Instruction: Share an example from your experience, including the steps you took to address the challenge.
Context: This question probes the candidate's problem-solving skills and their ability to make informed decisions under uncertainty.
In one project, I was working on a funnel analysis where several key user events were missing for a meaningful portion of sessions because instrumentation had changed across product versions. The first thing I did was stop treating the data as if it were complete. I wanted to understand exactly what was missing, for which users, and whether the missingness was random or tied to a specific platform, version, or workflow.
After that, I compared alternative sources, used proxy signals where they were defensible, and clearly separated hard facts from assumptions in the analysis. I also documented the gaps and explained the confidence level behind any recommendation. The biggest lesson for me was that incomplete data is manageable if you are transparent about uncertainty. It becomes dangerous when you force false precision onto a dataset that does not support it.
A weak answer says they "filled in the missing data" and moved on, without explaining how they checked whether the gaps were systematic, what assumptions they made, or how they communicated uncertainty.