This year, Openprise partnered with RevOps Co-op and MarketingOps to conduct a survey on data quality. This collaborative effort resulted in over 150 responses from operations professionals, giving us new insights into how people define data quality, what holds businesses back from achieving better data quality, and what patterns differentiate teams that achieve good data quality from the rest. Get the full report here.
Imagine trying to navigate a dark highway with faulty GPS.
You're cruising confidently, believing every direction. But each prompt leads you slightly astray, telling you to turn left when you should've turned right, promising open roads when you're heading into traffic.
Suddenly, you realize you're miles from your destination, your confidence is shot, and the cost of the detour is mounting rapidly.
That's precisely what happens when your go-to-market strategy relies on poor-quality data: resources are wasted, decisions unravel, and opportunities vanish.
According to the 2025 State of RevOps Survey, a staggering 71% of companies reported that poor data quality negatively impacts the success of their go-to-market activities.
That means more than two-thirds of GTM Ops teams are driving in the wrong direction. That's not just an operational headache—it actively undermines business outcomes in ways that may not be immediately obvious but that have far-reaching consequences.
In trying to understand the true impact of data quality challenges, what we discovered was both revealing and concerning: data quality issues go far beyond simple technical problems into the heart of how businesses make decisions—and they're wreaking havoc on GTM strategies.
With bad data, your GTM strategy is stuck in neutral
Your team is gearing up for a crucial product launch.
Campaigns are set, messaging is tested, and sales is briefed. But as you dive into the data for final targeting, the cracks start showing: duplicate records, inconsistent information, and prospect details just…missing. Meetings to finalize strategy spiral into second-guessing and rechecking numbers. Your momentum stalls, and your promising strategy is stuck in neutral, held hostage by data you can't trust.
The numbers paint a stark picture.
Only 11% of RevOps professionals consider their customer and prospect data to be of "excellent" quality, while nearly half (47%) openly admit their data needs improvement.
This isn't just a minor inconvenience—it's creating decision paralysis at critical moments.
Among organizations with poor data quality, 70% report difficulty making strategic decisions based on their customer and prospect data. Perhaps more surprising is that even among those who rated their data as "good enough," 52% still admitted that data quality negatively impacts their go-to-market activities.
Lack of clarity leads to lack of strategy, said one frustrated respondent.
Data distrust derails GTM execution
Every ops professional knows the feeling: you've meticulously prepared quarterly insights, only to have the discussion hijacked by skeptical teams questioning every number. These moments don't just derail meetings, they undermine morale and lead to costly strategic misfires.
When departments can't trust each other's data, the impact ripples through every aspect of go-to-market execution. Meetings that should focus on strategic decisions and market opportunities instead devolve into debates about whose numbers are correct.
The result is a domino effect of other organizational issues, such as:
- Ineffective targeting that wastes marketing spend
- Unreliable forecasting sabotages strategic planning
- Misalignment between sales and marketing causing wasted resources and internal conflict
We have many disagreements about what data we should use as our guiding light. The data that marketing needs is different than Sales and causes frustrations across the two orgs. one respondent said.
This misalignment doesn't just waste time in meetings—it fundamentally undermines your ability to execute a cohesive go-to-market strategy. When teams can't agree on basic metrics like qualified leads, pipeline value, or customer acquisition costs, the entire GTM engine sputters.
Top data quality issues: missing data, duplicates, and non-standardized data
Our research revealed that technical data challenges are nearly universal across organizations; 99% of respondents reported struggling with at least one dimension of technical data quality.
The most common issues were:
- Missing or incomplete data: cited by 93% with poor data quality, and 67% with acceptable data quality
- Duplicate records: 85% with poor data quality, 65% with acceptable data quality
- Data not being standardized: 77% with poor data quality, 42% with acceptable data quality
Fortunately, not all teams are stranded.
Successful companies have found ways to recalibrate their data GPS. For one, they're significantly more likely to use a designated platform that's automatically integrated with other tools and systems (49% vs 33% for those with poor data quality).
More importantly, these companies have established a shared definition of data quality across departments (25% vs 14%). Organizations succeeding in GTM execution aren't chasing perfection—they're strategic about prioritizing what's critical.
Interestingly, company size doesn't correlate strongly with data quality success. Enterprise organizations report the same frequency of data quality challenges as their smaller counterparts. The data quality crisis is truly an equal opportunity affliction.
Data quality requires leadership alignment
Our research revealed a surprising truth: while technical accuracy is universally challenging, the most significant barriers to good data quality are organizational, not technical.
Among companies struggling with data quality:
- 79% lack a standard definition of what "good data" means
- 55% report that adoption of key systems isn't enforced
- 48% say their leadership teams don't understand what's technically possible
The data quality crisis is as much a leadership and alignment issue as it is a technical one. Without clear definitions, enforcement of best practices, and realistic expectations from leadership, even the most sophisticated data tools and processes will fall short.
No matter how much I stress the importance, leadership believes they can sprinkle some money, and a fairy will just clean it all up, one ops professional noted.
Data quality management is a business practice, not a technical problem
Through our analysis, a clear pattern emerged among organizations that maintain better data quality. The difference isn't about company size or industry—it's about their approach to data quality as a fundamental business practice rather than a technical problem to be solved.
The organizations pulling ahead share a common operating model. They don't treat data quality as a clean-up project — they build it into the infrastructure of how their GTM engine runs. Here's what that looks like in practice, based on results from Openprise customers:
- CrowdStrike deployed AI-powered behavior scoring with region-specific logic, achieving a 100% improvement in Inquiry to Open Opportunity conversion rate. That result was only possible because the underlying lead and account data was clean, standardized, and enriched consistently — giving the AI model a reliable signal to work with.
- Denodo automated persona classification across their entire contact database, successfully classifying over 90% of all leads and 94% of contacts. The AI resolved complex job title exceptions that manual tagging had missed for years — but the foundation was a data quality management process that had normalized and deduplicated the records first.
These aren't edge cases. They're the expected outcome when data quality management is treated as a continuous, automated business function rather than a periodic cleanup sprint. The common thread across high-performing GTM teams:
- Clear definition: unified, cross-departmental definitions tied to business goals
- Leadership buy-in: committed executives enforcing and investing in quality
- Automated integration: seamless data flow between tools
- Continuous improvement: established processes, not one-time fixes
- Customized solutions: tailored to your unique needs
The most successful organizations don't strive for perfect data across all dimensions—they focus on what truly moves the needle, ensuring their most critical data is as accurate and complete as possible while maintaining a realistic outlook on less essential data.
Most importantly, they treat data quality not as a one-time fix or a purely technical problem, but as an ongoing business practice that requires continuous attention, cross-functional alignment, and leadership commitment.
Poor data quality isn't a problem that fixes itself. It's a persistent drain on resources, morale, and business performance. The best RevOps teams understand this, tackling data quality proactively to ensure it's an asset, not a liability.
Manage data quality like a world-class Revops team
Download the 2025 State of RevOps Survey for the benchmark data and proven frameworks behind better data quality — and read the Executive Imperative for AI report to understand how clean data becomes your competitive advantage in the AI era.
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