GDQ Global Data Quality logo in blue and orange

Closing the quality gap with the GDQ Feedback Loop

Data Points 17th August 2026

By Debrah Harding, Managing Director, Market Research Society (MRS)

A common concern expressed at our conferences is that attempts to use smart technology to combat fraud and bad actors in survey participation can only go so far. But inaction on this issue will soon turn a problem into an existential crisis. So when we learned of the General Data Quality initiative (or GDQ), a cross-industry framework and set of tools that has been developed under the leadership of MRS and embracing eleven other professional insights associations around the world, we got excited.

Battling declining data quality was always going to require a coordinated effort between sample providers, researchers and tech platforms. GDQ, and its integral Feedback Loop have been carefully constructed to facilitate exactly this kind of coordinated action. Even better, it considers the technical implications of this with intelligence and practicality, much to our relief.

For this issue of Data Points, we approached Debrah Harding, Managing Director of MRS, to provide a simple explainer for the ASC community on the thinking behind GDQ, and how we can get engaged and start to benefit from it from an operational and technical perspective. The GDQ Feedback Loop, as you will read here, lands very squarely in ASC’s domain, and is something we all need to know about.

Tim Macer, ASC Co-chair

Launching the new GDQ Feedback Loop

Data quality is one of the defining challenges for research and insight. As advances in technology, changing participant behaviours and the increasing sophistication of ‘bad actors’ have made the issue more complex to navigate, it’s clear that no single organisation, tool or market can tackle this alone.

This was the principle behind the Global Data Quality (GDQ) initiative: a shared, global effort to bring organisations and professionals together to protect the integrity of research and maintain confidence in the quality of our sector’s data and insights.

Launched in 2023 by the Market Research Society (MRS), the Insights Association and ESOMAR with others joining to support, the GDQ initiative works to provide data quality guidance and create common standards, language and tools that enhance the value of research.

We know that a challenge as broad reaching and multi-faceted as data integrity can’t be solved by silver bullets or isolated fixes. Instead, GDQ centres on creating the foundations that allow incremental, meaningful progress including through shared definitions, benchmark metrics and practical tools and guidance which every organisation working with data can and should adopt.

From commitment to capability

The reception to the GDQ across the sector is a clear and positive demonstration of the collective drive to improve. The Data Quality Pledge, launched through the initiative a year ago, has already been signed by over 280 organisations who have committed to the highest standards.

The pledge sets out three core principles for the sector: transparency, rigour and education. These principles are important because they recognise that improving quality is not just about detecting fraud, but about understanding why problems occur, how they are identified and classified, and what changes can be made to address them.

That need for a shared, operational approach to classifying sample quality issues led directly to the development of the GDQ/MRS/SampleCon Feedback Loop, one of the new, practical tools to emerge from the initiative.

What is the GDQ Feedback Loop?

The Feedback Loop is a structured framework that enables research buyers to provide consistent, transparent feedback on online sample quality issues where third‑party sample has been used. Developed by MRS in partnership with SampleCon, it provides a common code frame of 18 reasons for data removal based on quality, which can be applied during fieldwork or post‑fielding data checks.  These cover everything from fraud detection to participant fatigue or exaggeration.

This isn’t about policing or ranking research. The goal is to create a shared language that allows all parties to move beyond anecdote and assumption, towards evidence‑based conversations about the reasons for quality concerns.

The code frame quality removal reasons link back to the wider GDQ glossary, ensuring a shared vocabulary, reducing misunderstandings and ensuring consistency of interpretation across the supply chain and across markets.

The Feedback Loop in action

To understand why this matters, consider a typical online quantitative study using third‑party sample.

During data cleaning, a research supplier may remove a proportion of completes due to quality concerns. Historically, that feedback might have been summarised vaguely as “fraud”, “poor quality” or “speeding”, offering little insight to the sample provider and no opportunity to identify patterns or systemic issues – or, crucially, to improve.

Using the GDQ Feedback Loop, those removals are instead coded against the shared and specific framework, for example, distinguishing between bot detection, geo‑location mismatches, ghost completes or over-claiming failures.

That coded feedback will then be shared back with the sample provider in a consistent format. Over time, repeated use of the same code frame allows both parties to see where issues cluster, where processes are working well, and where targeted improvements are needed. Importantly, it also helps challenge assumptions. A pilot project undertaken before the launch showed fewer projects than expected experiencing extreme quality failure, underlining the value of evidence over perception.

This is the benefit of the Feedback Loop approach, it is a mechanism for learning, accountability and short and long-term improvements.

How the Feedback Loop links to other GDQ resources

The launch of the Feedback Loop isn’t the end of the process. It sits alongside other GDQ resources such as the GDQ benchmarks, the GDQ/GRBN buyer sentiment survey and the GDQ/MRS internal approaches guidance in helping practitioners to implement process changes to improve data quality.

Taken together, these resources help organisations of all sizes across the supply chain take tangible, achievable action, while remaining aligned with existing legal and ethical frameworks. Data quality should not be a competitive differentiator to be guarded – it is a shared responsibility that underpins the credibility of the entire research sector.

Data quality applies across the supply-chain

The success of the GDQ initiative depends on participation. Whether a buyer, research supplier, platform, marketplace or sample provider, adopting shared frameworks like the GDQ Feedback Loop is a practical way to contribute to a more transparent, trustworthy data ecosystem.

Improving data quality is complex, and there are no quick fixes. However, by committing to the GDQ, the research sector can close the quality gap together.

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