Building a data quality tech stack starts with thinking about the whole respondent journey

When researchers talk about data quality, the conversation often turns quickly to tools. Which fraud solution should you use? Which signals matter most? Which platform catches the most bad actors? In our recent webinar with Sago, our discussion moved beyond individual technologies and toward the systems that connect them.

The webinar featured Rob Berger, EVP Global Quantitative at Sago, Kelly Kitchens, VP Customer Success at Sago, and our CEO and co-founder Bob Fawson. Together, we explored how quality programs are evolving as researchers contend with more sophisticated fraud, synthetic responses, coordinated bad actors and a respondent ecosystem that has become much more complex to navigate.

The conversation began with a description of how quickly the landscape has changed: "We have a more sophisticated fraud issue that's going on out there... there are coordinated bad actors that exist out there unfortunately, and becoming and growing is a more synthetic and professional response."

Those challenges create obvious quality concerns, but they also create operational issues. Teams spend time investigating suspicious respondents, replacing sample, reconciling quality decisions and answering questions about data integrity after fieldwork is complete. The discussion focused on how much of that work can be reduced when quality decisions happen earlier in the process.

Understanding the person behind the response 

A theme that surfaced throughout the webinar was the importance of understanding the person behind the survey response. Respondents move across panels, suppliers, marketplaces and platforms. Looking at a single survey only provides a snapshot of that activity.

Bob described how DQC approaches this challenge by combining technical signals, behavioral indicators and participation history to build a more complete picture of respondent trustworthiness. "Like a credit score, past behavior is really highly predictive of future performance."

That history becomes particularly valuable when viewed across a broad network. Someone who has participated thoughtfully and consistently over time leaves a very different footprint than someone attempting to enter surveys under multiple identities or exhibiting suspicious participation patterns.

The discussion also highlighted something that can get lost in conversations about fraud. Researchers spend a lot of time talking about who should be excluded, but quality programs also need to recognize the respondents who contribute meaningful data and help make research possible.

"It's not really just about blocking bad respondents either or identifying bad actors, but we also need to identify the consumers who show up, that thoughtfully engage, and that really make our entire industry possible."

Quality works best when systems work together 

Sago shared how that philosophy connects to its broader quality framework. Kelly Kitchens walked through the company's approach to validating respondents throughout the entire participation process, beginning with identity verification and continuing through survey participation, quality monitoring and incentive fulfillment. "It begins at the beginning and to the end of it, we're really building that journey end to end so you can trust that data quality that we're bringing to market."

Several attendees asked questions about government ID verification and whether additional validation steps create friction for respondents. The conversation offered a useful reminder that quality is rarely built from a single check or technology. Different layers contribute different information, and those layers work best when they reinforce one another.

Kelly described this as a combination of technology, workflow design and human judgment. Quality signals are collected throughout the process, reviewed in context and used to support ongoing improvement.

The webinar also spent considerable time on collaboration and the role shared intelligence can play in strengthening research outcomes.

Because respondents participate across many different environments, no individual organization has a complete view of respondent behavior. One company may see part of a respondent's history while another sees something entirely different. Bringing those observations together creates a richer understanding of trustworthiness and helps quality decisions become more consistent over time. "The more history, the more powerful the scoring.”

One of the more thoughtful moments of the discussion came during the Q&A when attendees asked whether perfect data quality is even realistic.

Bob's response acknowledged something researchers sometimes forget. "I'm not sure perfect data quality is realistic in as much as data subjects or the people that are providing the data are not perfect."

Research will always involve people, and people will never be perfectly consistent. The objective is to build stronger systems that help researchers make better decisions with the information available to them.

Strong quality outcomes are built through layers of verification, respondent intelligence, operational processes and collaboration across the industry. The organizations making the most progress are finding ways to connect those pieces together and learn from them over time.

View the webinar: https://discover.sago.com/data-quality-webinar-registration