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: Model governance reviews before releasing new modeled attributes Feature-level checks ensuring no prohibited or sensitive signals are included Compliance-aware rebuilding and re-scoring, incorporating opt-outs and regulatory changes Validated delivery, ensuring attributes reflect the most current opt-outs, deletes, and compliance requirements 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. Our 2026 Digital trends and predictions report is available now and reveals 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 in 2026. Download 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: 1. Input Privacy-first, governed data: accurate, consented, continuously updated, and compliant. 2. Enrichment Predictive and contextual insights built from governed data, ensuring downstream intelligence reflects current and compliant information. 3. Orchestration Reliable, AI-powered workflows where governed data inputs ensures consistency in audience selection, activation, and measurement at scale. 4. Guardrails Transparent, responsible innovation. Clients apply their own model governance, explainability, and oversight supported by the visibility they have into Experian’s governed inputs. 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. Get started 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 What is AI data governance? AI data governance is the framework that manages data quality, consent, compliance and auditability before data enters AI systems. Why does AI data governance matter? AI decisions reflect the data used as inputs. Governance provides transparency, accountability and trust in automated outcomes. Does AI data governance prevent bias? AI data governance does not eliminate bias in models. It provides governed inputs that allow organizations to identify and address bias more effectively. How does privacy-first design support AI data governance? Privacy-first governance applies consent validation and compliance controls before data is activated, reducing downstream risk. Who is responsible for AI governance? Organizations govern their AI systems. Data providers govern the data foundation that feeds those systems. Latest posts

How third-party data has changed and why it matters in 2025 For years, third-party data operated in an expansive, lightly regulated marketplace: fast-moving, high-growth, and filled with players eager to capitalize on digital marketing’s demand for audience insights.   That era is over. Regulatory scrutiny, stricter compliance standards, and rising consumer expectations have already transformed the market. Today, third-party data belongs to partners with proven expertise and built-in compliance. This isn’t a space for opportunistic newcomers; it’s one that rewards long-term commitment and trust.  Even the rapid rise of retail media networks (RMNs) reflects this shift. These platforms are built on long-standing, trusted relationships between brands, retailers, and data partners, utilizing that foundation in new ways to reach audiences responsibly and effectively. The best providers have already made this transition; those still “shifting” are catching up. From growth to governance: A market defined by accountability The third-party data ecosystem has matured. After years of rapid expansion and recalibration, the market has stabilized around a new standard: data quality and regulatory accountability. Third-party data enriches first-party insights with attributes such as income, gender, and interests that round out the customer view. But when the industry grew unchecked, unreliable providers diluted quality and trust. This resulted in a decline in the overall value and reliability of the third-party data marketplace. That breakdown led directly to today’s privacy laws, now active across more than 20 U.S. states and numerous countries worldwide. These regulations reflect a permanent consumer expectation: relevance delivered responsibly. Consumers aren’t rejecting personalization; they’re rejecting how it’s been done in the past. They still want relevant, tailored experiences, but they expect brands to deliver them through ethical, transparent data practices. Does third-party data still matter in a privacy-first era?  Third-party data isn’t disappearing, if anything, it’s become more important. Brands will always need additional insight to deepen customer understanding; first-party data alone only reflects what’s already known. The industry has entered a mature phase where data quality and compliance are table stakes. The companies leading today built their data infrastructure on rigorous standards, regulatory foresight, and transparent governance. That same foundation powers the next wave of innovation, including the explosive growth of RMNs. RMNs rely on responsibly sourced third-party data to enrich shopper insights, validate audiences, and extend addressability beyond their own walls. Trusted data partners make that expansion possible, connecting retail environments with broader media ecosystems while maintaining privacy and accuracy. High-quality, compliant third-party data remains essential because it: Fills knowledge gaps Good third-party marketing data complements first-party insights with demographic, behavioral, and transactional context, providing the missing puzzle pieces to complete the full customer profile.  Improves accuracy Filling in gaps in customer understanding helps you identify, reach, and engage your customers more effectively. This helps improve the delivery of relevant messages and offers to your customers and prospects across channels.  Builds connections Third-party data helps brands build loyalty with consumers by speaking to their interests, and intent behind purchases.  Fuels prospecting Third-party data can help you find your best prospects. By enriching customer files, you can understand who your best customers are, and how to find more of them. By modeling this data, you can determine who your best customers are and source prospects similar to them.  Advancements in AI and machine learning are reshaping how this data is used across the ecosystem. What was once primarily a buy-side tactic is now expanding into the sell-side, where publishers and platforms are using data to curate, package, and activate audiences more intelligently. As AI enhances modeling accuracy and automation, third-party data will play an even greater role in connecting brands and consumers in more meaningful, privacy-conscious ways.  The bottom line: it’s not about having more data; it’s about having better, verified data you can trust. How can you spot a trustworthy data partner?  The strongest third-party data partners demonstrate accountability through experience, infrastructure, and integrity. Swipe right on the perfect data partner Look for providers that: Operate with clear data principles  Trustworthy partners publish and follow codified data principles that guide every step of data handling. Experian adheres to a set of global data principles designed to ensure ethical practices and consumer protection across all our operations. Treat new privacy regulations as routine  For mature providers, evolving privacy laws are routine, not disruptive. At Experian, privacy and compliance have long been built in. Every partner and audience goes through Experian’s rigorous review process to meet federal, state, and local consumer privacy laws. Decades of experience have shaped processes that emphasize risk mitigation, transparency, and accountability. Stay deeply connected  Leading data companies maintain deep relationships with technology partners and industry and regulatory groups to ensure that ethical data practices are put into practice and their customers are aware of platform-specific regulations. Experian's relationships with demand-side platforms (DSPs), supply-side platforms (SSPs), and even social platforms like Meta, ensures we are aware of any platform-specific initiatives that may impact audience targeting. We’re also active participants in many trade groups to ensure that the industry puts ethical data practices in place to ensure consumers still receive personalized experiences but their data usage and collection is opt-in, transparent and handled with their privacy at the center of the transaction. Have a proven track record in the industry  Longevity matters in a regulated and compliance-driven industry. Providers that have thrived through economic cycles and regulatory shifts are the ones equipped for the future. The ability to source high-quality third-party data is core to their business, not an afterthought. Our data is ranked #1 in accuracy by Truthset, giving our clients confidence that every decision they make is backed by the industry’s most reliable insights. Why the future of third-party data depends on accountability  The third-party data industry has already crossed the threshold from expansion to accountability. The companies leading this era have established their credibility through governance and proof. The future belongs to providers that:  Build with regulatory foresight  Maintain rigorous quality assurance  Prioritize partnership over profit The Wild West days are long gone. The third-party data ecosystem is now defined by stability, transparency, and shared responsibility. Partner with Experian for data you can trust and results you can prove  When accuracy and accountability define success, you need a partner built on both. Work with the company that’s setting the standard for responsible data-driven marketing and helping brands connect with people in meaningful, measurable ways.  Get started 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. Third-party data FAQs What is third-party data? Third-party data is information collected by organizations that don’t have a direct relationship with the consumer. It supplements first-party data by adding demographic, behavioral, and interest-based insights. Why are privacy regulations reshaping data practices? Privacy regulations are reshaping data practices because consumers expect control over how their information is used. That expectation led directly to today’s privacy laws, now active across more than 20 U.S. states and numerous countries worldwide. These regulations reflect a permanent consumer expectation: relevance delivered responsibly. Consumers aren’t rejecting personalization; they’re rejecting how it’s been done in the past. They still want relevant, tailored experiences, but they expect brands to deliver them through ethical, transparent data practices. Laws like the CCPA and state-level privacy acts enforce this expectation, holding brands and data providers accountable for the ethical use of data. Can brands still use third-party data safely? Yes, brands can still use third-party data safely when sourced responsibly. Partnering with established, compliant providers like Experian ensures both legal protection and data accuracy.  How does Experian ensure compliance with evolving privacy regulations? Experian adheres to a set of global data principles designed to ensure ethical practices and consumer protection across all our operations. At Experian, privacy and compliance have long been built in. Every partner and audience goes through Experian’s rigorous review process to meet federal, state, and local consumer privacy laws. Decades of experience have shaped processes that emphasize risk mitigation, transparency, and accountability. Experian's relationships with demand-side platforms (DSPs), supply-side platforms (SSPs), and even social platforms like Meta, ensures we are aware of any platform-specific initiatives that may impact audience targeting. We’re also active participants in many trade groups to ensure that the industry puts ethical data practices in place to ensure consumers still receive personalized experiences but their data usage and collection is opt-in, transparent and handled with their privacy at the center of the transaction. What should marketers look for in a data partner? Marketers should look for transparency, longevity, and evidence of compliance when looking for a data partner. The best partners can clearly explain how their data is sourced, validated, and maintained. Read Experian's guide on how you can swipe right on the perfect data partner here. Latest posts

Originally appeared in MarketingProfs We all understand the importance of data quality. Metrics like third-party validations, match rates, and accuracy scores help us assess data quality on its own terms. Yet too often, organizations struggle to connect high-quality data with real-world business outcomes. How does data accuracy directly impact the ability to reach target audiences and campaign performance? Scale and cost: The tradeoffs of accuracy Marketers are frequently incentivized to prioritize broad reach, even at the expense of precision. This often leads to decisions driven by short-term gains—reaching more people at a lower cost. The temptation is deceptively straightforward, but deep down we know overly simplistic approaches are likely to fall short. Cheaper data solutions, even if they seem to provide greater reach, mask a deeper issue. The data may not be accurate. In fact, the initial savings from cheaper data typically result in higher long-term costs due to inefficiencies and waste that are hard to track. Unless you’re carefully evaluating your campaign results, it can be difficult to see where the inefficiencies are creeping in. The hidden cost of inaccurate data Programmatic platforms make it easy for mistargeted impressions to slip through unnoticed. Common issues include: Stale data, where consumer behaviors and locations have changed but the data hasn’t. Inactive signals, where you think a digital identifier like a device ID is addressable, but the device hasn’t been used in months. Disparate or duplicative data, where you think you’re reaching three people but in fact it’s just one person who you’re frequency bombing. Nobody likes getting the same ad over and over again. It’s like if your co-worker messaged you “hey” five times in a row. Direct mail waste is tangible: towering stacks of returned mail serve as undeniable reminders of inefficiency, not to mention the financial costs of wasted postage. Digital campaigns, by contrast, often obscure their inefficiencies within complex programmatic platforms or impression reports. It’s like watching a gust of wind scatter piles of paper into the ether—it's hard to track and quantify. As a result of these data inaccuracies, brands mistakenly assume they’re optimizing their budgets when, in fact, they’re hemorrhaging money and reaching the wrong people with a message they don’t care about. It’s a marketer’s nightmare scenario. The perceived savings from cheaper, less accurate data turn out to be an illusion. The compounding effect of inaccurate data Consider a situation where an inaccurate insight or signal prompts a brand to adjust its targeting toward an underperforming segment. Each new campaign uses this flawed data to guide its optimizations, amplifying the waste. What starts as a minor inefficiency quickly becomes a significant budget drain, funneling resources into segments that aren’t delivering. If you bake a cake but use salt instead of sugar–each new ingredient only makes the final product more unpalatable. With ad targeting, the feedback loop created by optimization tools exacerbates this issue. Decisions are made based on misleading metrics, perpetuating flawed strategies and causing brands to over-invest in underperforming tactics. Without scrutiny, brands risk building entire strategies on fundamentally flawed insights. The value of investing in the highest quality data With accurate data, brands can zero in on the right audience. This is particularly critical in lookalike modeling. By enriching customer files, brands can understand the nuances of who their best customers are—and how to find more of them. Tailored messaging, based on a consumer’s actual behaviors and interests, deepens engagement. Conversion rates rise as campaigns meet customer needs. Accurate data also provides insights that aren’t immediately obvious. Sometimes, seemingly minor behaviors or unexpected demographic segments can emerge as key drivers of conversions. It’s like finding the one avocado at the grocery store that’s perfectly ripe…you’re well on your way to delicious guacamole. To truly grasp the impact of data accuracy, traditional validation metrics such as third-party assessments (e.g. Truthset) should be paired with other performance indicators that show you how well data reflects actual consumer behavior. With this complete picture in view, the choice is obvious: quality data is worth the premium.  Acting on what the data tells you Collecting accurate data is just the first step—the real challenge is having the ability to act on what it reveals. Many brands enter campaigns with preconceived notions about their target audience, only to find the data tells a different story. Ignoring these insights stifles growth. The value of data-driven marketing lies in trusting the insights and adapting strategies accordingly.  How to test if your current approach is working We understand that changing data providers can feel daunting, but there are low-lift ways to explore whether your current approach is truly delivering. Test the waters by selecting an Experian Audience on a major platform or building a custom audience to see how your campaigns perform. Alternatively, collaborate with Experian’s insights team to gain a deeper understanding of your audience and determine if it aligns with your current strategy. It’s a small step that could lead to a big impact. Get in touch with our team today Latest posts