Marketing leaders have never managed more customer information than they do today. Yet having access to millions of records doesn’t guarantee you actually know your customer.
Privacy changes continue to restrict how data is collected and unified. Buyer interactions are scattered across dozens of disconnected platforms. On top of that, AI features are injecting a fresh stream of generated and inferred signals into already complex data environments.
It all leads to a critical operational challenge for marketing organizations: How much of your customer data can you actually trust when driving key strategic decisions?
At the MarTech Conference, which takes place online Sept. 2, 2026, and is free to attend, the session “The data trust crisis: Why your customer data is getting worse,” breaks down what is actively undermining data quality — and how marketing teams can continue delivering hyper-relevant personalization when datasets fall short of perfection.
More data doesn’t equal better data
Marketing stacks are drowning in information. Every touchpoint generates another signal regarding buyer intent, interest, or behavior.
Connecting those signals into an accurate, actionable view of the customer grows more complex by the day.
Regulatory updates and browser policy shifts restrict data capture at every turn. Fragmented platforms create contradictory customer records. Meanwhile, AI-inferred inputs demand extra validation before teams can safely rely on them for targeting.
During this session, Craig Howard, chief solutions officer at Actable; Ana Mourão, founder and author of The Experimental Marketer Framework; Ryan Warren, chief CRM officer at Razorfish; and Zack Wenthe, director of product marketing and customer data evangelist at Tealium, will evaluate the technical and regulatory forces widening the gap between how reliable your team thinks your data is and how accurate it actually is.
When data decay compromises the customer experience
The consequences of poor data quality extend far beyond database hygiene.
Personalization depends on calculated assumptions about buyer behavior. When underlying inputs are incomplete, outdated, or inaccurate, those assumptions collapse.
That ripple effect extends well beyond a mismatched email campaign. Data decay destabilizes audience segmentation, journey orchestration, attribution, and spend optimization—leading automated systems to execute sophisticated decisions using compromised inputs.
As AI engines take over key decision-making workflows, feed hygiene becomes a core business driver. In this session, you’ll learn how to identify these vulnerabilities before data decay erodes the customer experience.
Performing with imperfect information
Achieving flawless customer data isn’t a realistic benchmark. A far more strategic question is how much uncertainty your organization can accommodate while still making confident, high-performing decisions.
Navigating this reality requires clear frameworks to evaluate signal quality, map coverage gaps, and determine when inputs are reliable enough to execute against.
Instead of treating every data point as equally trustworthy, modern marketing teams can deploy confidence-scored approaches that adapt to varying levels of completeness. This approach ensures your go-to-market strategies remain resilient, even when the broader customer picture remains partially obscured.
Master your data strategy with complete confidence
Navigating data uncertainty is completely doable when equipped with the right governance playbooks. Join us for this free interactive session on Sept. 2, 2026, to arm your team with the insights required to audit datasets, protect personalization, and drive growth with complete trust in your metrics.
View the agenda and register for the MarTech Conference for free.
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