How should fuzzy matching thresholds be calibrated for deduplication?

How should fuzzy matching thresholds be calibrated for deduplication?

• A. Start with conservative thresholds
• B. Test against labeled data
• C. Adjust to balance false positives and negatives and monitor outcomes
• D. All of the above

Answer: (D)

Calibrating fuzzy matching thresholds for deduplication works best when you combine conservative starts, ground-truth testing, and ongoing balance and monitoring. Begin with conservative thresholds to minimize the risk of merging distinct records too aggressively. Then test the thresholds against labeled data to quantify how often true duplicates are captured (precision/recall) and how often non-duplicates are incorrectly merged. Use those results to adjust the threshold, aiming to balance false positives and false negatives in line with business needs. Finally, keep monitoring outcomes over time because data characteristics can drift, so re-evaluation and adjustment are often necessary. Taken together, these steps provide a robust, practical approach to setting thresholds.