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.
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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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Leading global marketing technology company, Tapad, a part of Experian, adopts The Trade Desk’s streamlined cookie ID into its identity resolution solutions to increase global cookie coverage across the internet April 03, 2019 09:00 AM Eastern Daylight Time LOS ANGELES & NEW YORK–(BUSINESS WIRE)–Global marketing technology company, Tapad, a part of Experian, has partnered with The Trade Desk to onboard The Trade Desk’s unified ID solution into its digital identity driven solutions. The Trade Desk’s unified ID solution is free for all parties spanning the digital advertising supply chain, including SSPs, DSPs, DMPs and data providers. With leading players onboarding the unified ID solution, such as Tapad, streamlined cookie syncs make ad viewing experiences better for consumers, increase match rates for all parties, and expand cookie coverage on a global-scale. Adoption of The Trade Desk’s global cookie footprint enables Tapad customers to benefit from greater precision and potentially higher performing digital marketing campaigns. “Digital identity resolution is key to improving digital marketing campaigns. Supporting The Trade Desk’s unified ID solution, in conjunction with our cross-device and identity resolution offerings, helps us and the entire open ecosystem,” said Chris Feo, SVP of Global Data Licensing and Strategic Partnerships at Tapad. “It simplifies cookie syncing, and increases match rates among our partners; enabling our customers to improve marketing campaign performance.” “Identity is critical in the ecosystem and as a long-standing leader in the space, Tapad has shown a strong commitment to building digital identity resolution solutions that help marketers launch more effective campaigns on a global scale,” said Ed Chater, VP of Data Partnerships, The Trade Desk. “We are thrilled for their participation and look forward to solving the digital identity narrative across the industry through this partnership. The continued widespread adoption of the unified ID solution validates our collective mission to improve the effectiveness of digital advertising.” About Tapad Tapad, Inc. is a global marketing technology company and leader in digital identity resolution solutions. The Tapad Graph™, and related solutions, provide a privacy-safe approach to connecting device identifiers to brand and marketer data, thereby allowing for enhanced measurement, attribution, reach and ROI of marketing campaigns. The Tapad Graph™ enables marketers around the world to maximize campaign effectiveness and drive business results. Tapad is recognized across the industry for its innovation, growth and workplace culture, and has earned numerous awards, including the TMCnet Tech Culture Award. Based in New York, Tapad also has offices in Chicago, London, Oslo, Singapore and Tokyo, and is a wholly owned subsidiary of Telenor. About The Trade Desk, Inc. The Trade Desk™ is a technology company that empowers buyers of advertising. Through its self-service, cloud-based platform, ad buyers can create, manage, and optimize more expressive data-driven digital advertising campaigns across ad formats, including display, video, audio, native and, social, on a multitude of devices, such as computers, mobile devices, and connected TV. Integrations with major data, inventory, and publisher partners ensure maximum reach and decisioning capabilities, and enterprise APIs enable custom development on top of the platform. Headquartered in Ventura, CA, The Trade Desk has offices across North America, Europe, and Asia Pacific. To learn more, visit thetradedesk.com or follow us on Facebook, Twitter, and LinkedIn. Contact us today
by AdExchanger // Friday, March 15th, 2019 – 12:06 am “Data-Driven Thinking” is written by members of the media community and contains fresh ideas on the digital revolution in media. Today's column is written by Preethy Vaidyanathan, chief product officer at Tapad, a part of Experian For years marketers asked, “How do we get more data?” Now that they've mastered data mining, marketers want to know what’s next. The time has come for organizations to make their abundance of digital data actionable, increase ROI and reach consumers with consistent, personalized experiences across all touchpoints. A seamless consumer experience can only be achieved by consolidating digital data. Organizations, however, are finding that consolidating data silos is more time-consuming and complicated than initially expected. The challenges One of the most pervasive obstacles companies face in when consolidating data is adopting inefficient and costly tactics that quickly become outdated. For instance, over the last couple of years, many companies turned to enterprise data warehouses to consolidate data silos, but some were too expensive or poorly suited for raw, unstructured and semi-structured data. This led companies to adopt data management initiatives, which bogged down many enterprises. Perception among senior level executives is another challenge. Many still question the need for digital transformation – achieving greater efficiencies through updating business and organizational processes with new technologies. Gartner found that more than half (54%) of senior executives say their digital business objective is transformational, while 46% say their objective is optimization. Digital transformation and data consolidation require time and effort. So, many large organizations work to overcome data silos as part of a multiyear digital transformation versus an immediate action item, delaying the benefits the company sees from taking on this project. All of these challenges make delaying progress in data consolidation easy, but companies should remember the impetus for doing so: creating a seamless customer experience that, in turn, drives business results. Brands with higher quality customer experience grow revenue faster than direct competitors with lower quality customer experience. The approach Many brands go into the digital transformation process assuming they have massive amounts of customer data, and that much of it is valuable or will be in the future. They might spend months aggregating that data in data stores or data lakes – at great expense. The trouble is that their data was scattered across multiple databases, which means it’s highly fragmented. As a result of this fragmentation, marketers can’t activate their data in ways that enhance the customer experience. To do so, companies must ensure their digital data is highly flexible so it can provide a holistic view of the consumer journey across every digital, in-store, in-venue and offline channel. I’d recommend that organizations taking on data centralization initiatives prioritize use cases that offer the company the greatest benefit. This is where organizations should establish a “crawl, walk, run” approach to data centralization to ensure key executives buy into the process. Starting with a subset of use cases, such as customer retention or upsell, or with a campaign, which is an even smaller starting point, allows executives to see the benefits of data consolidation projects relatively quickly. Once they validate these initial benefits, they can expand the range of use cases or campaigns, as well as the marketing ROI for their business. While data centralization is a long-term project that may take several years to complete, it doesn’t mean a business can’t get started now and see measurable results quickly. Break down data consolidation into stages so the organization can experience wins along the way. At the end of the day, data consolidation will help organizations deliver more effective marketing campaigns that drive business growth. Contact us today