Avoiding Entity Key Drift in a Data Lake: Step 2, When Fuzzy Matching Stops Working

Reconciling messy entity identifiers like product part numbers in a data lake often involves using fuzzy matching algorithms. An analysis of five common string-similarity metrics, including Damerau-Levenshtein and Jaro-Winkler, was conducted on a set of sensor station identifiers. After creating a ground truth by manually verifying part numbers against manufacturer datasheets, the results showed a critical failure. No metric could be tuned to a threshold that successfully corrected all typos without also incorrectly merging genuinely different products. This outcome suggests that relying solely on automated fuzzy matching for this type of data cleanup is unsafe and can lead to significant data integrity issues.