Duplicate and Entity Matching Review
Plan how to detect and merge near-duplicate records.
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Plan how to find and merge near-duplicate records. Dataset description and size: [DATASET_DESCRIPTION]. Columns available: [COLUMNS]. Examples of duplicates I have seen: [DUPLICATE_EXAMPLES]. Tool: [SPREADSHEET_APP_OR_TOOL]. Use this decision tree and give concrete formulas or steps for my tool: 1. Are there reliable unique identifiers (customer ID, email, tax number)? If yes, count duplicates on the ID first. 2. If not, standardize first: trim spaces, make case consistent, remove punctuation, normalize abbreviations (Street/St), split names and addresses, and format phone numbers. 3. Exact duplicate on standardized key (name plus date of birth, email, address)? Flag. 4. Fuzzy matches: suggest similarity approaches by data type (edit distance for names, phonetic codes for surnames, token matching for company names) and appropriate thresholds. Define three bands: auto-merge, human review, keep separate. 5. Which record survives? Define merge rules (most recent, most complete, trusted source) and what to do with conflicting values. 6. Keep an audit trail: original IDs, merge date, rule used. Output a step plan, example formulas, a review table layout, and a sample of 6 pairs from my examples classified into the three bands with reasons. Warn about false merges of different people with similar names.
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