10K contacts. Already run through Clay. Still broken.

A global B2B SaaS company, an active Clay user, wanted an independent check for data quality issues on a working list of 1,000 accounts and 9,973 contacts, before running outbound against it.

Pristine re-verified and corrected the list at both the account and contact level: the firmographics used to target the right accounts, the job titles used to identify the right persona at each one, and the LinkedIn, mobile, and email data needed to actually reach that persona once identified.

The audit surfaced three data quality issues:

Data quality issue #1: targeting

Account targeting runs on firmographics, mainly employee count and revenue. Employee size was wrong or missing on 96% of the accounts in the list, revenue on 97%. A list meant to filter for "companies this size, this revenue" was filtering on numbers that weren't accurate for almost every account in it.

Data quality issue #2: the wrong person

Job titles were wrong or missing on 68% of contacts, some never captured at all, others already stale, a promotion, a role change, a departure the client's own data hadn't caught. More than a third of contacts, 39%, were tied to the wrong company outright, a different entity, industry, or domain than the one on record, not a formatting fix.

Data quality issue #3: no way to reach them

LinkedIn was wrong or missing on 66% of contacts. Mobile was wrong or missing on 77%, including every number that was blank or flagged as outright junk, all of it replaced with a real one. Email was wrong or missing on 34% of contacts.

All three data quality issues traced back to the same cause: the list was enriched once and never re-checked.

This wasn't a cold list. It came out of a live Clay workflow, already in production, running against real send volume. The data quality issues were still there.