Data presentation and storytelling for data product operators. Narrative structures, chart selection, headline formulas, and anti-patterns. Use when presenting data to stakeholders, building a data presentation, writing an executive summary of findings, telling a data story, or making a case with data. For stakeholder alignment process, see stakeholder-alignment. For metric definitions, see metrics-definition.
Scanned 5/27/2026
Install via CLI
openskills install hollandkevint/data-product-operator---
name: data-storytelling
version: 0.1.0
description: >
Data presentation and storytelling for data product operators. Narrative structures,
chart selection, headline formulas, and anti-patterns. Use when presenting data to
stakeholders, building a data presentation, writing an executive summary of findings,
telling a data story, or making a case with data. For stakeholder alignment process,
see stakeholder-alignment. For metric definitions, see metrics-definition.
user-invocable: false
---
## Headline Formula
Every data finding needs a headline: **[Specific Number] + [Business Impact] + [Actionable Context]**.
"Readmission risk scores now flag 23% more high-risk patients, saving $2.1M annually." Not "We improved our model performance." Not "Results were statistically significant."
The number makes it concrete. The impact makes it relevant. The context makes it actionable.
## Narrative Structures
Pick the structure that matches the situation:
| Structure | When to Use | Shape |
|-----------|------------|-------|
| Problem-Solution | Pitching new work | "Here's the gap, here's what we built" |
| Trend | Status updates | "Here's what changed and why it matters" |
| Comparison | Trade-off decisions | "Here are two options with costs" |
## Narrative Arc for Presentations
**Hook** (the surprise or gap) → **Context** (what the audience needs to know) → **Evidence** (the data, 2-3 charts max) → **Implication** (so what?) → **Recommendation** (now what?)
Start with the finding, not the methodology. Executives care about the answer. They'll ask about the method if they want it.
## Chart Selection
Match the metric type to the right chart:
| Metric Type | Chart | Example |
|-------------|-------|---------|
| Counts | Bar chart | Monthly patient encounters |
| Rates over time | Line chart | 30-day readmission rate by quarter |
| Part-of-whole | Stacked bar | Claim denials by category |
| Distribution | Histogram or box plot | Length of stay distribution |
| Correlation | Scatter plot | Cost vs complexity score |
| Ranking | Horizontal bar | Top 10 diagnoses by volume |
NEVER use pie charts. Stacked bar does everything a pie chart does, with readable labels.
NEVER use dual Y-axes. Two metrics, two charts. Dual axes let you imply any correlation by scaling the axes.
ALWAYS label data directly on the chart. A legend across the room is useless.
## Presentation Anti-Patterns
**Charts without a "so what."** Every chart needs a headline that states the finding. "Figure 3: Revenue by Region" tells the audience nothing. "Northeast revenue dropped 12% after formulary change" tells them what to see.
**Data dumps disguised as analysis.** 47 metrics on one slide helps no one. Pick the 2-3 numbers that matter and make them big.
**Leading with methodology.** "We used a logistic regression with 47 features and cross-validated using..." Save it for the appendix. Lead with the result.
**Hiding bad news in an appendix.** If the data tells an inconvenient story, put it up front. Credibility compounds faster than any dashboard metric.
## Cross-References
For stakeholder alignment process (getting agreement on what to build), see `stakeholder-alignment`. For metric definitions (what exactly does this number mean?), see `metrics-definition`. This skill covers how to present the results.
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