Unlocking Data Quality with Bob Fawson on the HappyMR Podcast

In a recent episode of the Happy Market Research Podcast, host Dan Fleetwood of QuestionPro sat down with our CEO & Co-Founder Bob Fawson to explore the evolving challenges—and emerging solutions—in first-party data quality. Bob reflects on his 20-year journey in the market research industry and shares actionable insight on how transparency, AI and storytelling are reshaping the data quality conversation.

Why the Data Quality Co-op Exists

In his discussion with Dan, Bob shared that Data Quality Co-op was launched to address two persistent pain points in the market research industry: buyers struggle to evaluate data quality before purchase, and sellers lack visibility into how their sample performs in the wild. He shares, “Ultimately, buyers of insights are very willing to pay for quality—but oftentimes they don't know how to evaluate it or understand it.”

Bob describes DQC as a clearinghouse for data quality signals, similar to a credit score. By aggregating signals from across the ecosystem, DQC helps both buyers and suppliers make informed, proactive decisions that elevate trust and improve outcomes. 

A 40% Problem: The Industry’s Data Waste Crisis

Bob and Dan dove into how the scale of the challenge in today’s market research industry is staggering. Bob cited a widely circulated stat: “About 40% of our data is unusable. I can’t imagine what would happen to General Motors if 40% of the parts they ordered were unusable.”

He emphasized that this level of waste is not sustainable. And worse, the ecosystem often lacks the tools to assess risk or value before data is delivered. That’s where DQC steps in—creating a baseline of shared understanding around sample quality and performance.

The AI Shift: A Double-Edged Sword

Artificial intelligence is changing everything, and Bob shares on the HappyMR Podcast that it's already transforming the way we evaluate data. Traditional methods—like fraud detection or manual open-end analysis—don’t stand up to AI-generated responses that mimic human behavior. “AI produces data that looks very human. We need to shift toward understanding the inputs to AI models.”

Bob sees this shift as both a risk and a powerful opportunity. The research industry has a direct line to consumers and can play a critical role in training AI systems—if it can deliver quality, trusted data inputs. “Our industry is so well-positioned. I can’t think of another industry with better access to relevant, high-quality data to feed the AI models of the future.”

The Illusion of Diversity in Sampling

Bob shared a revealing example from a recent study: while he identified 118 distinct sample sources, they ultimately traced back to just 18 unique suppliers. “80% of the sources looked very similar in value proposition. There’s an illusion of diversity, but not much actual differentiation.” This lack of true diversity not only hampers innovation—it also opens doors for fraudulent actors to slip through. Bob believes that transparency around source provenance is critical to fixing this, sharing that “We’ve created hundreds of sources that look the same... but without the benefit of real market liquidity.”

Storytelling, Strategy & the Researcher’s Role

While much of the conversation centered on systems and tech, Bob also circled back to something more human: storytelling. In a world where AI can summarize data, nuance, insight, and narrative are where researchers add the most value. He tells Dan that “Good business strategy requires nuance. Our job isn’t just to get the headline right—it’s to bring nuance to the conversation.”

To hear the whole conversation between Bob and Dan on the HappyMR podcast, visit https://happymr.com/ep-2-unlocking-data-quality-with-bob-fawson/.