Skills · Data & AI

Data Storytelling

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Transform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or building executive presentations.

Originally by wshobson · MIT

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Transform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or building executive presentations.

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data-storytelling/SKILL.md71 lines2.1 KBRawView on GitHub
Frontmatter — 2 properties
namedata-storytelling
descriptionTransform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or building executive presentations.
1---
2name: data-storytelling
3description: Transform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or building executive presentations.
4---A5No allowed-tools declared — no way to tell what this skill may touch
5 
6# Data Storytelling
7 
8Transform raw data into compelling narratives that drive decisions and inspire action.
9 
10## When to Use This Skill
11 
12- Presenting analytics to executives
13- Creating quarterly business reviews
14- Building investor presentations
15- Writing data-driven reports
16- Communicating insights to non-technical audiences
17- Making recommendations based on data
18 
19## Core Concepts
20 
21### 1. Story Structure
22 
23```
24Setup → Conflict → Resolution
25 
26Setup: Context and baseline
27Conflict: The problem or opportunity
28Resolution: Insights and recommendations
29```
30 
31### 2. Narrative Arc
32 
33```
341. Hook: Grab attention with surprising insight
352. Context: Establish the baseline
363. Rising Action: Build through data points
374. Climax: The key insight
385. Resolution: Recommendations
396. Call to Action: Next steps
40```
41 
42### 3. Three Pillars
43 
44| Pillar | Purpose | Components |
45| ------------- | -------- | -------------------------------- |
46| **Data** | Evidence | Numbers, trends, comparisons |
47| **Narrative** | Meaning | Context, causation, implications |
48| **Visuals** | Clarity | Charts, diagrams, highlights |
49 
50## Detailed patterns and worked examples
51 
52Detailed pattern documentation lives in `references/details.md`. Read that file when the navigation tier above is insufficient.
53 
54## Best Practices
55 
56### Do's
57 
58- **Start with the "so what"** - Lead with insight
59- **Use the rule of three** - Three points, three comparisons
60- **Show, don't tell** - Let data speak
61- **Make it personal** - Connect to audience goals
62- **End with action** - Clear next steps
63 
64### Don'ts
65 
66- **Don't data dump** - Curate ruthlessly
67- **Don't bury the insight** - Front-load key findings
68- **Don't use jargon** - Match audience vocabulary
69- **Don't show methodology first** - Context, then method
70- **Don't forget the narrative** - Numbers need meaning
71 

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