Your team gets 50 ad-hoc data requests a week. How would you reduce that load without hurting the business?

What the interviewer wants to hear

• Analyse and categorise the requests to find repeated ones
• Build certified datasets and a semantic layer with clear metric definitions
• Create self-serve dashboards for common questions
• Train users and write documentation
• Set up an intake and prioritisation process
• Measure adoption and trust

Model answer

I would first log and categorise a few weeks of requests. Usually 60 to 70% are repeat questions like "sales by region last week" or "how many new users". For those, I build a small set of well-designed dashboards with filters, on top of certified datasets where metrics like revenue and active user are defined once in a semantic layer such as dbt metrics or Looker. I write a short data dictionary and run training sessions so business teams can answer simple questions themselves. For the remaining requests, I set up an intake form with a clear prioritisation rule tied to business impact, so the team spends time on deep analysis instead of pulling numbers. I would measure success by fewer repeat requests, dashboard usage, and whether people trust the numbers. Self-serve fails if definitions are unclear, so governance is part of the plan.

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