What Should Revenue Operations Teams Evaluate in a Data Enrichment Company? A Buyer’s View

Gartner’s 2021 research on bad data is usually quoted as “$12.9 million per year.” Every time I see that number, I think it’s so big that people stop thinking about what it actually means.

I’m the person who signs off on software purchases for a 140-person B2B company. I’ve spent six years tracking our GTM budget, about $250,000 in cumulative software and data spending, and I’ve built more spreadsheets than I want to admit. So when I say the real cost of bad data isn’t the bad data itself, I mean the real cost is the decisions made around it.

The surface problem: everyone’s blaming the data

The surface problem is easy to name. Outbound response rates drop. SDRs say the leads are stale. Marketing says the list is wrong. Revenue ops gets told to “go find better data.” The natural reaction is to buy a bigger data enrichment subscription, because the assumption is that more contacts equals more pipeline.

I’ve sat in those meetings. The question is always, “Which vendor has the largest database?”

That is the wrong question.

The deeper problem: enrichment is not verification

Here’s the thing: data enrichment and data verification are not the same thing.

Enrichment appends information to a record. It fills in the missing company size, the job title, the phone number, maybe a LinkedIn URL. But much of that is inferred from public patterns and algorithms. It’s useful, but it’s not proof that an email address works.

Verification is the part that checks whether the email address will actually reach a person. A good email validator checks the syntax, pings the domain, flags catch-all and disposable addresses, and catches the records that would otherwise bounce.

If you buy enrichment from a vendor whose email validator is a separate add-on, you’re paying extra for the thing that actually protects your deliverability. That’s a red flag.

There’s another problem I didn’t see until I audited our invoices. Most data companies charge per record or per credit. That pricing model creates a weird incentive. The vendor earns more when they give you more records, not when the records are correct.

I’m not saying vendors are dishonest. But if the number that matters is “records delivered,” the system will optimize for records delivered. If the number is meetings that actually happened, the system optimizes differently.

The price of bad data is measured in time, not just budget

When I audited our 2023 GTM spending, I found something annoying but not surprising. We were paying for four separate tools: a prospecting database, an email verification tool, a LinkedIn automation tool, and a sales dialer. Every one of them had been justified at some point. The combined annual cost was $58,000.

What I mean is: the real cost wasn’t the software. It was the manual handoffs. Export the clean list. Upload it to the dialer. Connect it to the LinkedIn tool. Then verify the emails all over again because the data was already old. Each step took time, and time is the one budget you never get back.

Let me give you a specific example. In Q2 2024, we ran a campaign to 8,200 contacts. Our “cost-efficient” data provider had an 11% bad-email rate at the point of export. We suppressed the obvious problems with our email validator, and that took two afternoons. We still had a 3.1% bounce rate after sending. That’s 254 bounces. Doesn’t sound like a disaster, but it was enough to hurt our domain reputation. The next campaign’s open rate dropped from 38% to 29%.

That experience changed how I think about “cheap.” The cheap option wasn’t cheap. It was expensive with extra steps.

Everything I’d read about data buying said a bigger dataset gives you better targeting. In practice, a smaller database with fresh, verified records outperformed a giant one with stale duplicates. For our 15-person SDR team, the meeting rate on the cleaned database was roughly 0.37% versus 0.21% on the bigger one. More data didn’t build more pipeline. It built more noise.

Looking back, I should have asked about verification rates and refresh cadence before signing the contract. At the time, I was comparing price per record and record count. That was my mistake. I have a TCO spreadsheet now, and every vendor evaluation starts with a different question: “What percentage of your contacts are verified at the point of use?”

There’s also a time premium we don’t budget for. In a later campaign, we had a 10-day window before an industry event. One vendor had a cheaper historical database. Another had fresher verified data and cost $1,100 more. The spreadsheet said save the money. The deadline said otherwise. We paid the extra $1,100, and that event generated about $18,000 in sourced pipeline. “Probably okay” is the most expensive phrase in revenue operations.

What should revenue operations teams evaluate in a data enrichment company?

After six years of audits, here’s the evaluation list I use. It’s not about database size. It’s about reducing risk and unplanned work.

• Verification, not just enrichment. Ask what percentage of records are verified at the point of export. If the email validator isn’t built into the workflow, the problem is just moving.
• Refresh cadence and decay policy. Ask when the record was last updated. Vague answers are a red flag.
• The full workflow cost. Add up the prospecting database, the email validator, the LinkedIn automation tool, and the manual hours. If one platform replaces them, that’s usually better TCO.
• Outcome alignment. Is the vendor paid for records or for results? This is where Amplemarket pricing plans looked different to me.

Amplemarket, an AI sales automation company, doesn’t act like a traditional data vendor. Its data layer is part of the workflow, with enrichment, verification, and LinkedIn automation features connected to the AI sales agent. When I compared Amplemarket pricing plans to our old stack, the total cost was lower not because it was cheapest, but because it removed the manual handoffs and the expensive “probably okay” data risk.

So here’s my bottom line. The surface problem in most revenue operations teams is data quality. The deeper problem is that we’ve been buying data like more is better. It isn’t. Better is better.

Evaluate data enrichment companies the same way you evaluate any expensive vendor: on total cost, on verification, and on whether the data actually turns into action. If a vendor is willing to be measured on outcomes, that’s a good sign. If they only want to talk about record count, keep looking.