AI has raised the bar for what qualifies as “good data.”
The conversation is no longer about how much data marketers can collect but how well that data reflects reality. In this new era, success depends on data that is accurate, fresh, consented and interoperable – principles that ensure AI models learn from accuracy.
Let’s define terms:
- Accurate: Verified and anchored in real human identity.
- Fresh: Continuously updated to reflect today’s consumers.
- Consented: Collected and governed transparently.
- Interoperable: Easily integrated across platforms through a secure, signal-agnostic identity spine, enabling seamless data activation.
This is the data AI can trust and the data that keeps marketing relevant, predictive and privacy-first.
Why data accuracy matters more than ever
AI models are only as intelligent as their inputs.
As marketing moves toward agentic advertising, where autonomous systems handle campaign buying and optimization, data accuracy becomes a competitive differentiator. If your ad server or audience data is flawed, these new AI agents will simply automate bad decisions faster.
The most advanced AI systems apply rigorous quality filters and conflict resolution logic to validate that every signal reflects real behavior. As data pipelines become more complex, marketers must confront a new truth: Deterministic signals aren’t automatically reliable; they require constant validation, deduplication and context to identify meaningful behavior.
The systems that succeed will be those built on verified human identity, where every data point is anchored in reality, not assumptions.
The case for fresh data
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Outdated data can’t predict tomorrow’s behavior. AI thrives on recency. Consumer intent now shifts at algorithmic speed. In an environment defined by constant change, including economic trends, cultural moments and viral influence, AI systems that rely on stale data are already behind.
Audiences should be refreshed continuously to mirror real-world signals, from purchase intent to media habits, so every campaign reflects what’s happening now, not six months ago.
But freshness alone isn’t enough. With predictive insights, models must go beyond describing the past. They must forecast behaviors, fill gaps with inferred attributes and recommend next-best audiences, helping marketers anticipate opportunity before it happens.
The real challenge for marketers isn’t data collection but continuous calibration, ensuring that models remain dynamic, adaptive and predictive as behaviors evolve.
Consent and governance build trust in AI
Responsible AI starts with responsible data.
As data privacy laws expand, compliance has evolved from a regulatory necessity into a business imperative. But true governance goes beyond legal adherence; it’s the foundation for sustainable AI innovation. Systems built on transparent consent frameworks earn the trust that fuels long-term adoption and consumer confidence.
Today, privacy and compliance should be built into data platforms. Every data signal, attribute, audience and partner should go through rigorous review processes to meet federal, state and local consumer privacy laws.
In the next wave of AI development, governance won’t be the cost of innovation; it will be its catalyst.
Interoperability enables AI’s full potential
AI delivers its best insights when data connects seamlessly across fragmented environments. The fragmentation of the marketing ecosystem is one of AI’s biggest challenges. Data interoperability and the ability for systems to connect, communicate and learn across environments is what turns isolated signals into meaningful intelligence.
When signals connect across environments, AI gains a more complete view of the customer journey, revealing true behavior patterns, intent signals and cross-channel impact that would otherwise remain hidden.
Interoperability will define which AI systems can see the whole picture of the customer journey and which remain trapped in silos.
Where AI and human oversight meet
AI can find patterns at scale, but human perspective gives those patterns purpose. The most effective marketers will be those who design feedback loops between machine learning and human judgment, turning automation into augmentation.
AI-powered models surface connections, recommend audiences and uncover insights that would take humans months to find. But experts shape the process, crafting the right inputs, ensuring data quality, reviewing model outputs and refining recommendations based on industry knowledge and client goals.
It’s this partnership between advanced AI and experienced people that turns predictions into actionable, trustworthy solutions.
Why the future of AI depends on ‘good’ data
The future of AI-driven marketing will reward those who treat data not as a commodity but as a living ecosystem: accurate, current, ethical and connected. In that future, “good data” isn’t just what fuels AI; it’s what makes marketing more intelligent, accountable and human.
Leaders will:
- Operate with clear data principles grounded in transparency and truth
- Build consent and compliance into every workflow
- Keep data accurate, current and interoperable
- Pair automation with human oversight
AI success starts with good data. And good data starts with Experian, where accuracy, privacy and purpose come together to make marketing more human, not less.
For more articles featuring Budi Tanzi, click here.
