In an ideal world, all datasets would be perfectly aligned, with consistent formats and no discrepancies. But in reality, data often contains inconsistencies due to typos, formatting differences, or missing values. This is where fuzzy matching in Power Query becomes invaluable.
In this guide, we’ll explore what fuzzy matching is, how to enable it when merging tables, and best practices for handling imperfect data.
1. What is Fuzzy Matching in Power Query?
Fuzzy matching is a feature in Power Query that allows you to merge tables even when there isn’t an exact match between the values in the key columns. Instead of relying on exact matches, it looks for similar or approximate matches, making it useful for dealing with:
✔️ Misspelled words (e.g., “Jon” vs. “John”)
✔️ Inconsistent formatting (e.g., “NY” vs. “New York”)
✔️ Partial matches (e.g., “ABC Corp” vs. “ABC Corporation”)
This feature is especially helpful when working with datasets from multiple sources that may have different naming conventions.
2. How to Enable Fuzzy Matching in Power Query
To use fuzzy matching when merging tables, follow these steps:
Step 1: Load Your Data into Power Query
- Open Power BI or Excel and load the tables you want to merge into Power Query.
- Navigate to the Home tab and click on Transform Data to open the Power Query Editor.
Step 2: Merge Tables
- In the Power Query Editor, click on Merge Queries or Merge Queries as New.
- Select the key columns you want to use for the merge from both tables (e.g., “Customer Name” in Table 1 and Table 2).
- In the Merge dialog box, check the option for Use fuzzy matching.
Step 3: Adjust Fuzzy Matching Settings (Optional)
To fine-tune the fuzzy matching process, click on the gear icon next to the merge step in the Applied Steps pane and adjust the following settings:
- Similarity Threshold: Controls how closely values must match (0 = very loose, 1 = exact match). Lower the threshold to find more approximate matches.
- Ignore Case: Enable this option to make matching case-insensitive.
- Maximum Number of Matches: Specify the maximum number of matches to return per row.
- Transformation Table: Use a custom mapping table to define specific match rules (e.g., mapping “USA” to “United States”).
3. Best Practices for Fuzzy Matching in Power Query
While fuzzy matching is powerful, it’s important to use it carefully to avoid incorrect matches. Here are some best practices:
1. Adjust the Similarity Threshold
Start with a higher threshold (e.g., 0.8) and gradually lower it if needed. This reduces the risk of incorrect matches.
2. Use a Transformation Table
A transformation table allows you to map specific values manually, improving match accuracy. For example, you can map common abbreviations (“Inc.” to “Incorporated”) or handle known spelling variations.
3. Validate the Results
After merging the tables, review the matched data to ensure accuracy. You can create a summary table to check for unexpected matches.
4. Combine with Conditional Columns
Use conditional columns to flag potential mismatches based on business rules (e.g., flagging matches with a similarity score below 0.7).
4. Use Cases for Fuzzy Matching in Power Query
Fuzzy matching is particularly useful in scenarios where data consistency is challenging to maintain. Common use cases include:
- Customer Data Integration: Merging customer records from different CRMs with slight variations in names.
- Address Matching: Matching addresses from different datasets, even if they contain minor differences (e.g., “123 Main St.” vs. “123 Main Street”).
- Product Catalog Merging: Combining product lists with inconsistent naming conventions.
Conclusion
Fuzzy matching in Power Query is a game-changer when dealing with imperfect data. By enabling approximate matches, it helps streamline data integration and reduce manual cleaning efforts.
By following the steps and best practices outlined in this guide, you can confidently merge tables and handle data discrepancies more effectively.