# Data Storytelling

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

- Skill: `majiayu000/data-storytelling` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds add majiayu000/data-storytelling`
- Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/data-storytelling/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: majiayu000 (https://skillmd.com/u/majiayu000)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/majiayu000/data-storytelling

---


# Data Storytelling

Transform raw data into compelling narratives that drive decisions and inspire action.

## When to Use This Skill

- Presenting analytics to executives
- Creating quarterly business reviews
- Building investor presentations
- Writing data-driven reports
- Communicating insights to non-technical audiences
- Making recommendations based on data

## Core Concepts

### 1. Story Structure

```
Setup → Conflict → Resolution

Setup: Context and baseline
Conflict: The problem or opportunity
Resolution: Insights and recommendations
```

### 2. Narrative Arc

```
1. Hook: Grab attention with surprising insight
2. Context: Establish the baseline
3. Rising Action: Build through data points
4. Climax: The key insight
5. Resolution: Recommendations
6. Call to Action: Next steps
```

### 3. Three Pillars

| Pillar        | Purpose  | Components                       |
| ------------- | -------- | -------------------------------- |
| **Data**      | Evidence | Numbers, trends, comparisons     |
| **Narrative** | Meaning  | Context, causation, implications |
| **Visuals**   | Clarity  | Charts, diagrams, highlights     |

## Detailed patterns and worked examples

Detailed pattern documentation lives in `references/details.md`. Read that file when the navigation tier above is insufficient.

## Best Practices

### Do's

- **Start with the "so what"** - Lead with insight
- **Use the rule of three** - Three points, three comparisons
- **Show, don't tell** - Let data speak
- **Make it personal** - Connect to audience goals
- **End with action** - Clear next steps

### Don'ts

- **Don't data dump** - Curate ruthlessly
- **Don't bury the insight** - Front-load key findings
- **Don't use jargon** - Match audience vocabulary
- **Don't show methodology first** - Context, then method
- **Don't forget the narrative** - Numbers need meaning

