"Data Drives Decisions" (FK-A034, Faisal Khan, 15 August 2026) lays out how to validate a business idea with interviews, surveys and field research before building anything. Its starting point: "I want to get into the money transfer business" is an industry statement, not a problem statement — the first question is not what sector to enter but what problem is being solved.
The paper's central discipline is not to sell during early interviews. Founders commonly explain their idea to interviewees instead of listening, which contaminates the research by telling the person what problem they should have. The document is blunt about the risk of layering AI on top of that: "if your interviews are biased, the AI will simply help you analyze biased information faster." A null result — the supposedly enormous problem doesn't surface after 15 or 20 conversations — is treated as useful information in itself, not a failure.
What the guide covers:
- Stage one: five to seven unstructured conversations of about fifteen minutes each, with people who actually perform the activity, not friends likely to agree
- Stage two: turning recurring themes into seven to fifteen neutral, trade-off-forcing questions, with roughly 35 respondents as a useful minimum and 100 better still
- Guarding against confirmation bias by actively hunting for evidence that would kill the idea
- Segmenting results by amount sent, payout method or corridor, since overall percentages hide the real signal
- Stage three: taking the research offline, because what people say they do and what they actually do often differ
- Recognizing a "Problem B" that emerges instead of the original idea, and following the evidence rather than ignoring it
The stated payoff: it is far cheaper to change direction after ten interviews than after twelve months of software development. The endpoint is a problem statable in one or two sentences — only then does the product, business model and licensing strategy get decided.