You're paying for enriched data that was accurate sometime last quarter. The vendor harvested it, stored it, and sold it to you alongside everyone else. And by the time your BDR picks up the phone, a meaningful chunk of it is already wrong.
This isn't a vendor quality issue. It's a structural one. The way most data vendors work — harvest, store, enrich on a cadence, sell — guarantees staleness. It's baked into the business model.
How the Database Model Breaks
Here's the cycle: a vendor collects data from various sources. They store it. Periodically — maybe quarterly, maybe every six months — they re-enrich a portion of that database. Then they sell you access.
Between enrichment cycles, the data decays. People change jobs. Companies get acquired. Offices relocate. Subsidiaries rebrand. The database doesn't know any of this happened until the next refresh — and that refresh might not even touch the records you care about.
A RevOps team at a large real estate software company told me they'd evaluated multiple enrichment vendors and kept hitting the same wall: the databases looked complete on the surface but fell apart under scrutiny. Records that had changed within the last year — ownership transfers, companies that had been acquired or dissolved — were almost always wrong. The most recent changes, the ones you'd actually want to know about, were exactly what database vendors were worst at.
That's the paradox at the center of every static database. The data that matters most is the data that changed most recently. And no refresh cycle is fast enough to catch it.
The Dirty Secret: Everyone Just Googles It
Here's something I've now heard from enough people that I'm comfortable calling it an industry-wide pattern: everyone checks their vendor's data the same way. They Google it.
A record comes back from ZoomInfo or Dun & Bradstreet or whoever. The sales rep looks at it, something feels off, so they open a browser. They check the company website. They check LinkedIn. They compare what they see online to what the vendor gave them. And half the time, the web tells a different story than the database.
One company we spoke with — a global biotech with about 800,000 contacts — told me they stopped Googling entirely about a year ago. Not because their data got better, but because they switched to internal GPT workflows and N8N automations to do the same verification work. The Googling didn't stop. It just got automated — because the underlying data from their vendors still couldn't be trusted on its own.
Think about what that means. The entire value proposition of a data vendor is that they do the research so you don't have to. But the verification step — the step that tells you whether the vendor's data is actually right — is the same web search your reps were doing manually before you bought the vendor in the first place.
The Sales Navigator Lesson
One company I spoke with had invested millions in LinkedIn Sales Navigator over several years, thinking it would solve their contact accuracy problem. It was the big dream from the business users — just get us Sales Navigator and we'll be fine.
The punchline: nobody puts their work email into LinkedIn. That's what Gmail is for. So the team ended up with phone numbers routing to personal cells, emails that bounced, and a dataset that looked rich but was practically useless for outreach at scale.
Sales Navigator is a great tool for individual research. It's not a data infrastructure solution. But when your data is broken and someone promises you a database of 900 million professionals, it's easy to convince yourself that volume equals accuracy. It doesn't.
What We Do Differently
When we built Salmon, we started from a simple observation: if the way everyone verifies data is by checking the web, why not just make the web the primary source?
Every time a record is queried through Salmon, we go to the live web and verify it. We check the company website. We cross-reference public records and registrations. We validate that the email domain matches the employer. We look at whether the person still shows up on their company's team page.
We're not returning a cached result from last quarter. We're doing what your best analyst would do — except across your entire database, in parallel, automatically.
It's not instant. A record can take up to 30 seconds to fully resolve. But the tradeoff is straightforward: would you rather have an answer in milliseconds that might be wrong, or an answer in seconds that you can actually build on?
If you're paying for data that your team has to verify anyway, something's broken. Get in touch.