Data quality is not a filter, it's a discipline
July 21, 2026
Data quality is often treated as a checkbox, but the impact of bad data reaches far across an organization. Insights teams must move from accepting claims to demanding proof. The hosts discuss the hidden costs of poor quality, including the erosion of stakeholder trust and slowed decision velocity. By explaining a framework centered on prevention and proof, they show how the industry can move toward a more disciplined approach where transparency becomes the primary buying criterion for research partners.
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.
What You’ll Learn:
- The four costs of bad data that never show up on an invoice
- How the erosion of stakeholder trust creates a data quality loop of hell
- The difference between a filter-first mindset and a discipline-first approach
- Why 70 percent of fraud slips through standard industry screening criteria
- The four p framework for evaluating data quality: prevent, protect, purify, and prove
- How survey length and device type contribute to respondent fatigue
- Why the proof layer is the most defensible part of the research process
- The red flags to watch for when evaluating a research vendor
- Why transparency is becoming the ultimate buying criterion for insights teams
Episode Resources:
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