Data Health

Catch dirty data before it costs you.

Donify continuously scans your donors for duplicates, undeliverable addresses, and contribution-limit violations. Fix issues before they become FEC problems.

Duplicates
12
Address issues
4
Limit violations
2
FindingAction
Sarah Mitchell + Sara MitchellSame name + ZIP, different email
Resolve
James O'BrienAddress could not be verified
Resolve
David Chen$10,500 — exceeds 2026 Primary cap of $10,000
Resolve
Riverside Realty Group + Riverside RealtyTrigram match
Resolve
Aisha PatelApartment number missing
Resolve

Duplicates

Same donor, three different spellings.

Trigram fuzzy matching catches the cases plain string equality misses: nickname swaps, typos, address-change splits, household members. Resolve, merge, or hide each match — your call. Anything you merge moves donations and tags from the duplicate into the canonical record.

Donor ASarah Mitchellsmitchell@example.com12 State St, Albany NY
Donor BSara Mitchellsara.m@example.com12 State St, Albany NY
Same name + ZIP · 92% trigram similarity

What it catches

Three kinds of dirty data, three ways to fix it.

Duplicate donors

Trigram fuzzy match on name + ZIP. Email-based exact match. Pairs surfaced for one-click merge.

Bad addresses

Google Address Validation flags addresses that won't deliver — typos, missing apt numbers, structural junk.

Limit violations

Per-donor totals against your election limits. Catches accidental over-contributions before the filing deadline.

Continuous scan

Every new donor or donation triggers a re-check. Nothing goes stale.

Hide false positives

Mark a finding as not-an-issue. Won't surface again unless the underlying data changes.

Audit trail

Every resolve and merge is logged. Field-level history of who changed what.

Clean data, automatically.

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