Query transformations more efficient, readable, and maintainable. Whether you’re preparing data for analysis in Excel or building dashboards in Power BI, these strategies will help you create clean, scalable workflows that are easier to troubleshoot.
Do you have any other Power Query tips to share? Feel free to drop them in the comments!
