Introduction

Marketers waste millions because nearly half their data is unreliable. In the AI era, bad data means bad decisions, faster.

What’s the problem it solves?

The report shows how poor data quality leads to wasted money, wrong insights, and weak campaigns. It explains why fixing data is the single biggest way to improve marketing results, especially as AI makes errors spread faster.

Quick Summary

Adverity’s 2025 report reveals a big problem: 45% of marketing data is incomplete, outdated, or wrong. That means almost half of the information marketers use to plan campaigns, set budgets, and report results cannot be trusted. Many teams know this, yet still accept bad data as “normal.”

This is especially risky now that AI tools are everywhere. AI doesn’t fix weak data - it just makes mistakes happen faster and at larger scale. If your data is broken, your AI-driven insights will be broken too. That’s why 30% of CMOs say improving data quality is the single most powerful way to boost performance, ahead of automation and access.

The main problems with marketing data today are missing data, inconsistent formats, and duplicate records. Fixing these requires clear ownership, automated workflows, and governance. Teams with low automation worry more about pulling data together. Teams with high automation focus on keeping it clean and consistent. By industry, financial services care most about accuracy, while agencies wrestle with consistency and eCommerce struggles with completeness.

Key Takeaways

• 45% of marketing data is unreliable (incomplete, inaccurate, or outdated).
• CMOs rank data quality as the top lever for marketing performance.
• AI makes poor data riskier: bad input equals bad output, faster.
• Biggest issues: completeness (31%), consistency (26%), duplicates (16%).
• Many marketers “trust” data they admit is flawed - showing complacency.
• Fixing data quality means setting ownership, automating checks, and monitoring for errors.

What to do

• Audit your current data to see how much is incomplete or outdated.
• Make data quality a company-wide priority, not just a side project.
• Automate data collection, cleaning, and validation to catch errors early.
• Create clear rules and ownership for data across teams.
• Focus first on completeness and consistency before chasing advanced AI tools.
• Treat data quality as an ongoing discipline, not a one-time fix.

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