Elevate Your Data Quality in 2026
Bots are passing your screeners. Professional respondents are gaming your quotas. And you’re making million-dollar decisions on data you can’t fully trust.
Nothing feels worse than presenting research to leadership, only to have someone ask a question that makes you realize the data doesn’t add up. By then, the budget’s spent, the timeline’s gone, and your credibility is on the line.
The way to avoid this potential delivery disaster is to work with a trusted partner like Eleven Market Research. We spend all of our time worrying about this on your behalf. We are obsessed with superior data quality. We have years of experience and reliable processes in place to help deliver the most trustworthy data available. If you’re interested in learning more and receiving our complementary data quality checklist, please enter your info below.
Get the Data Quality Checklist
Get the Data Quality Checklist
The Problem
- Bad data doesn’t always announce itself.
- It blends in. It looks clean. It passes surface-level checks. And then it quietly undermines decisions.
- In today’s research environment, low-quality responses, professional respondents, bots, and AI-driven fraud are no longer edge cases — they’re persistent threats. Too often, teams don’t discover the problem until results don’t add up or stakeholders start questioning the findings.
- If your research informs real decisions, data quality isn’t a “nice to have.” It’s mission-critical.
The Eleven Market Research Difference
At Eleven Market Research, data quality is not a single tool or checkbox. It’s a system.
Our approach focuses on stopping bad data before it enters your dataset — not explaining it away afterward.
Our quality-first framework includes:
- How to design screeners that qualified respondents pass naturally — but fraudsters have to guess”
- The 3 fieldwork practices that catch problems at 50 completes instead of 500
- When to kill a sample source (and how to do it without derailing your timeline)
- Advanced fraud detection and respondent validation
- Proactive field monitoring by experienced project managers
The result: data you can defend, trust, and act on.
What You’ll Get: Data Quality Checklist
We’ve distilled our quality framework into a practical, no-fluff checklist you can apply
immediately.
The Data Quality Checklist covers:
- Screener design mistakes that invite fraud
- Red flags that signal low-quality respondents
- Fieldwork practices that protect data integrity
- Sample sourcing strategies that reduce risk
- When automation helps — and when human oversight matters more
This is the same thinking we apply internally on every project.
Ready to pressure-test your data quality approach?
Download the checklist and schedule a complimentary strategy session with our team to review
your current or upcoming research.