| name | data-story |
| description | Communicate data insights to non-technical stakeholders in a structured, persuasive format. Use when the user says "present these findings", "explain this analysis to leadership", "make a data presentation", "tell the story behind the data", "translate this into a business narrative", "make this insight actionable", "stakeholder readout", "executive summary of results", or needs to turn analysis output into a decision-ready document or slide deck.
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Overview
Based on "Storytelling with Data" by Cole Nussbaumer Knaflic. The core principle: data does not speak for itself. Context, audience, and a clear call to action determine whether an insight drives a decision or gets ignored. Every data communication is a story with a protagonist (the audience), a tension (the problem), and a resolution (the recommended action). Remove everything that doesn't serve that arc.
Workflow
Step 1: Define the audience and the decision they need to make
Before touching a chart or a sentence, answer:
- Who is in the room? (technical ICs, business leads, C-suite?)
- What decision do they need to make as a result of seeing this?
- What do they already believe? What might they push back on?
Write one sentence: "After seeing this, [audience] should [specific action or decision]."
This sentence controls everything downstream.
Step 2: Lead with the conclusion, not the methodology
Knaflic's rule: the insight goes first. The supporting evidence follows.
Structure:
- Situation - what context does the audience need?
- Complication - what changed or what problem exists?
- Resolution - what does the data say to do?
Bad order: "We ran a 4-week A/B test. We measured conversion, retention, and revenue. Here are the results. [charts] Based on this, we recommend..."
Good order: "Variant B increased revenue per user by 12%. Here's why it worked and what we should do next."
Step 3: Choose one chart per insight
Do not show a chart because you have it. Show a chart because it communicates something words cannot.
Chart selection guide:
- Comparison over time: line chart
- Comparison between categories: bar chart (horizontal if > 5 categories)
- Part-to-whole: stacked bar or pie (only if 2-3 slices)
- Distribution: histogram or box plot
- Relationship between two variables: scatter plot
Remove: dual-axis charts, 3D charts, pie charts with > 3 slices, and any chart where the title is a label instead of an insight.
Step 4: Edit every chart to carry a single message
Each chart should have:
- A title that states the finding ("Conversion dropped 18% after the November release" not "Conversion rate over time")
- A single highlighted data series or data point that draws the eye to the key number
- All non-essential gridlines, legends, and axes labels removed or minimized
Apply Knaflic's preattentive attributes to guide attention: use color sparingly (one accent color for the key insight), use bold or size for emphasis, use position to show ranking.
Step 5: Write the narrative scaffolding
For a written report or slide deck, each insight needs a three-part structure:
Headline (1 line): The finding, stated as a conclusion.
Evidence (2-3 bullets): The numbers that prove the headline.
Implication (1 line): What this means for the decision at hand.
Example:
Headline: Power users drive 80% of revenue despite being 12% of accounts.
Evidence:
- Top 12% of accounts by login frequency generated $4.2M of $5.3M total ARR.
- Median revenue per power user: $1,400/yr vs. $90/yr for casual users.
- Power users churn at 4% annually vs. 31% for casual users.
Implication: Retention investment should prioritize power user health, not broad activation.
Step 6: Review for clutter and call to action
Before finalizing, cut:
- Any slide or section that does not directly support the audience's decision
- Methodology details that belong in an appendix
- Charts that show "we did a lot of work" rather than proving a point
End every presentation or report with a single, explicit ask:
"We recommend [action]. We need [decision] by [date] to [outcome]."
Anti-Patterns
1. The data dump
Bad: Sharing a 40-chart dashboard and telling the audience to "explore it".
Good: Curating 3-5 charts that build a specific argument and presenting them in order.
2. Titles that describe instead of conclude
Bad: Chart title = "Monthly Active Users by Segment, Jan-Dec"
Good: Chart title = "Enterprise segment MAU grew 34%; SMB flat for 6 months"
3. Using color for decoration
Bad: Each bar in a bar chart is a different color.
Good: All bars are grey except the one being discussed, which is the accent color.
4. Burying the recommendation at the end
Bad: Walking through all the analysis before stating what the audience should do.
Good: State the recommendation in the first 30 seconds or on slide 2. Everything after is evidence.
Quality Checklist
