Data quality is not a filter, it's a discipline - market research - The Curiosity Current: A Market Research Podcast

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Episode notes

Every research vendor claims their data is clean, but few can actually measure it. Stephanie Vance and Molly Strawn-Carreño have a host-only conversation about the real business issues behind data quality. Bad data leads to more than just a cleaning bill; it creates a cycle where wrong decisions erode trust and slow the entire organization. The conversation moves past standard screening to a holistic discipline that begins before a survey ever goes live. Stephanie and Molly walk through a four-layer framework to evaluate and improve data integrity. This model covers everything from survey instrument design to the final proof layer where performance is measured and benchmarked. The traditional filter-first mindset is no longer sufficient in an environment filled with AI-generated responses and sophisticated fraud. By focusing on design and transparency, insights professionals can better defend their numbers and earn a permanent seat at the decision-making table. This talk is a call for the industry to stop relying on trust and start requiring auditable evidence for data quality claims.