Data Quality Co-op Releases Study That Provides a Blueprint For Transforming Research Through Cross-Industry Collaboration

Research-on-research shows that shared signals and cooperation can lead to stronger, more precise insights

SALT LAKE CITY, June 26, 2025 /PRNewswire/ -- Data Quality Co-op (DQC), the insights industry's first independent clearinghouse for data quality measurement, has released a new white paper, "Elevating Data Quality Through Collaboration: How Shared Signals and Cross-Industry Cooperation Improve Research Outcomes." The report outlines findings from a new research-on-research study that tested whether a shared, collaborative approach to evaluating data quality can improve research outcomes across the market research ecosystem.

Amid rising concerns about AI-generated responses, survey fraud, and inconsistent sourcing, the study proposes a new path forward. It urges the market research industry to combine quality signals from buyers, suppliers, platforms, and fraud tools to build a more complete and accurate picture of data integrity.

"The industry's current approach to data quality is too reactive and siloed," said Bob Fawson, co-founder of the Data Quality Co-op. "This paper outlines a clear path forward that prioritizes cooperation, structure, and transparency. If we want to generate reliable insights from first-party data, we need systems that support trust and repeatability from the start."

Key insights from the study include:

• Sampling is more complex than expected. Even with overlapping suppliers, providers delivered very different quality and targeting outcomes.
• Provider choice matters. Differences in internal practices led to major variations in respondent quality.
• Survey design affects data quality. Irrelevant or poorly routed questions caused disengagement, even among high-quality participants.
• Not all bad data is fraud. Segments like "Bad Open-Enders" and "Survey Newbies" passed basic checks but still lowered data quality.
• Data cleaning impacts results. Inclusion or exclusion of certain respondent types shifted key findings, including package and feature preferences.
• Collaboration improves accuracy. Sharing quality signals across the ecosystem leads to more reliable, repeatable insights.

The study was conducted in March 2025 and included more than 2,000 U.S. parents of children ages 0 to 17. Respondents were sourced from two major sample providers and recruited through a range of digital channels, including panels, get-paid-to sites and fintech apps. Participants completed a 12-minute survey on food pouches that captured brand preferences, product evaluations, open-end responses, and self-reported sourcing information.

"We need to stop thinking of quality as something we clean up after the fact," said Ian Haynes, co-founder of Data Quality Co-op. "Quality starts upstream, and that means improving visibility into how respondents are sourced, how they behave, and how our decisions, like question routing and sample filtering, impact outcomes."

The full white paper is available now here.

About Data Quality Co-op

Data Quality Co-op (DQC) is an independent first-party data quality clearinghouse. We transform how buyers and suppliers of first-party data measure, understand and manage the quality of their data. Our platform offers continuous quality measurement and real-time quality certification by aggregating, analyzing, and benchmarking data quality signals. Together with our clients, we are shaping the future of fast, reliable data-driven insights. Headquartered in Salt Lake City, Utah, our mission is to ensure each business decision, marketing campaign or AI model is driven by data that's high-quality, high-value and perfectly suited for its purpose. For more information, visit www.dataqualityco-op.com.

Media contact: Marie Melsheimer, [email protected], +1-541-815-3951

SOURCE Data Quality Co-Op