Show that you can be resourceful and systematic when data is missing: clarify the actual question, find proxies or alternative data sources, and validate assumptions. Emphasize that you would communicate trade-offs and iterate rather than stall.
Pro tip: At Apple, data is often siloed and privacy-sensitive, so mention that you would first check what data is available through internal tools and partnerships before considering external or synthetic data. Also, highlight that you would document your assumptions and share them with stakeholders to maintain trust.
Understand the underlying question and what decision the data is meant to inform. This helps identify what 'good enough' data looks like.
Check internal databases, logs, or adjacent metrics that might serve as proxies. Consider qualitative data like user feedback or support tickets.
If no direct data exists, propose a proxy metric or run a small experiment to collect the needed data. Be creative but rigorous.
Test assumptions with stakeholders and iterate. Use statistical methods to estimate uncertainty and avoid overconfidence.
Present findings with clear caveats and recommend a path forward. Emphasize that decisions can be made with imperfect data if risks are understood.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.