Do you want to know what keeps the person with all the data and insights in your company up at night? I host a monthly Rev Ops Peer Group that brings together dozens of leaders to discuss what's going on in their business. Here are the key challenges that continue to come up: 1) Fragmented Data Across Systems: Many teams are dealing with data scattered across different tools (CRM, LMS, sales enablement platforms, etc.), making it hard to consolidate insights. As one member put it, "we have so many different systems that track different data that there's no one easy way to just click a button and say, here's my dashboard where it has everything I need". 2) Inconsistent Metric Definitions: Different teams may use different terms or definitions for the same metrics, complicating reporting. This challenge was mentioned alongside the difficulty of pulling clean, comparable data sets across the org. 3) Proving Enablement and Training Impact: A persistent issue is showing how enablement programs and training translate to business outcomes beyond onboarding. While ramp time is often well-tracked, broader enablement effectiveness—especially linking to quota attainment or win rates—is harder to quantify. 4) Overwhelming or Unstructured Data: Even when data is available, there's sometimes too much of it, or no clear cadence to assess its impact. One RevOps leader described struggling with when and how to review and iterate based on the data collected. 5) Lack of Leadership Buy-In or Action: Even with data available, without leadership acting on it—whether for training completion or enforcing enablement programs—there’s limited impact. If I were to summarize them, it would look like this: 👉 Enablement and RevOps leaders are sitting on valuable insights—but can’t always activate them. 👉 Organizational alignment (around tools, metrics, and priorities) is still a massive gap. 👉 As companies scale, the cost of this misalignment grows exponentially.
Data in 12 tools, 6 definitions of “pipeline coverage,” zero alignment on what “good” even means. And then people wonder why no one trusts the dashboard. 🤷♂️ The strategic miss? Most companies don’t have a data problem...they have a decision problem. Insights are there. What’s missing is the operational muscle to: - Define what matters - Align teams around it - Enforce a cadence to act on it You don’t need another BI tool. You need shared definitions, real ownership, and execs who treat RevOps like a growth engine, not an analytics concierge.
So true, Brad 👏 As someone who's spent years cleaning up tangled GTM stacks, I’ve seen firsthand how data fragmentation and misaligned metrics quietly kill momentum. Even worse item #5: when RevOps does surface insights, but leadership doesn’t act...Thanks for articulating what many of us feel but rarely say out loud.
I think 5 presents the biggest opportunity for RevOps teams to be as impactful of a role as it should be. Leadership buy-in universally helps, but if it isn’t there, this is where RevOps can just step up and be the leader that owns this. We see 1 and 2 consistently every day. There aren’t many things worse than having to try to reconcile the random report that was pulled by someone somewhere in the org. Most companies would benefit from centralizing gtm data and insights first, decentralize later after getting definitions better aligned and governance in place.
Another thing to add to #2 is that beyond just aligning on what a metric means - is making the underlying logic/calculations reusable and version controlled. It’s not enough to define metrics centrally. Different teams need to reuse the same calculation logic, reference the right + healthy fields inside their own reports and workflows—without rebuilding it each time so that there's credibility across the board around the number they're reporting on. I'd like to think of this as shared components in engineering/dev: centralized, version-controlled, and consistent across the org. This is a hard problem to solve for, but definitely worth an attempt.
Data Leadership Consultant
6moThis definitely resonates with what I see. I'd add one (maybe hidden) challenge that is a common thread across all five of the challenges you listed. Folks tend to underestimate the gravity of impact that any singular business process change has on the historical quality and consistency of their data. If you could snap your fingers and solve 1, 2, and 4, I promise you that you'd find that even with all the data centralized, metrics defined consistently, and clever dashboards provided, you'll still find that you still struggle to come to confident conclusions in analysis. The little tweak you made a couple quarters ago to the lead lifecycle process, or the addition of a new opportunity stage, or even the simplest of enrichment automations can fundamentally change the nature of the *meaning* of data over time. Data is a partial (and usually biased) representation of reality. Storytelling is a critical skill for Rev Ops leaders to use to explain and inject meaning into raw data. Tough problems to solve... Leadership, communication, and consensus-building are key. (Or, never mind, that new shiny AI tool magically solves all that for you 🫠 😆 )