3 Strategies to Improve Data Quality in Research
When I think about the future of market research, one thing is clear – data quality can’t be treated as a checkbox. It’s the backbone of every meaningful insight our clients deliver. With increasing pressure from automation, tight timelines, and growing fraud threats, researchers must adopt multi-layered approaches to ensure the integrity of their work.
In this blog, I’ll dig into three ways organizations can tackle the creeping threat of fraud.
1. Blend Passive and Active Quality Measures
The most resilient data quality frameworks combine what’s happening behind the scenes (passive) with proactive verification (active). Passive systems often include tools like digital fingerprinting, device detection, and IP validation. These checks quietly flag suspicious behavior without disrupting the respondent experience.
But active measures are where organizations can truly elevate their standards. This might include integrating external verification databases, tailoring question logic to test for internal consistency, or deploying smart open-ended responses that flag low-effort answers.
Crucially, active quality measures should be designed with respect for the respondent. The goal is not to catch people out—but to create seamless ways to confirm legitimacy while preserving the integrity of the experience.
2. Reinforce Technology with Human Oversight
Automated fraud detection has come a long way, but no algorithm is perfect. Bringing skilled humans into the process can add a layer of discernment machines can’t replicate.
Data quality teams who review responses—looking for nuance, judgement calls, or subtle inconsistencies—can spot issues that might otherwise go undetected. This hybrid model not only improves accuracy but builds client trust. Stakeholders can be confident their data has been reviewed with rigor and care.
3. Use Historical Data to Inform Future Quality
When handled responsibly, performance data from past research can be a goldmine for quality improvements. Understanding how question formats affect completion rates, identifying where drop-offs occur, and learning which audiences overclaim or underperform can all inform smarter survey design.
This feedback loop, where the output of one project improves the inputs of the next, is essential to building sustainable, scalable quality standards. It allows researchers to proactively address common issues before they impact the results.
Building a More Resilient Ecosystem
For me, it all comes down to a balance – protecting integrity, creating better experiences for respondents, and being honest with clients.
We’re building a research ecosystem where technology and humans work together, where the process is transparent, and where the people behind the data are always respected.
To me, that’s the future of data quality.
To learn more about the ways we’re combatting fraud and bad data, check out our video series here.
