Detailed definition
What-if analysis is the practice of adjusting assumptions and observing the projected effect on metrics. In BI, this can include changing prices, conversion rates, demand levels, costs, staffing assumptions, or timing assumptions.
The goal is not to predict the future perfectly. The goal is to make assumptions explicit and compare scenarios using the same definitions used in reporting.
Why it matters
Reporting tells teams what happened. What-if analysis helps them explore what could happen next if a variable changes. That makes BI more useful for planning, prioritization, and decision-making.
When what-if analysis is connected to governed metrics, teams can discuss scenarios without losing alignment on the base numbers.
How it works
The BI platform provides scenario inputs, calculations, and visual outputs. Users adjust a variable and the system recomputes affected metrics based on modelled relationships and assumptions.
AI can help by creating scenario views from natural language, suggesting variables to test, summarizing outcomes, or turning a scenario workflow into a reusable agent.
Practical examples
- A finance team tests how a price increase affects revenue and margin.
- A marketplace team changes take-rate assumptions to compare growth plans.
- A retail team estimates the effect of promotion changes on sell-through.
- A sales leader compares best-case, base-case, and worst-case pipeline outcomes.
Common pitfalls
- What-if analysis is only as good as the assumptions behind it.
- It should not blur actuals, forecasts, and scenarios. Users need clear labels.
- Scenarios should reuse governed metrics so planning and reporting stay aligned.