In this episode, Sanjiv Kumar shares hard-won lessons from nearly four decades in data at companies like Moody's, Bear Stearns, and Freddie Mac. He challenges the traditional warehouse-first approach to data, arguing that organizations should start with value and work backwards. Sanjiv explains how defining specific data products — rather than piling data into lakes and warehouses — can reduce risk, break down silos, and unlock the data needed for AI. A must-listen for anyone rethinking their data strategy.
