Healthcare Analytics Has a Cloud Cost Problem. And It's Not the One You Think.

Healthcare analytics organizations are quietly building some of the most complex cloud data architectures in existence. Petabytes of EHR data. Claims streams arriving in near-real time. Clinical trial datasets that span multiple sites, multiple countries, and multiple consent regimes. Predictive models for patient readmission risk running on top of BigQuery ML. Databricks clusters processing Epic and Cerner extracts into curated Delta tables every four hours. And almost all of them are significantly overspending. Not because they chose the wrong vendors. Not because they over-built their infrastructure. But because the skills required to manage cost at this level of complexity demand something the industry hasn't fully articulated yet: you need to be both a FinOps engineer and a deep query performance specialist, simultaneously, across multiple platforms that each have completely different cost models. I want to walk through exactly what that looks like in practice — where the money go