The Onboarding Call That Actually Prevents Bad Data: A Working Checklist | NPLUS Global

Most onboarding calls with data vendors are ceremony. Someone from sales hands you off to "customer success," you get a walkthrough of a dashboard you'll never look at again, and three weeks later you're staring at a file full of retired NPIs and job titles that expired in 2021. The call itself isn't the problem — what gets covered (or skipped) in it is.

Below is a working checklist, grouped by phase, for what a genuinely useful onboarding process should include. None of this is about "alignment" or "partnership." It's about catching problems before they're baked into your CRM.

Before you sign

• Ask for the actual source list, by name. Not "verified, multi-sourced data" — get the specific registries, licensing boards, claims feeds, or self-reported panels they pull from. If they won't name sources, assume the file is aggregated from other resellers.
• Get the refresh cadence per source, in writing. NPI data, state license boards, and hospital org charts don't update on the same schedule. A vendor who says "updated monthly" without specifying which fields update monthly is giving you a marketing line, not an answer.
• Request a sample pull from your actual target segment — not a generic demo file. If you sell to cardiology groups in the Southeast, ask for 100 records matching that exact criteria before you commit to anything.
• Ask what share of the file has been touched in the last 90 days. This is a more honest freshness metric than any blended "accuracy rate" they quote you.
• Get the match/replace policy in writing before you sign, not after your first bad batch. How long do you have to report bad records? What counts as proof? Is it a straight swap or a credit?
• Find out who your contact is after the deal closes. If the answer is vague or it's "your rep will loop in someone," push for a named onboarding contact before you sign anything.

During the onboarding call itself

• Walk through their match logic field by field. How do they define "specialty," "decision-maker," or "practice size"? These definitions vary wildly between vendors, and a mismatch here is the single most common source of downstream frustration.
• Get an actual data dictionary, not a slide deck — a document showing every field, its possible values, and how it's derived (self-reported vs. inferred vs. verified).
• Ask how they de-dupe against your existing records. Do they run against a suppression file you provide, or do they just hand you a fresh dump and expect you to sort it out? Get this settled before the first delivery, not after you've loaded 40,000 duplicates into your CRM.
• Confirm the exact delivery format and cadence — flat file via SFTP, API, direct CRM push — and get a date, not a season. "Early next month" is not a delivery commitment.
• Establish the suppression/opt-out exchange process. Ask specifically how DNC and opt-out lists move between your systems and theirs, and how often. This matters more in healthcare than most verticals given the compliance exposure.
• Set a defined escalation path with names, not a support email. Who do you call when 12% of a file bounces? Get a person, not a ticketing system.
• Agree on what "clean" means numerically, before you've sent anything. Define an acceptable bounce/undeliverable threshold now. If you wait until after the first campaign to argue about what counts as "bad data," you'll lose that argument.
• Clarify usage rights explicitly. Can you enrich the records with your own research? Can you retain the data after the contract ends? Can it be shared across business units? Get this in plain language, not buried in the MSA.

After the first delivery

• Manually audit a random sample of 50 records against public sources before loading anything into your CRM. Check NPI status, practice affiliation, and license status yourself. This takes an afternoon and it's the single best early-warning system you have.
• Calculate your own bounce/undeliverable rate rather than accepting the vendor's self-reported number. Run a small test send if the file is large enough to justify it.
• Check for duplicates against your existing database before merging anything. Even with a stated de-dupe process, run your own match on a sample batch.
• Test one campaign against a small subset — not the full file — before scaling spend or outreach against it. This is standard practice for paid media; there's no reason data vendors should be exempt from the same discipline.
• Log every discrepancy with a timestamp and record ID, not a general email saying "the data seems off." Specificity is what gets you a credit or a fix; vague complaints get you a form response.
• Put the 30-day and 90-day review meetings on the calendar now, while the relationship is still cordial. Waiting until there's a problem to schedule a review means you're negotiating from a weaker position.

The pattern worth watching for

None of this is exotic. It's the same due diligence you'd apply to any vendor whose output feeds directly into revenue-generating activity — the difference is that data problems are quieter than, say, a broken integration. Bad records don't throw an error. They just quietly tank your reply rates, waste rep hours, and erode trust in whatever system they're loaded into, usually for weeks before anyone traces it back to the source.

At NPLUS Global, the onboarding conversations that go well are almost always the ones where the buyer asks pointed, specific questions early — not the ones where everyone nods along to a deck. If your vendor's onboarding call can't survive the checklist above, that's information too.