Can you trust your data? Learnings from our conversation with Dig Insights

Data quality has always mattered in market research, but many of the challenges facing the industry today are different from the ones researchers dealt with even a few years ago.

In our recent Insight Platforms webinar with Dig Insights, we explored why traditional approaches to quality management are under pressure and what it means to think about data quality as a connected system rather than a series of individual checks.

The conversation covered everything from professional survey takers and AI-assisted responses to the growing complexity of sample supply chains. While each of these challenges creates its own risks, the common thread was visibility. Most quality controls are designed to evaluate what happens within a single survey, platform or supplier relationship. The reality is that respondents move across a much larger ecosystem.

As Kevin Hare, EVP at Dig Insights, explained, today's challenge is not simply identifying low-quality responses. Researchers are increasingly dealing with respondents who understand how the ecosystem works and how to navigate it. During the discussion, Bob Fawson, CEO of DQC, made an observation that resonated with many attendees:

"The respondents know more about how our platforms and sample work, and how to navigate them than most of us as researchers know about how it works."

Kevin shared how Dig approaches quality throughout the research process. That starts with survey design. Dig has invested heavily in mobile-first survey experiences that are easier for respondents to complete and more likely to keep participants engaged. The company also works with vetted sample partners and applies a range of quality controls, including machine learning models designed to identify unusual response patterns within individual datasets.

Dig also discussed the value of validating findings through multiple sources. In addition to survey data, the company incorporates social conversation data to provide additional context and a broader understanding of consumer sentiment. Looking across multiple sources helps reduce dependence on any single dataset and can provide a more complete picture of what consumers think and feel.

At DQC, we shared why we believe data quality requires visibility beyond individual studies. Today's respondent ecosystem is highly interconnected, with participants often moving across platforms, panels and suppliers. Quality signals that exist within a single survey can be valuable, but they only tell part of the story.

Our approach focuses on independent verification and respondent-level quality intelligence. Using platform-agnostic integrations, persistent identity resolution and our Data Trust Score™, we evaluate trustworthiness based on participation history, behavioral signals, device intelligence and quality outcomes observed across the broader ecosystem.

During the webinar, Bob described the challenge in simple terms: "You need to know when they show up at the front door what they look like." That visibility happens when quality signals can be viewed across projects, platforms and time rather than within a single survey environment.

One of the themes that resonated throughout the discussion was the importance of independent verification. Research suppliers, platforms and sample providers all play important roles in maintaining quality, but each only has visibility into a portion of the respondent journey. Independent quality infrastructure creates a shared view that can help organizations make more informed decisions about respondent management, supplier performance and quality assurance.

The discussion also highlighted practical benefits that extend beyond fraud detection. Shared quality signals can support supplier optimization, reduce reconciliation work, improve alignment between partners and help researchers spend less time investigating quality issues and more time delivering insights.

Perhaps the most important takeaway from the session was that no single tool or process will solve the industry's quality challenges on its own. Data quality is not a problem that can be addressed with one checkpoint or one technology. It requires continuous learning, collaboration and a broader view of how respondents move through the research ecosystem.

"This is not about finding a magic bullet. This is about creating a feedback loop and an infrastructure that helps us with continuous learning and just getting better."

That mindset may be the most important shift of all. Strong data quality is becoming a shared responsibility across the research ecosystem, supported by better signals, better visibility and better collaboration.

Watch the full webinar to learn more about Dig Insights' quality framework, DQC's independent verification model and how ecosystem-level quality signals can help strengthen research outcomes.