data-consumer-discovery
Five Discovery Questions
Ask these in order. Each builds on the prior answer:
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"What decision are you trying to make?" Not "what data do you need?" Data requests are solutions. Decisions are the actual problem. If they say "I need a dashboard," ask what decision the dashboard enables.
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"Walk me through the last time you made this decision." Concrete past behavior beats hypothetical future needs. Listen for: where the data came from, how long it took, what they trusted, what they second-guessed.
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"Where did the data come from? How much did you trust it?" Trust is the adoption barrier for data products. A perfect pipeline that nobody trusts is useless. Map their trust signals: source familiarity, recency, whether they cross-checked.
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"What did you end up doing?" The action reveals the real need. "I exported to Excel and manually combined three reports" tells you more than any requirements document.
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"If you could get this answer in under a minute, what changes?" Gauges impact. If the answer is "nothing much," the problem isn't painful enough to build for. If it's "we could catch billing errors before they go out," you have a validated need.
NEVER ask "what data do you want?" or "what would you like us to build?" These questions produce wish lists, not validated needs. The Mom Test applies to data teams: talk about their life, not your product.
Workaround Archaeology
The strongest discovery signal is existing workarounds. If an analyst built a 47-tab Excel workbook, that's a validated need with proven demand.