The letter, which is from Bob Fawson, chief executive and co-founder at Data Quality Co-op, called for the market research industry to move on from problem spotting and focus on a “systemic solution” to the issue of data quality.

While the letter acknowledges work already done to document data quality issues, from industry analyses to academic research, Fawson stressed that while associations and private sector innovators are making important progress, these efforts remain crucial but insufficient on their own.

The Global Data Quality initiative, which is a partnership between several insight industry bodies including the Insights Association and Market Research Society (MRS), is working to highlight the exisiting issues in data quality across market research and encourage companies to take action to address the underlying problems .

MRS also launched its Campaign for Better Data earlier this year to raise the quality of the evidence the sector produces and reinforce public trust in research.

Fawson said in the open letter from th Data Quality Co-op that key drivers of industry growth, such as programmatic sampling, have delivered speed, reach and cost efficiency, but that “alongside these gains, fraud, participant disengagement and eroding trust continue to threaten research outcomes”.

“While good data remains the norm, it’s human nature to focus on the challenges,” Fawson wrote. “We are intrinsically wired to dwell on problems more than progress.

“As a result, big data quality challenges – resulting from industry evolution – feel intractable. They aren’t, but big challenges do require new solutions.”

The letter also warned that synthetic data was not a “panacea” and would need better data quality practices across the industry to become a feasible and beneficial addition to the research toolkit.

“Many in the industry are hoping for a synthetic data panacea that avoids the hard work of improving primary data,” Fawson wrote. “However, fully realising the transformative power of synthetic data also requires improving and validating the quality of primary data used to build synthetic datasets and train AI models.

“This letter is a call to move past fear and toward new solutions, and to create an insights ecosystem poised to drive impact today and in the future.”

The letter asked for the industry to come together to implemented shared solutions to the data quality issue.

“The future of data quality depends on moving from fragmented efforts to shared accountability,” said Fawson. “If you’re not helping build or adopt real solutions, you’re sustaining the problem. It’s time to stop admiring what’s broken and start building what works – together.”