In financial risk management, debates about Bayesian versus frequentist inference are often framed as methodological or philosophical. In practice, the choice is far more pragmatic: it is primarily a data problem. Model risk, drift, and operational risk live upstream of market, credit, and liquidity models. They are shaped less by elegant theory and more by the realities of data volume, stability, and interpretability. This is where the distinction between frequentist and Bayesian inference becomes operationally meaningful.