RevOps maturity goes beyond team structure or tool selection. Even well-funded teams with access to the right technologies often find themselves stalled, as we discovered in our last blog on RevTech stacks. The root cause here is often buried inside the foundation RevOps is built on: data.
When data is inconsistent, duplicated, or siloed, no amount of automation, AI, or reporting dashboards can mask the problem. In this third and final blog from our 2025 RevOps and RevTech study series, we look at how poor data quality is quietly undermining revenue strategies and what the most effective RevOps teams are doing to fix it.
Even when the right tools are in place, data challenges often prevent those systems from working as intended. To understand just how deeply data quality affects RevOps performance, we surveyed over 250 B2B professionals.
We asked about the frequency of data inconsistencies, their impact on pipeline and decision-making, and the ways teams are using technology like AI to address the problem.
The findings confirm that poor data is more than a technical nuisance. It is one of the most notable threats to RevOps maturity and revenue performance in 2025. Let’s look at a summary of some key insights from the study in this aspect.
RevOps teams today have more tools than ever to support forecasting, pipeline visibility, and campaign performance. But beneath that surface, these tools are only as good as the data that fuels them. Most teams hit a wall here.
According to our 2025 study, 75% of RevOps professionals cite data inconsistencies as the most frustrating part of their tech stack. These are not occasional sync delays. They are daily roadblocks like duplicate records, mismatched fields, and fragmented systems that limit confidence in even the most basic performance metrics.
So, it is no surprise that only 16% of respondents say their RevOps tech provides strong, data-driven insights that lead to revenue-impacting decisions. This is despite growing investments in automation and AI.
The message is clear: Technology alone cannot fix data issues when ownership is unclear and cross-functional alignment is missing. Most of the time, messy data reflects deeper organizational gaps in responsibility and process.
The impact of bad data is directly tied to lost revenue opportunities. In fact, 48% of professionals in our survey said that poor data quality results in inefficient pipeline management. Some other reported problems include misalignment between marketing and sales or missed revenue altogether.
These issues are not isolated, as nearly half of all respondents said data inconsistencies occur frequently or constantly across their systems. That kind of instability creates ripple effects across teams. Forecasts become unreliable, attribution modeling breaks down, and campaign performance becomes harder to measure or optimize.
The cost is reflected in misinformed decisions, missed follow-ups, and fractured GTM alignment. Yet many organizations continue to treat data quality like a back-office task rather than a central part of their revenue strategy.
There is a growing recognition among more mature RevOps teams that clean data is a competitive advantage. The majority of teams leveraging AI effectively are currently using it for tasks like data cleanup and enhancement. In addition, they are formalizing governance structures, setting standards across systems, and working with IT, sales, and marketing to reduce inconsistencies at the source.
What sets these organizations apart is not how many tools they have or how much they spend. It is the clarity of their approach. They create shared dashboards, align update cycles, and agree on unified definitions so that everyone from marketing ops to the C-suite is working from the same foundation. Briefly, these teams go beyond just buying tools to build processes that make data quality a shared responsibility. And they are seeing the results.
Among organizations with RevOps teams that have been in place for three or more years, 63% report that their tech stack contributes directly to revenue growth. That number drops to just 35% in younger teams. This demonstrates that handling data as a core resource leads to clear, tangible results.
Data quality is more than a technical concern. It serves as a foundational element for RevOps success. When it breaks down, so does the ability to scale, align, and drive revenue predictably.
In one line, stronger data results in stronger RevOps. Here is what teams should focus on to ensure this:
RevOps leaders who take data seriously are already seeing improvements in pipeline visibility, forecasting confidence, and executive trust. Those who delay will continue fighting uphill battles with avoidable friction.
This series gave you a lens into how RevOps is evolving, where RevTech is falling short, and how data challenges are holding teams back. However, the full 2025 State of RevOps and RevTech in GTM report offers a much deeper look into benchmarks, AI usage, team structures, and cross-functional dynamics that are shaping how B2B organizations operate today.
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