Behavioral · medium

How do you handle a metric that means different things to different teams?

Asked in BI Developer interviews.

Short answer

Name and define each version explicitly rather than shipping one ambiguous revenue — gross vs net, booked vs recognized — and document the definition on the dashboard itself. Get teams to agree the canonical one where you can; where you can't, label them so nobody argues over a number that was quietly measuring two things.

How to answer it

Stop treating it as one metric. "Revenue" that is gross to sales, net of refunds to finance, and recognised over time to accounting is three metrics that share a name, and every argument about the number is really an argument about which one is meant.

The fix has three parts:

• Name them. Gross bookings, net revenue, recognised revenue. Each with a written definition: what is included, what window, what currency rule. The definition lives next to the data, in the metrics layer or the model, not in a wiki nobody reads.
• Agree a canonical one for company-level reporting. This is a decision for whoever owns the number, usually finance, and the analyst's job is to make the tradeoffs clear, not to pick.
• Label the others where they appear. A dashboard that says "Revenue" with no qualifier is the bug. One that says "Net revenue (refunds within 30 days deducted)" cannot be misread.

Then the mechanical part: one tested model per definition, and every dashboard reads the model rather than recomputing the number in its own query. When two dashboards disagree, the cause is always two implementations, and one implementation removes the cause.

Worked line: "Sales and finance disagreed on Q2 by 8%. It was refunds and a currency date. Three named metrics, one model each, definitions printed on the dashboard. The Q3 meeting spent zero minutes on it."

• What they are checking: naming and definition over adjudication, and putting the definition where the number is.
• Common mistake: picking a winner and quietly relabelling the other team's dashboard. They notice.

Related questions

• Tell me about a time you influenced a decision using data.
• Tell me about a time you had to meet a tight deadline.
• Describe the most technically challenging data problem you've solved.
• Tell me about a conflict with a stakeholder and how you resolved it.
• How do you decide what belongs in the transformation layer versus the BI tool?
• A number on the dashboard suddenly spikes. Walk me through what you check.

Practice this for real

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