Data quality issues of all types are highly pervasive in market research, yet most researchers never see a clear picture of what's actually happening inside their studies. Without transparency into where problems occur and how they're resolved, it's nearly impossible to build the judgment needed to design better research over time.
In this session, Jonathan Goodbread, head of data quality strategy at aytm, shares a four-layer framework for thinking about data quality end-to-end: from who enters your survey, to how responses are analyzed, to how survey design choices affect the data you collect. Drawing on aytm's approach and commitment to data quality transparency, Jonathan will explore what the data actually reveals about real-world research quality and what that means for how researchers should be thinking about their own programs.
Attendees will leave with:
• A practical four-layer framework for evaluating data quality across any research program.
• A clearer understanding of which quality threats are most prevalent and which are easier to miss than you'd expect.
• Concrete actions for improving panel integrity, survey design and response analysis.
• A leading perspective on where data quality innovation is heading and what it demands of researchers today.
At Quirk's Virtual, Goodbread will share a six-layer framework for evaluating data quality end-to-end and explore what the data actually reveals about the challenges and opportunities shaping research today.
