The Essential Data Quality Checklist for Researchers

DATA INTEGRITYMARKET RESEARCH NEWSBEST PRACTICESDATA QUALITYAI
Written By Jon Pirc

Bad data isn’t new, but AI bots are making it a bigger problem than ever. They can slip through surveys with perfect grammar and logic, and at scale, they can wreck the reliability of your results.

That’s why the Lab42 team pulled together a Data Quality Checklist, a simple, practical tool that helps you:

  • Choose sample providers with better vetting and accountability

  • Build surveys that flag bots and low-quality responses before they slip through

  • Use smarter open-ends to separate humans from AI

  • Maintain daily hygiene to keep your data clean over time

No one tactic solves everything. The key is layering smart practices across sourcing, survey programming, and manual review. This checklist gives you a quick way to put those practices into action and protect the integrity of your research.

Use it as a guide the next time you program a survey, and keep it handy for your team. Clean data means better insights, and that’s what every research project depends on.

Jon Pirc

Jon has spent his professional career as an entrepreneur and is constantly looking to disrupt traditional industries by using new technologies. After working at Sandbox Industries as a ‘Founder in Residence’, Jon founded Lab42 in 2010 as a way to make research more accessible to smaller companies. Jon has a Bachelor’s of Science in Psychology from Northern Illinois University.

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