Bad data isn't a Salesforce problem, it's a process problem expressed inside Salesforce. Admins inherit the consequences and own the cleanup. This page is the operational guide: how to find the bad data, how to clean it without breaking everything downstream, and how to keep it clean once it is.
04 steps · 04 FAQs
“Every clean data project becomes a re-org. Every dirty data project becomes someone else's problem. Pick early.”
Practical steps
Run the duplicate report, the completeness report, and the validation-error report. Without baselines, you can't measure improvement.
If new bad data is still entering the system, cleaning the legacy is bailing out a leaking boat. Fix the validation rules, the integrations, and the rep behaviors first.
Never run a 50,000-record update in one go. Batch by 500. Snapshot before each batch. Test the result of one batch before the next.
Once clean, write down what 'clean' means. Field-by-field. Otherwise it slides back to baseline within 90 days.
From the library
Score
The 4 permission layers and how they really resolve, 10 named anti-patterns, the SOQL query bank, real-world breach stories, and a cleanup sequence that won't break access. Spring '26 current.
Avg readiness
A comprehensive checklist to evaluate your Salesforce org's readiness for AI, covering data quality, automation maturity, user adoption, and integration preparedness.
Read time
A 10-chapter guide for admins and the leadership teams who fund them. Worksheets, AI prompts, and a 90-day plan to audit, prioritize, and fix Salesforce technical debt.
Frequently asked
Three passes: (1) exact-match on website domain (highest signal), (2) fuzzy-match on company name with manual review queue, (3) phone+address composite key for the long tail. Don't try to do it in one query.
Required fields for the 3-5 facts you absolutely need at record creation. Validation rules for everything else. A 14-required-field record creation form is how you get reps creating fake records to bypass it.
Continuous, not quarterly. Run dedupe checks on every record creation. Run completeness checks daily. Reserve manual cleanup batches for true legacy data, not as a substitute for ongoing hygiene.
Yes, with the right guardrails: (1) every change goes through an approval queue, (2) every change is logged with reversal SQL, (3) only specific field types (formatting, enrichment) are auto-applied. The agent does the work; you keep the audit.
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