Leadership and ops are TOTALLY aligned on data quality

This year, Openprise teamed up with RevOps Co-op and MarketingOps to survey operations professionals about data quality. With over 150 responses, we gained valuable insights into how people define data quality, the challenges businesses face, and the key differences between teams that excel at it and those that don’t. Get the full report here.

According to our 2025 State of RevOps Survey, leadership teams have achieved perfect alignment with operations on data quality. Everyone’s on the same page, resources are allocated appropriately, and there’s complete agreement about what constitutes “good data.”

Let’s explore all the ways leadership and GTM Ops teams see eye-to-eye on data quality.

#1: “Everyone knows what we mean by ‘good data quality'”

When the CMO says “clean up the data,” everyone nods in perfect understanding. Sales, marketing, and finance all interpret this instruction identically. There’s no confusion about which fields matter, what “clean” means, or how to prioritize tasks.

Companies have a unified definition of data quality that crosses all team boundaries. When the CRO talks about “good data,” the marketing ops team immediately understands what that means—and sales ops gets it, too.

 #2: “Our data quality is perfect for our needs”

Leadership teams have an uncanny ability to assess data quality accurately. When they say the data is “good enough,” operations teams can rest easy, knowing that’s a technical assessment based on deep understanding of the systems, not just a way to avoid allocating resources to fix problems.

Companies are enjoying smooth go-to-market execution thanks to their pristine data. Sales forecasts are accurate, marketing segmentation is precise, and no one wonders if they’re targeting the right prospects with the right messages.

#3: “We just need one tool to fix all our data problems”

Leadership teams are wisely focusing entirely on technology as the solution. After all, humans can’t be expected to enter data correctly or follow processes consistently—that’s what automation is for. One new tool with an impressive demo is all you need to transform your entire data ecosystem. No need for user adoption plans, governance frameworks, or executive mandates. The tool itself will handle all that.

#4: “Data quality is IT’s problem”

Data quality is purely a technical challenge and it belongs solely to IT. Executives recognize this has nothing to do with business processes, organizational culture, or cross-functional alignment.

IT departments everywhere have been given authority over data quality, along with budgets to fix any issues. When the CRM data is a mess, that’s clearly an infrastructure problem—not a sales enablement, marketing operations, or executive sponsorship issue.

RevOps data quality is no joke

If you’re still reading this prank post, you know the state of your GTM data quality is not funny at all.

The reality revealed by our survey shows:

• 79% of organizations don’t have a standard definition of data quality
• 71% of respondents said data quality negatively impacts go-to-market success
• 99% face technical data challenges—but better data quality comes from leadership commitment and organizational alignment, not just tools
• 55% report system adoption isn’t enforced, and 48% say leadership doesn’t understand what’s technically possible

One frustrated survey respondent put it bluntly: 

“No matter how much I stress the importance, leadership believes they can sprinkle some money, and a fairy will just clean it all up. They shy away from having to make big boy/girl decisions.”

The data quality reality gap

The gap between what leaders believe about data quality and what ops teams experience can sometimes feel like a running joke. But it doesn’t have to be.

Our research found that organizations succeeding with data quality are doing four things differently:

1. They share a definition of data quality across departments (25% vs 14% for those with poor data quality)
2. They use designated platforms with automated integration (49% vs 33%)
3. They have established processes for improving data (94% vs 82%)
4. They use custom apps or code for their unique needs (50% vs 31%)

These aren’t companies with bigger budgets or more resources—they’re simply organizations where leadership considers data quality a fundamental business practice that requires ongoing attention and cross-functional alignment.

Be a source of insight, not just data

If you’re an operations professional tired of the disconnect between executive perception and data reality, here are some practical steps to close that gap:

1. Start the conversation about data quality. Get key stakeholders to explicitly define what “good data” means for your organization.
2. Quantify the impact. Show leadership that 70% of companies with poor data quality struggle to make strategic decisions.
3. Focus on enforcement. Make the case that system adoption needs to be mandated from the top.
4. Prioritize what matters. Align on the data that directly impacts revenue and customer experience first.

Bad data quality is no laughing matter. But, with the right alignment, leadership support, and organizational commitment, your company can join the 11% who confidently rate their data quality as “excellent.”

Download the 2025 State of RevOps Survey to discover how leading GTM teams have improved data quality (and how you can too).