At A Glance
Trustworthy AI depends on AI data governance. Automated systems rely on governed data that is accurate, fresh, consented, and interoperable at every stage. Without governance at the data foundation, organizations lack visibility, accountability and confidence in AI-driven decisions. Responsible automation begins with privacy-first data governance that supports transparency, compliance, and long-term sustainability.Why AI data governance determines trust in automated decisions
AI is reshaping audience strategy, media investment, and measurement. Automated systems now make more decisions at scale and in real time. Trust in those decisions depends on the data that informs them.
AI data governance provides the framework that allows organizations to answer foundational questions like:
- Which information or inputs guided this decision?
- Is the model respecting consumer rights?
- Could bias be influencing the outcome?
- If AI made the wrong call, how would we know?
Without governed data, these questions remain unanswered. AI data governance creates accountability by establishing quality controls, consent validation and auditability before data enters automated systems.
Most organizations are still building their readiness to govern data at scale. Many vendors highlight “fast insights” or “transparent reporting,” but few can support true data governance — the auditability, privacy-by-design, quality controls, and continuous compliance required for responsible AI.
That foundation is where responsible automation begins. And it’s why trust in AI starts with data governance.
Responsible automation begins with governed data
Automation produces reliable outcomes only when data is accurate, current, consented and interoperable. AI data governance makes responsible automation possible by applying controls before data reaches models, workflows, or activation channels.
AI systems may interpret context, predict signals, and act in real time. But no model, logic layer, or LLM can be responsible if the data feeding it isn’t governed responsibly from the start.
This raises a core question: How do we ensure AI systems behave responsibly, at scale, across every channel and workflow?
The answer begins with trust. And trust begins with AI data governance.
Governing the data foundation for responsible AI
Experian’s role in AI readiness begins at the data foundation. Our focus is on rigorously governing the data foundation so our clients have inputs they can trust. AI data governance at Experian includes:
By governing data at the source, we give our clients a transparent, accurate, and compliant starting point. Clients maintain responsibility for bias review within their own AI or LLM systems — but they can only perform those reviews effectively when the inputs are governed from the start.
This is how AI data governance supports responsible automation downstream.
Our2026 Digital trends and predictions reportis available now andreveals five trends that will define 2026. From curation becoming the standard in programmatic to AI moving from hype to implementation, each trend reflects a shift toward more connected, data-driven marketing. The interplay between them will define how marketers will lead in2026.
Privacy-by-design strengthens AI data governance
Privacy gaps compound quickly when AI is involved. Once data enters automated workflows, errors or compliance issues become harder, and sometimes impossible, to correct. AI data governance addresses this risk through privacy-first design.
Experian privacy-first AI data governance through:
- Consent-based, regulated identity resolution
- A signal-agnostic identity foundation that avoids exposing personal identifiers
- Ongoing validation and source verification before every refresh and delivery
- Compliance applied to each delivery, with opt-outs and deletes reflected immediately
- Governed attributes provided to clients, ensuring downstream applications remain compliant as data and regulations evolve
Experian doesn’t govern our client’s AI. We govern the data their AI depends on, giving them confidence that what they load into any automated system meets the highest privacy and compliance standards.
Good data isn’t just accurate or fresh. Good data is governed data.
How AI data governance supports responsible automation at scale
With AI data governance in place, organizations can build AI workflows that behave responsibly, predictably, and in alignment with compliance standards.
Responsible automation emerges through four interconnected layers:
Together, these layers show how data governance enables AI governance.
AI integrity starts with AI data governance
Automation is becoming widely accessible, but responsible AI still depends on governed data.
Experian provides AI data governance to ensure the data that powers your AI workflows is accurate, compliant, consented, and refreshed with up-to-date opt-out and regulatory changes. That governance carries downstream, giving our clients confidence that their automated systems remain aligned with consumer expectations and regulatory requirements.
We don’t build your AI. We enable it — by delivering the governed data it needs.
Experian brings identity, insight, and privacy-first governance together to help marketers reach people with relevance, respect, and simplicity.
Responsible AI starts with responsible data. AI data governance is the foundation that supports everything that follows.
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About the author

Jeremy Meade
VP, Marketing Data Product & Operations, Experian
Jeremy Meade is VP, Marketing Data Product & Operations at Experian Marketing Services. With over 15 years of experience in marketing data, Jeremy has consistently led data product, engineering, and analytics functions. He has also played a pivotal role in spearheading the implementation of policies and procedures to ensure compliance with state privacy regulations at two industry-leading companies.
FAQs about AI data governance
AI data governance is the framework that manages data quality, consent, compliance and auditability before data enters AI systems.
AI decisions reflect the data used as inputs. Governance provides transparency, accountability and trust in automated outcomes.
AI data governance does not eliminate bias in models. It provides governed inputs that allow organizations to identify and address bias more effectively.
Privacy-first governance applies consent validation and compliance controls before data is activated, reducing downstream risk.
Organizations govern their AI systems. Data providers govern the data foundation that feeds those systems.
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In this article…How data collaboration is evolving from 2023 to 2024How to create efficient data collaboration strategies We live in a data-driven world, and businesses need effective data collaboration strategies to remain successful. Before you determine your 2023 and 2024 data collaboration options, it’s essential to understand what data collaboration is. In short, it involves sharing and combining data from multiple sources to better understand a customer base and make informed marketing decisions. Read on to learn more about our three-step plan to create new data collaboration strategies, how it’s evolving, and what we do to ensure our solutions help maintain your company’s data privacy. How data collaboration is evolving from 2023 to 2024 Data collaboration strategies continually evolve thanks to changing industry dynamics and new technologies. As we move from 2023 to 2024, we’ll likely see collaboration extending outside businesses, meaning data can be shared with external partnerships in the form of a data ecosystem. A data ecosystem is a platform that combines numerous information points, including packages, algorithms, and cloud-computing services, to allow businesses to store, analyze, and use the data they’ve collected. To ensure you’re ready for 2024 data collaboration, you’ll need to take a forward-thinking approach toward new data strategies. How to create efficient data collaboration strategies Here are our three steps for efficient collaboration to make the most of 2023 data collaboration and prepare for 2024. Identify your collaboration goal What are you hoping to gain from data collaboration? Do you understand the audience you’re trying to target and what you want regarding outcomes? To measure your success, you should set short- and long-term goals surrounding data collaboration in 2023 and 2024. 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By collaborating on data, you can gather essential insights without relying on cookies. This means you’ll still get the information you want while complying with privacy regulations. Choose the right collaboration partner Before you choose a data collaboration partner, it’s essential to ensure their privacy standards align with yours. How do they collect data and use it ethically and responsibly? At Experian, we are dedicated to protecting consumers and delivering responsible and transparent data practices. We focus on five Global Data Principles — security, accuracy, fairness, transparency, and inclusion — to ensure we treat data carefully and respectfully while boosting economic growth and resilience in the marketing environment. When you partner with us for data collaboration, you can trust that your data is protected in a system built for 2023 data collaboration needs — both known and unknown — while still evolving for 2024 and beyond. Choose a secure environment for collaboration Data collaboration security is vital to safeguard your business and consumers’ information. You can make sure your new data collaboration options are protected in several ways. We’ve outlined three options below. Collaboration in clean rooms Clean rooms are secure, private environments where data is shared and analyzed without exposing the underlying raw data. This ensures that sensitive information remains protected and insights are discovered securely. Experian has vetted clean room partners if this is an option you prefer while still getting industry-leading identity resolution. Collaboration directly Collaborating directly with your partner can be a good option if you have robust security measures. Encryption, access controls, and regular audits are essential to maintain data security in direct collaborations. Collaboration with Experian We excel at meeting our clients where they are and accommodating their technical capabilities and how they manage their data. We offer a secure and compliant environment for data collaboration. Our data collaboration solutions are designed to protect your data while enabling deeper insights. At Experian, we understand the importance of data privacy, and our platform reflects our commitment to safeguarding your information. Enable deeper insights and activation with Experian’s data collaboration solution Data collaboration is crucial in today’s business world, and Experian’s solutions are designed to help you bring together your 2023 and 2024 data collaboration strategies securely and efficiently. With Experian, you can unlock deeper insights, resolve digital identities, and confidently navigate the evolving data privacy landscape. If you’re looking for the right partner to enhance data collaboration to drive growth and innovation in your business, you’ll find a secure environment and the right partner with Experian. Contact us today to get started. Contact us Latest posts

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Centralized data access is emerging as a key strategy for advertisers. In our next Ask the Expert segment, we explore this topic further and discuss the importance of data ownership and the concept of audience as an asset. We're joined by industry leaders, Andy Fisher, Head of Merkury Advanced TV at Merkle, and Chris Feo, Experian’s SVP of Sales & Partnerships who spotlight Merkle's commitment to centralized data access and how advertisers can use our combined solutions to navigate industry shifts while ensuring consumer privacy. Watch our Q&A to learn more about these topics and gain insights on how to stay ahead of industry changes. The concept of audience as an asset In order to gain actionable marketing insights about your audience, you need to identify consumers who are actively engaged with your brand and compare them against non-engaged consumers, or consumers engaged with rival brands. 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These are later published to various endpoints for activation. The Merkury platform covers three classes of data: Proprietary data set – Permissioned data set covering the entire United States, compiled from about 40 different vendors Marketplace data – Includes contributions from various vendors like Experian First-party data from marketers – Allows marketers to bring in their own data Merkury's identity platform empowers brands to own and control first-party identity at an individual level, unifying known and unknown customer and prospect records, site and app visits, and consumer data to a single, person ID. This makes Merkury the only enterprise identity platform that combines the accuracy and sustainability of client first-party data, quality personally identifiable information (PII) data, third-party data, cookie-less media, and technology platform connections in the market. 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This enables you to turn data into actionable insights and makes it possible to target specific individuals within a household or consider the household as a whole. How Experian and Merkle work together Experian and Merkle have established a strong partnership that magnifies the capabilities of Merkle's Merkury platform. With Experian’s robust integration capabilities and extensive connectivity opportunities, customers can use this technology for seamless direct integrations, resulting in more effective onboarding to various channels, like digital and TV. "Experian's role in Merkury's data marketplace is essential as they are considered the gold standard for data. It significantly contributes to our connectivity through direct integrations and partnerships. Experian's presence in various platforms and technologies ensures easy connections and high match rates. Our partnership is very important to us."andy fisher, head of merkury advanced tv Through this partnership, Merkle can deliver unique, personalized digital customer experiences across multiple platforms and devices, highlighting their commitment to data-driven performance marketing. Watch the full Q&A Visit our Ask the Expert content hub to watch Andy and Chris's full conversation about data ownership, innovative strategies to empower you to overcome identity challenges, and navigating industry shifts while protecting consumer privacy. Tune into the full recording to gain insights into the captivating topics of artificial intelligence (AI), understanding how retail networks can amplify the value of media, and the growing influence of connected TV (CTV). Dive into the Q&A to gain rich insights that could greatly influence your strategies. Contact us today About our experts Andy Fisher, Head of Merkury Advanced TV As the Head of Merkury Advanced TV, Andy's primary responsibility is driving person-based marketing and big data adoption in all areas of Television including Linear, Addressable, Connected, Programmatic, and X-channel planning and Measurement. Andy has held several positions at Merkle including Chief Analytics Officer and he ran the Merkle data business. Prior to joining Merkle, Andy was the EVP, Global Data & Analytics Director at Starcom MediaVest Group where he led the SMG global analytics practice. In this role, he built and managed a team of 150 analytics professionals across 17 countries servicing many of the world’s largest advertisers. Prior to that role, Andy was Vice President and National Lead, Analytics at Razorfish, where he led the digital analytics practice and managed a team of modeling, survey, media data, and business intelligence experts. He and his team were responsible for some of the first innovations in multi-touchpoint attribution and joining online/offline data for many of the Fortune 100. Andy has also held leadership positions at Personify and IRI. Andy holds a BA in mathematics from UC Berkeley and an MA in statistics from Stanford. Chris Feo, SVP, Sales & Partnerships, Experian As SVP of Sales & Partnerships, Chris has over a decade of experience across identity, data, and programmatic. Chris joined Experian during the Tapad acquisition in November 2020. He joined Tapad with less than 10 employees and has been part of the executive team through both the Telenor and Experian acquisitions. He’s an active advisor, board member, and investor within the AdTech ecosystem. Outside of work, he’s a die-hard golfer, frequent traveler, and husband to his wife, two dogs, and two goats! Latest posts