Revenue operations teams evaluate B2B contact data platforms backwards. The standard RFP leads with two questions: how many records are in the prospect database, and what's the price per thousand. After four years of reviewing contact lists before they reach sales reps, I'd change the order. The number that matters isn't total records. It's whether you can predict, before a single email goes out, that a record is real, current, and safe to contact. If a platform can't show you the last verified date for an email address, the size of its database is a liability, not an asset.
That might sound like a dramatic opening. I don't think it is. A well-stuffed prospect database looks like momentum, but sending to stale contacts doesn't move pipeline. It produces bounces and spam complaints, and both make every following campaign harder. Whether you use a human SDR or an AI SDR, the data underneath decides the outcome.
Quick background so you know where this is coming from. I'm the quality and compliance manager at okkigo. I review the contact data that flows through our platform, roughly 150 to 200 list exports per quarter. In 2025 I flagged about 11% of first-run exports. Not because the emails were obviously fake, but because the confidence data around them didn't support a send. That's the lens I'm using here.
Early in 2025, I audited a 40,000-contact list a customer brought from a well-known data provider. The contract said 95% email accuracy. I pulled a random sample of 2,000 records, and 18% failed our verification pass. The list was about 14 months old and had never been re-verified after purchase. That's not a story about a uniquely bad vendor. It's what happens when teams buy contact data and treat it like a fixed asset. Contact data rots.
What Revenue Operations Teams Should Actually Evaluate
If you're selecting a platform, renegotiating with a current one, or trying to answer the okki go vs clay question, this checklist is a better starting point than record counts.
1. Verification method, not the marketing accuracy score. Every provider quotes an accuracy number, and almost no two define it the same way. Syntax-only checks confirm formatting. That's close to worthless. Mailbox detection goes deeper, but catch-all domains are where most of the gray area hides.
Here's what I mean by that last part. If an address sits on a catch-all domain, the verification service can't know whether the specific mailbox exists. It only knows the domain accepts everything. Some vendors count that as valid. I've never fully understood why. At okkigo, catch-all results get their own tier so a RevOps lead can see the risk before queueing a campaign. Whatever platform wins your business should give you that visibility too.
2. The last verified date for each record. A prospect database is not stable inventory. People change jobs at a fairly constant rate, and a record that was perfect in January can be dead weight by April. Ask the vendor: when was this email actually tested? If the answer is when we enriched it and the enrichment date is hidden, that's a red flag.
3. Source transparency and enrichment design. Data comes from many places: firmographic providers, job change signals, public sources, other vendors. The quality question is whether you can tell which source populated a field. A phone number from a job board in 2019 carries different risk than a mobile number updated last month. Platforms that run waterfall enrichment, like okkigo, use one source to fill a gap and then the next source, and they keep a record of what filled what. When I review lists, I look for those source trails. They tell me whether I'm looking at a person likely still in role or at a historical artifact.
4. The ability to actually generate leads from within the data, not just search it. A prospect database is inventory, not motion. To generate leads, your team needs to move from the database to a sequenced, verified, human-approved outreach flow. That's why I'm biased toward agent-native platforms: an agent plans the target list, enriches it, checks it, and hands a queue to a person for approval. The loop closes before send, not after a bounce report.
5. Compliance tooling. This one usually gets no airtime until there's a problem. Under GDPR Article 5(1)(d), personal data must be accurate and kept up to date. In the US, CAN-SPAM requires a working opt-out and a valid postal address in commercial email. Per FTC guidance, marketing claims like accuracy rates need to be substantiated, so ask the vendor to show you their methodology. A quality platform also has to handle unsubscribe lists and suppression files across imported data. If the ops team has to jury-rig that part, count it as a hidden cost.
Oh, and one blind spot I see in almost every evaluation: teams compare total coverage but never check coverage inside their priority accounts. A database with hundreds of millions of records can still have only a fraction of your top 500 accounts covered with verified emails. Ask the vendor to run a sample against your actual account list before you sign. Their reaction tells you a lot.
Okki Go vs Clay: The Comparison I Keep Hearing
Nearly every RevOps call eventually reaches the okki go vs clay question. I don't dodge it.
Clay is a genuinely powerful data orchestration tool. I've seen teams do impressive things with it, mostly because they had an ops engineer or a growth analyst who enjoyed building complex enrichment workflows. If that skill sits on your team, Clay lets you assemble a prospect database exactly the way you want it, with dozens of sources and custom logic. To be fair to Clay, that flexibility is the whole product.
okki go solves a different problem. It was built as agent-native prospecting for RevOps teams who don't want to assemble a tech stack from parts. An agent plans the outreach around target accounts, runs waterfall enrichment and intent data inside the same flow, and verifies before anything is sent. The human-in-the-loop step means a RevOps person or SDR reviews, edits, and approves. Nothing goes out without a human checkpoint.
If you ask which one I'd choose, the answer is boring: it depends on the team. For a typical outbound unit with a generalist RevOps lead, I'd pick okki go because the controls live in one place and quality is enforced by design. If you have an engineer whose job is literally to build data workflows, Clay deserves a hard look. They don't have to be mutually exclusive either. Some teams use Clay for discovery and okki go for sending. That's a valid architecture.
When I coach teams setting up okki go for RevOps, I tell them to spend as much time defining what they won't accept as what they want. A small list with high confidence outperforms a huge prospect database with unknown confidence every single time.
Where This Logic Breaks Down
I don't want to oversell the checklist. There are situations where the priorities above shift.
If your team is at the very start, before you have a defined ICP, no data platform will save you. The first deliverable is a crisp description of the account and contact you want to reach. If you're prospecting a niche vertical where only a few thousand companies exist globally, data breadth matters less than the depth and freshness of records in that vertical. And if the real bottleneck is messaging, if replies are missing even on clean lists, then a new prospect database is a distraction, not a fix.
I'm not a data engineer or a privacy lawyer, so I can't speak to every API architecture or GDPR edge case. What I can tell you from a quality review perspective is that the boring operational details are usually what separate a good platform from an expensive one.
One more uncomfortable truth. No contact data platform, okkigo included, guarantees replies or ROI. Anyone who promises reply rates is selling something that doesn't exist. A clean, fresh, well-governed prospect database simply gives your messaging the best chance it's going to get. The rest is on the campaign.
