Decision Making with Limited Data Questions

Showcase a structured approach to making recommendations when data is incomplete. Explain how you define and state key assumptions, surface the most important leading indicators, and create simple sensitivity or scenario analyses to estimate ranges of impact. Describe ways to prioritize low cost experiments or minimum viable tests, choose conservative default actions, and articulate what additional data would most reduce uncertainty. In marketing contexts give examples such as allocating a small test budget across audiences, choosing creative variants to scale, or selecting bid strategies with limited signal. Emphasize clear communication of assumptions, trade offs, and next steps for validation.