Rethinking Data Quality: Addressing the Industry’s Trust Deficit

For years, market research has wrestled with persistent and rapidly evolving data quality challenges.  The challenge isn’t insurmountable - far from it. We need a fresh approach; a structural shift that rewires how we think about transparency, trust, and incentives across the entire ecosystem.

The core challenge isn’t unique to our industry. Data quality challenges are deeply rooted in what economists call a “lemon market.” Much like the used car market, where buyers can’t distinguish between high- and low-quality products, the survey data market suffers from an information asymmetry problem. Buyers often assume average quality, which pressures sellers to focus on volume rather than quality. For sellers, this dynamic incentivizes cutting corners and focusing on volume over quality. Only a few firms need to follow this incentive for a vicious cycle to erode trust and perpetuate the commoditization of data.

Structural Change: Not Just a Buzzword

Rebuilding trust in our industry starts with improving transparency. Buyers need reliable, independent signals to differentiate between high-quality and low-quality data, and sellers need to be rewarded for investing in quality. Other industries have successfully navigated the lemon market. Take the example of credit rating agencies, which provide independent benchmarks to help financial markets function efficiently. Similarly, commodities futures markets have operated continuously from the 1700s based on independent quality signals being widely available.

A similar clearinghouse model could work for market research, providing unbiased evaluations of data quality. This would enable buyers to align their expectations with their needs—whether they require top-tier data for a strategic initiative or lower-cost data for confirmatory research—and help sellers showcase their quality standards.

Don’t Overlook the Participants

Another critical piece of the puzzle is understanding the people behind the data. Research participants aren’t a passive resource to be mined; they’re active contributors to be engaged, who respond to the offers and incentives presented to them. Treating these interactions as labor-like contracts, where participants’ time and input are fairly compensated, is essential for attracting diverse, high-quality respondents.

Too often, we overlook the participant experience when discussing data quality. If the same brands commissioning research truly understood how participants engage with surveys, would they approve of the process? This disconnect highlights the need for greater alignment between the end clients, suppliers, and participants.

Why AI Makes This More Urgent

The rise of powerful generative AI has rendered humans decreasingly effective at discerning human-generated content from AI-generated content. In order to trust the data that drives important decisions, we need to turn our attention from yesterday’s quality management approach - evaluating the output - to evaluating the human input that informs quant and qual projects and trains AI models. This will require a clearinghouse model to aggregate and report on participant behavior across projects, buyers, suppliers and time.

It’s not all bad news, though. As the demand for high-quality, first-party data continues to grow, the stakes for addressing data quality in a new way have never been higher. Generative AI models rely on robust, reliable data inputs. If the insights industry wants to remain relevant in this new landscape, we must position ourselves as the gold standard for first-party data collection.

This is a pivotal moment. The solutions we implement today will determine whether we capitalize on the growing demand for high-quality data or allow other industries to fill the gap. Transparency, collaboration, and innovation will be key to positioning market research as the trusted source for actionable insights.

Optimism for the Future

Despite the challenges, I’m optimistic about our industry’s ability to evolve. There’s precedent for this kind of transformation in other sectors, from digital advertising to financial services. If they can tackle similar issues, so can we.

The path forward requires bold thinking and a willingness to break free from the status quo. By embracing structural changes, fostering open communication, and focusing on participant experience, we can restore trust, elevate data quality, and unlock new opportunities for growth.

The future of market research hinges on our ability to deliver on one simple yet critical question: How do we know the data we’re selling is true? If we can answer that question with confidence, we’ll not only rebuild trust but also secure the future of the insights industry.