Every product team has been there: the funnel metrics look fine, but users are visibly struggling. The A/B test declares a winner, but your best customers are complaining. The dashboard says engagement is up… so why does everyone on the team have a nagging feeling something is wrong?
This conversation sits at one of the most uncomfortable and overlooked tensions in modern product building: what happens when quantitative data and qualitative human insight point in opposite directions… and someone has to make a call.

What will attendees learn? Why data and user feedback conflict more often than most teams admit, and what’s actually going on when they do. How to build a decision-making framework that weighs both signals without defaulting to whoever argues loudest. Where AI-generated analytics is making this tension sharper, not easier… and how to stay grounded when the models are telling you one thing and your users are telling you another.

Who is this for? Founders making product bets with incomplete information. Data and analytics leaders who’ve watched a “data-driven” decision blow up in production. Designers and PMs who’ve lost an argument to a metric they didn’t trust. If you’ve ever said “but the data says...” (or had it said to you) this conversation is for you.

What makes it compelling? This isn’t a theoretical debate about quant vs. qual. It’s a practitioner conversation with real stakes, real failures, and real frameworks, told by two people who’ve lived on both sides of the table. Expect disagreement, candor, and at least one story that will make the audience wince in recognition.