In this special in-person episode of Signal and Noise, the hosts sit down with Beth Finn, CEO, and Jason Ebbing, COO of The Directions Group, for a wide-ranging conversation on the future of insights leadership.

Beth and Jason share their career paths, how The Directions Group approaches integrated intelligence, and what it means to move beyond siloed research toward clearer signals that drive real business action. The discussion also explores data quality, speed to insight, pricing research based on value, and how artificial intelligence should support rather than replace human thinking.

This episode offers a candid look at modern leadership in the insights industry and how organizations can stay relevant as decision-making accelerates and expectations rise.

Key Takeaways:

• Clients are overwhelmed with data but still struggle with clarity and decision-making

• Integrated intelligence works best when multiple data sources are planned together from the start

• Research teams gain influence when insights are tied directly to business actions

• Data quality must be usable, credible, and actionable to create real value

• AI should amplify human judgment, not replace it, especially in strategy and leadership

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In this episode of Signal and Noise, Brian Lamar sits down with Frank Kelly, one of the most experienced voices in panel management, sampling, and respondent engagement, to unpack what is actually broken in online research and how the industry can fix it.

Frank reflects on nearly four decades working across every major panel model, from postal and telephone panels to online access panels at Nielsen, Kantar, Ipsos, and now Virtual Incentives. He explains why today’s fraud and data quality challenges are not new problems, but the result of incentives, engagement, and trust being systematically undervalued for years.

A central theme of the conversation is compensation. Frank makes the case that low incentives drive fraud, disengagement, and professional respondents, while fair and meaningful incentives expand the pool of real people willing to participate. He challenges the assumption that higher incentives automatically increase fraud and explains why the opposite is often true.

The discussion also explores how conversational AI, video, and smarter profiling can radically improve panel quality if paired with the right incentive strategy. Frank outlines a future where premium panels support deeper qualitative work, smaller samples, and AI-powered synthesis, all while maintaining higher standards of validation and trust.

Key Takeaways:

• Low incentives shrink the respondent pool and invite fraud

• Fair compensation expands access to real, engaged participants

• Incentive strategy is as important as fraud detection technology

• Conversational AI and video can improve quality when paired with better pay

• Premium panels will be essential as big qualitative research grows

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In this episode of Signal and Noise, Brian Lamar and Andrew DeCilles are joined by leaders from CASE4Quality for a candid and deeply informed conversation on the state of data quality in market research.

The discussion features Mary Beth Weber, founder of CASE4Quality, alongside Tia Maurer - Data Quality Guru, Efrain Ribeiro - Godfather of Sampling, and Karine Pepin - Data Fairy. Together, they unpack how the industry arrived at its current data integrity challenges and why progress has been slower than many expected.

The episode explores the realities of online sampling, fraud, and professional respondents, including how the promise of unlimited and inexpensive sample has distorted incentives across the ecosystem. The guests explain how aggregation, lack of transparency, and pressure for speed and cost reduction have quietly undermined confidence in research outputs.

The conversation also addresses the growing tension between AI-driven research and poor-quality input data. The panel warns that synthetic data and advanced analytics cannot solve quality problems if the underlying data is flawed. Throughout the episode, the group emphasizes the need for transparency, accountability, and brand-led standards to restore trust and long-term viability to the research industry.

Key Takeaways:

• The industry operates under the false assumption that unlimited high quality sample exists

• Speed and low cost have consistently been prioritized over data integrity

• Fraud and professional respondents remain widespread and often undetected

• Aggregated and opaque sampling practices make validation nearly impossible

• AI and synthetic data amplify risk when built on compromised data foundations

• AI can improve efficiency in research, but human judgment is still essential for understanding emotion, taste, humor, and nuance.

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