Data quality is not a filter, it's a discipline

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.