Why Data Quality Detection Matters More Than Ever in Market Research

Market research relies on several interconnected elements to ensure we’re delivering high-value, actionable insights to clients. Survey method, design, and countless behind-the-scenes decisions all make up one piece of the larger equation. But perhaps the most critical component – one that underpins everything else – is data quality. We can collect all the responses in the world, but if our data is filled with bots, survey farms, or inattentive respondents, we can’t confidently stand behind the insights we deliver.

Recently, I attended a webinar hosted by Dynata, one of our longtime panel partners and a leader in first-party data collection. They shared the latest innovations in fraud detection within their platform, and it was both eye-opening and energizing. It reminded me just how essential data quality is in today’s tech-driven research landscape – and how much progress is being made to protect it.

Dynata’s platform can now detect problematic survey responses in real time. Through a combination of checks – like IP traffic history, erratic pacing, and other advanced behavioral signals – they’re able to flag questionable activity as it happens. If a response looks suspicious, it can be paused and investigated immediately. This is a major improvement over the traditional approach of only cleaning data after the survey is complete.

And they’re not the only ones making strides. With our own platform partner, QuestionPro, we have access to multiple built-in flags that help us identify potential issues: duplicate IPs, response speed, straight-lining on grid questions, respondent location, gibberish open-ends, and more. These individual indicators don’t necessarily mean the response is fraudulent, but taken together, they tell a more complete story. That’s why a layered, contextual approach is so important.

Not all bad data comes from bots or fraudsters—sometimes it’s just a human respondent who gets distracted, rushes through, or doesn’t fully engage with the questions. But regardless of the intent, the result is the same: data we can’t trust. That’s why our role as researchers includes being vigilant. We have to recognize the red flags, dig into the context, and decide what stays and what gets cut. The reality is, threats to data quality are evolving—bots, survey farms, and AI-generated responses are getting more sophisticated. But so are our tools and techniques. With the right mix of technology and human oversight, we can stay ahead of the curve and protect the integrity of our work.

TLDR:

You can’t do great research with bad data. Whether it’s bots, distracted respondents, or full-blown survey farms, poor data can sneak in—and it matters. Thankfully, tools for detecting fraud are getting smarter, and so are we. With a layered approach and the right tech on our side, we can stay a step ahead and keep delivering insights that actually mean something.