Data Quality Toolkit

To put it simply, if your research data is collected via online panels, the significant increase in fraudster infiltration means that you probably can’t trust it!

Based on our experience, the vast majority of quality assurances that panel providers give are just not enough to stop fraudsters from completing your surveys.

At MM-Eye, we are committed to sharing our knowledge and experience on this topic. That's why we've put together a quick test designed to help insight teams pressure test the quality of your data and spot where confidence may be weaker than it looks.

Can you prove your respondents are real, relevant and engaged?

Because sample size only matters if the people behind the numbers are real, relevant and engaged. Poor-quality respondents can quietly distort findings, making weak data look statistically robust and giving false confidence in the decisions that follow.

When did you last audit your data quality, not just accept cleaned data?

Because quality checks should not stop once fieldwork is complete. A proper data audit looks beneath the surface of the dataset to identify patterns that standard cleaning can miss, from low-effort responses and inconsistent answers to hidden duplication, suspicious behaviours and quality issues linked to specific sources.

Are you making conscious trade-offs, or is quality being compromised by default?

Every research project involves pressure around speed, cost and sample. The risk is not that trade-offs exist but that quality is compromised without being acknowledged. This leaves decisions being made from data that looks complete, but is not reliable enough.

Pressure test your data

Only 22% of researchers feel 'confident' in the quality of their data from standard online surveys

Before making decisions from your data, ask yourself 3 simple questions.

Quite simply, if you can’t answer these 3 questions, you can’t confidently trust your data. We can help.

That’s why we created our Data Quality Framework