How do I make good decisions when the data is thin?

You can build the standard models now, and where you lose your footing is the thin-data edge, the calls where the numbers run out before the decision does. That's not a hole in your skill. It's the place where risk work stops being math and starts being judgment, and everyone feels shaky when they first cross that line. When the data is thin, no model saves you, because the model was never the whole job. The point is to reason clearly under uncertainty: state your assumptions, weigh what little you have, look for comparable cases, and be honest about your confidence. A good analyst with thin data doesn't fake certainty. They make a defensible call and say plainly what could make it wrong. Build the habit of writing down your reasoning on the hard ones, then checking later how they turned out. That feedback loop is how judgment gets sharp. Over time you'll trust yourself in the gray zone, not because the data got better, but because you learned to think well when it isn't there. That skill is worth more than any model.