Deep Reads: Quality Is More Than a Clean Dataset

Deep Reads: Quality Is More Than a Clean Dataset

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Three new market-research reads from August 12-13 make the same practical point from different angles: quality is not just whether a respondent is real. It is whether the study is designed for its downstream use, the measurement matches the decision, and the insight reaches someone who can act.

Quality is more than data

Ray Poynter's Quality is More than Data argues that fraud prevention is necessary but incomplete. Research now feeds pricing, personalization, models, and automated decisions, so risk can accumulate when data is combined or reused outside the original study context. Poynter's practical test is to make reuse, assumptions, limits, and ownership explicit. 1
The article cites an MRII study reporting that 62% of researchers said they or their team were using AI in 2025; that figure is supporting context from the cited study, not Poynter's own survey. 2

When a good score hides a weak relationship

In Kenneth Peterson's The CX Metric That's Lying to You, QuestionPro's Q1 2026 Experience Benchmarks show banking and credit unions at 72% CSAT and 19 NPS. The article's central distinction is that CSAT describes a specific interaction, NPS describes the broader relationship, and Customer Effort Score surfaces friction that can build before churn. 3
The data is proprietary to QuestionPro, so the episode treats it as a measurement lens rather than a universal causal benchmark. The transferable lesson is to give each metric a job and investigate disagreement instead of averaging it away.

The work after the study

Jill Miller's Three Themes Every Brand-Side Researcher Should Be Tracking is a conference recap, not a representative survey. Its value is the practitioner's synthesis: immersive human-led qualitative work still matters; respondent quality extends beyond bots and duplicates; and strong research can still fail when insights cannot reach a decision-maker in time. 4
Taken together, the three reads offer a useful checklist for the next brief: what can happen to the data after fieldwork, which measure is fit for the decision, and who owns the move from finding to action.

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