hey all! I've just read the 2025 State of RevOps survey that RevOps Co-Op published, and a stat that jumped out was that 71% say data quality hurts their GTM success, even though 42% of those same people think their data is good enough. I've seen this exact thing in scale-ups that I've worked in: it's not that the numbers are wrong, but they just mean different things to different teams. Have any of you dealt with this, and if so what actually has helped you move the needle?
that tension usually reflects a definition problem more than a raw data problem. teams often say data is good because fields are populated and reports run, but if marketing, sales, and revops attach different operational meanings to the same stages or statuses, downstream metrics diverge even when the underlying records are technically accurate, which is why aligning on a shared metric dictionary tied to decision use cases tends to move the needle faster than another cleanup sprint. itβs not fully deterministic how misalignment compounds until pipeline reviews start surfacing contradictions.
Same pattern everywhere: numbers arenβt wrong, definitions are fragmented. Metric dictionary + field ownership + CRM guardrails + weekly exception review fixed it for us.
Strong take, Vaibhav. I see the same pattern: βdata qualityβ is often a definition and ownership issue, not a population issue. Fields can be complete and still be operationally wrong if stage meanings differ across Marketing, Sales, and RevOps. The fastest fix Iβve seen is a shared metric dictionary tied to actual decision points, then enforcing it in stage gates and review cadence.
