Treat what stakeholders say as a hypothesis and check it against the system. Pull volumes, exception rates, and timings from the actual data before agreeing a problem is worth solving, because perceived frequency and real frequency often differ by an order of magnitude. Doing it early also sizes the benefit, which is what turns a requirement into a business case rather than an opinion.
Why interviewers ask this
The analyst role has moved much closer to data work, so interviewers want evidence you can check claims yourself rather than relying on assertions. They listen for querying source systems or partnering with someone who can, for sizing benefit as well as verifying the problem, and for handling the awkward case where the data contradicts a senior stakeholder without turning it into a confrontation.
How to structure your answer
- Treat stakeholder statements as hypotheses to be tested.
- Pull volumes, timings, and exception rates from the source system.
- Use the numbers to size the benefit, not just confirm the problem.
- Present contradicting data as a question rather than a correction.
Example answer
I take what people tell me as a hypothesis and then go and look. Someone will say this happens all the time and it is killing us, and it is worth finding out whether all the time means daily or twice a quarter, because the answer changes what we should build. On one project the business case rested on a manual rework loop everyone described as constant. When I pulled the numbers it was about four percent of volume, but those cases took eleven days each and touched three teams, so the real story was duration rather than frequency, and the solution we scoped ended up completely different. I write my own SQL where I have access, and where I do not I find someone in the data team and buy them a coffee. When the data disagrees with a senior stakeholder I bring it as a question rather than a correction, because you want the conversation to continue.
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See how it worksFollow-up questions to expect
- What do you do when the data quality is too poor to trust?
- How do you get access to systems you have no permissions on?
- Tell me about a time the data proved a stakeholder wrong.
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