Structuring a Data Narrative
Explore how to structure a data narrative that links charts to decision-making through context, insight, so-what, and recommendation. Understand how to present evidence honestly, estimate business impact, state assumptions clearly, and frame actionable recommendations under uncertainty to support effective decision-making with data.
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Ten charts can describe a subscription business without answering whether leadership should fund onboarding improvements or change pricing. A data narrative connects evidence to a decision through four linked parts: context states the decision, stakes, and shared definitions; insight gives the most important pattern the charts show; so-what explains the practical consequence of that pattern; and recommendation proposes a specific action while naming its tradeoffs.
The order matters. Context prevents a churn rate from floating without a time period or customer group. Insight prevents a presentation from becoming a chart-by-chart tour. So-what makes the business consequence visible. Recommendation turns an observation into a decision that someone can approve, delay, or reject.
A useful narrative does not claim more than the charts support. A churn trend can show that churn increased. A tier comparison can show that one tier has a higher churn rate. Neither chart alone proves why customers left.
One decision: Build the narrative around one decision. Supporting charts should strengthen the same conclusion, not compete for attention.
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For a subscription business, leadership must choose between investing in onboarding improvements and changing pricing. The available evidence includes monthly churn, churn by customer tier, and the time customers take to reach ...