Salesforce Duplicate and Data Quality Audit
Measure duplicates, completeness and formatting.
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[VERIFY] is an editorial flag, not a blank to fill. Wherever the prompt shows [VERIFY], check that item against a reliable source before you use it.
Audit data quality in Salesforce using this extract of accounts and contacts: [DATA_EXTRACT]. Fields that matter most: [KEY_FIELDS]. Our duplicate rules: [DUPLICATE_RULES]. Use anonymised data only; do not ask me to paste real personal data. Explain the matching rules you used, in a table with Rule, Example match, Confidence and Action: same email (high, merge after review), same name and company (medium, review), same phone with different names (low, review); give examples of an exact duplicate, a probable duplicate and a false positive. Calculate completeness for each key field (filled divided by records) with the count and percentage, exact and probable duplicates, invalid emails and phone formats, inconsistent country or state values, records with no owner, and contacts not linked to an account. Data quality score is the average of the key fields: completeness of 96, 88, 71, 64 and 92 percent averages 82.2 percent. Return a table, a merge plan with a survivorship rule per field, a backup before merging records with related history, and mark which duplicate decisions need a human. Prevention plan in order of value and effort: required fields, picklists, matching rules, duplicate warnings on creation [VERIFY], training. Monthly report of three numbers (new duplicates, completeness of key fields, records without owners), targets for next quarter, and an owner for data quality.
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