Ryan Crochet

Data modeling didn't disappear - it got squeezed out. Agile delivery and cloud migration made careful modeling look like ceremony, and the work quietly narrowed to whoever still had the skills for it. AI is now exposing the cost: systems nobody can explain and meaning that lives in one person's head. But reviving the practice doesn't mean routing everything back through a single team. Rigorous enterprise modeling and faster, AI-assisted modeling suit different people and different tempos - architects working one way, analysts and engineers another, against a shared foundation. So, who plays which position? Where does iteration speed pay off, and where does it cost you? This session makes the case for modeling as a shared practice - one where business outcomes are built in from the first pass, not reverse-engineered at the end.