According to Adverity’s 2025 survey, CMOs estimate that 45% of the data used to drive marketing decisions is incomplete, inaccurate, or outdated.
Let that sink in. Nearly half of the fuel driving today’s marketing engines is contaminated.
In an era where AI promises smarter, faster insights, bad data slows you down, sure. But worse than that, it accelerates your mistakes. If the inputs are flawed, the outputs are too. AI analytics tools don’t fix cracks in the foundation. They deepen them.
“If you couldn’t automate without AI, you cannot automate with AI.”
So why are so many marketing teams still stuck on the data quality issue?
Our new report, Fixing the Foundation: The State of Marketing Data Quality 2025, is both a wake-up call and a roadmap. Based on a survey of 200 CMOs across the US, UK, Germany, Austria, and Switzerland, it reveals where the real problems lie, how they differ by region and industry, and what the most advanced teams are doing to fix them. Check out the full report here or read on for the highlights.
The idea that marketing suffers from bad data is hardly new. But this year’s research reveals how deeply the issue runs, and just how urgently leaders are beginning to address it.
Asked to identify the single most impactful way to improve marketing performance, the most common answer wasn’t AI, automation, or better analytics. It was improving data quality.
Nearly a third of CMOs (30%) said improving the quality of their data would have the greatest effect on performance. Marketing teams are keenly aware that focusing efforts on data quality as a whole would make a significant impact on their performance, and this urgency has likely been catalyzed by the evolution of analytics tools powered by AI that depend on clean, high-quality data. However, such low levels of data quality across the respondent demographics imply that many of them are still in the early stages.
So, CMOs know that with analytics tools advancing as quickly as they are, improving data quality is a huge opportunity in 2025. But what’s holding them back?
The most common issues cited were completeness, consistency, and uniqueness. That means marketers are struggling to collect all the data they need, struggling to align it across platforms and teams, and struggling to ensure it isn’t duplicated.
While some of these problems are technical, for example, requiring better automation and infrastructure, many stem from deeper operational issues: inconsistent naming conventions, unclear data ownership, and a lack of governance across sources.
And while the issues are widespread, they aren’t identical everywhere.
But whether the challenge is missing data or messy formats, the impact is the same: decisions based on bad data are bad decisions, no matter how sophisticated the tech stack used to make them.
Perhaps the most alarming finding in the report isn’t the scale of the problem. It’s the cognitive dissonance between knowing the value of addressing poor data quality and simultaneously pretending the problem doesn’t exist.
The fact that 85% of CMOs say they trust their data, despite acknowledging its poor quality, suggests a widespread blind spot. That contradiction points to a troubling reality in marketing. Teams have become so accustomed to incomplete or inconsistent data that they’ve stopped questioning it.
Instead, they work around it. They rebuild the same dashboards, layer on more tools, add manual checks, or fall back on intuition when something doesn’t add up. Over time, these workarounds become process. And the opportunity to address the root cause slips further down the priority list.
But with the rise of AI and automated decision-making, that blind spot is becoming impossible to ignore.
“AI doesn’t fix cracks in your foundation, it accelerates them.”
For years, the issue of data quality has been something of an open secret in marketing. While teams know improving data quality would drive better results, the task is a daunting one. Data quality has been talked about as a problem area for so long that the alarm bells have faded into background noise. Low-quality data is accepted as the cost of doing business.
There’s a temptation to believe that the growing capabilities of AI will eventually solve marketing’s data problems. But the reality is more nuanced. The rise of AI in analytics is forcing marketing teams to confront data quality head-on. Not only is high-quality data the fuel for effective AI models, but AI-powered tools now make it faster and easier to clean, transform, and govern data at scale.
Our research shows that the teams with the most automation are also the ones most focused on improving data quality. They’re laying the groundwork to make sure AI adds value, not risk.
In contrast, teams still in the early stages of automation are focused on more immediate concerns like data access and integration. And rightly so: you can’t fix what you can’t reach. But without a plan to improve data quality alongside automation, they risk scaling inefficiency instead of insight.
The most effective teams in our research aren’t treating data quality as a one-off project. They understand it as an ongoing discipline, one that touches every part of the data pipeline, from collection and transformation to reporting and analysis.
They’re investing in:
None of this is particularly flashy. But it’s what separates teams who scale successfully from those who spend half their time putting out fires.
As we put it in the report:
"You don’t need to put out every fire. You just need to stop building things that burn."
Fixing the Foundation: The State of Marketing Data Quality 2025
The data quality crisis in marketing isn’t hypothetical. It’s already here, and it’s already costing millions. Gartner estimates that poor data quality costs organizations $12.9 million per year on average. And that number is likely to rise as AI-driven analytics tools become more widespread, more autonomous, and more trusted.
But the path forward is clearer than ever. Fix the foundations. Prioritize structure over short-term shortcuts. Because when AI accelerates everything, the difference between leading and lagging may come down to one simple question: can you trust your data?