Barry Jennings

North America Ambassador

Esomar

Barry Jennings is a seasoned client-side insights leader with 30+ years of experience turning market research into strategic advantage. He got his start as a phone survey researcher in college — and never looked back. For the past decade, Barry led B2B research teams at Microsoft’s Research + Insights group, most recently driving insights for Azure Cloud, AI, and Commercial Business Planning. Before that, he spent 18 years at Dell spanning virtually every product category across consumer and B2B markets. He then took his expertise into mobile at BlackBerry, where he headed market research and competitive intelligence. Earlier in his career, Barry built a strong foundation in international technology research at Kantar/Millward Brown/IntelliQuest — giving him a rare perspective that bridges both supplier and client sides of the industry. Today, Barry serves as Esomar’s North America Ambassador, on the Board of Directors for the Insights Association, and on the Executive Advisory Board for the University of Wisconsin’s Marketing Leadership Institute, while consulting for both client-side and vendor-side research organizations.

Sessions

Apr 29

11:05 AM - 11:25 AM EDT

Qualtrics Market Research Stage

Responsible AI

Synthetic Data

Synthetic Data at a Tipping Point: Hype, Risk and the New Rules for Responsible Augmentation

Synthetic data has moved from innovation labs into client decks, procurement conversations and live decision-making, requiring ever-stronger standards for guiding its use. In a landscape shaped by AI acceleration, budget pressure and demand for speed, buyers are being asked to trust augmented synthetic outputs without a shared language for evaluating them. This session offers a practical, globally relevant framework to help research leaders separate credible augmentation from statistical theatre and make confident, responsible decisions.
Key Takeaways:
1. Clarify what buyers are actually being offered by distinguishing augmented data, fully synthetic data and persona-based systems, and understanding where each can and cannot add value.
2. Move beyond surface validation by assessing fidelity, correlations, dependencies and real-world performance against intended business use.
3. Evaluate privacy, bias, compliance and vendor transparency considerations to ensure synthetic data strengthens, rather than weakens, trust in insights.

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