The age of AI has amplified the importance of high-quality data which has long been the backbone of effective marketing. However, algorithms are only as good as the information they process. As these algorithms begin to work at an unprecedented scale and speed, their performance is defined by the quality of input data.
For marketers and agencies, this reality changes how third-party data is evaluated and raises the stakes for making the right choice.
AI raises the bar for data quality
AI can process more information than any human team, but it cannot discern truth from error without guidance. This plays out most visibly in audience targeting. For instance, while creating predictive audiences: segments built by AI and machine learning models to forecast the specific consumer groups who are best poised to take a desired action.
The models draw from a mix of signals: past purchase history, intent activity, demographic information, and contextual behaviour. If these inputs are clean, verified, and well-structured, the resulting audiences are more precise and performant. If the data is flawed, the output deteriorates, propagating inaccuracies at speed and scale.
This is the core reason why AI makes data quality non-negotiable. Inaccurate, incomplete, or poorly sourced third-party data can erode campaign performance, trigger compliance risks, and misdirect spend. With AI at the centre of decisioning, small flaws can rapidly multiply into big problems.
Accurately evaluating third-party data for the AI era
For buyers, assessing the quality of third-party data requires additional rigour, moving past traditional checks for basic accuracy and coverage. Instead, marketers must focus on three areas that matter the most in an AI-driven environment.
Trust: Confidence in a data provider starts with transparency. Marketers should expect clarity around data sourcing, how the information is validated, and the frequency with which it is refreshed. Independent certifications—such as IAB Tech Lab’s Data Transparency Standard or privacy scores from neutral auditors—offer concrete assurance that data meets baseline quality and compliance standards. In an AI context, trust serves as a safeguard against “black box” risks where the source of errors is hidden.
Stability: AI models need consistent, persistent inputs to perform reliably over time. Data providers should demonstrate continuity in sourcing, resilience in operations, and adaptability to shifts in the regulatory and technical environment. Providers that are deeply embedded in the global ecosystem, with established compliance frameworks and strong industry participation, are better positioned to deliver stable, dependable datasets for ongoing AI activation.
Interoperability: As campaigns span multiple platforms, IDs, and activation environments, it becomes essential to connect data across systems. AI models can optimise effectively only if the data flows freely across DSPs, CDPs, cloud environments, and analytics systems. Evaluating a provider’s ability to support ID-agnostic activation and seamless integration into the existing martech stack is now a key part of due diligence.
An example of AI-ready data in practice
Eyeota, a Dun & Bradstreet company, has built its audience solutions around the same principles that define high-quality data in the AI era. Its Predictive Audiences capability uses advanced machine learning models to identify individuals most likely to take a desired action—whether that’s a purchase, subscription, or brand interaction—based on verified, privacy-compliant source data.
Eyeota’s Predictive Audiences delivers results because the underlying inputs are carefully curated and maintained. The company invests in transparency, with independent verification of privacy standards and data sourcing. Its global scale and integration into the D&B identity infrastructure provides stability, ensuring consistent data performance over time across both B2C and B2B audiences and environments. With a focus on interoperability, Eyeota enables activation across demand-side platforms, marketing clouds, and other environments where agencies and brands collaborate.
Choosing data that can keep up with AI
As AI moves from experimentation to everyday practice in marketing, there will be a sharper focus on the definition of quality data. Providers who can offer verified, stable, and interoperable datasets will create benchmarks and set the standards for an AI-driven ecosystem.
The challenge for buyers is to move beyond surface-level evaluations and insist on the attributes that make AI work as intended. More than merely checking a box, this calls for informed scrutiny and a willingness to align with partners who can back up their claims.
AI will continue to reshape marketing in profound ways. The question for agencies and brands is whether their data will keep pace—or hold them back.
(Except for the headline, this story has not been edited by PostX News and is published from a syndicated feed.)
