Data storytelling

Data Storytelling

Property	Value
Domain	Data Analytics
Category	Narrative & Orchestration
Components	SKILL.md + data-storytelling.instructions.md + datastory.prompt.md
Depends	data-analysis (findings), data-visualization (charts), dashboard-design (layout)

Overview

This is the orchestrator skill. It transforms raw data into a complete, coherent narrative by activating analysis, visualization, and dashboard skills in sequence. The output is a self-contained artifact -- typically an HTML dashboard or scrollable report -- where every chart, annotation, and KPI serves the story.

The cardinal rule: data stories have arguments, not just observations. A collection of charts with captions is a report. A data story says "here's what happened, here's why it matters, and here's what we should do."

Module 1: Three-Act Data Structure

Every data story follows this arc:

Act	Purpose	Content
Setup	Establish context and baseline	Who, what, when, where + "here's how things were"
Conflict	Reveal the surprise, problem, or gap	"But then this happened" / "X is not what we expected"
Resolution	Deliver insight and recommendation	"This means X, and we should do Y"

Act Mapping to Dashboard Components

Act	Dashboard Element
Setup	KPI cards showing baseline stats
Conflict	Hero chart revealing the anomaly
Resolution	Supporting charts + annotated insight

Module 2: Audience-First Framing

Before choosing a single chart, identify who will read this and what decision it supports.

Audience	Time Budget	What They Need	Artifact Style
Executive	30 seconds	Headline + action recommendation	KPI dashboard, 5-Visual
Manager	2 minutes	Context + options + trade-offs	Dashboard with filters
Analyst	Unlimited	Full data + methodology + caveats	Detailed report with drill
General	1 minute	Simple story, familiar visuals	Scroll narrative, annotated

Module 3: Knaflic Method ("Storytelling with Data")

The 5-step framework from Cole Nussbaumer Knaflic:

Step	Action
1. Understand context	Who is the audience? What do they need to do?
2. Choose an effective visual	Story intent → chart type (SKILL cross-ref)
3. Eliminate clutter	Remove everything that isn't data or supporting story
4. Focus attention	Use color, size, position to direct the eye
5. Tell a story	Connect visuals with narrative text

Module 4: Duarte Contrast ("What Is" vs. "What Could Be")

Nancy Duarte's tension pattern drives engagement:

What Is:      "Today, we process 500 support tickets per day"
What Could Be: "With the new model, we could process 2000 with the same team"
What Is:      "Current SLA breach rate is 12%"
What Could Be: "Top performers achieve 3% -- the gap is process, not people"

Use this pattern when the story needs to motivate action, not just inform.

Module 5: Big Idea Worksheet

Force the story into one sentence before building anything:

[Subject/audience] should [action/decision]
because [evidence from data].

Examples:

• "The exec team should double Q4 marketing spend because every $1 of email marketing generates $3.20 in pipeline"
• "Engineering should prioritize Region B support because resolution time is 2.5x the company average"

If you can't write the Big Idea sentence, the analysis isn't done yet.

Module 6: Explanatory vs. Exploratory

Mode	You Know the Story?	Goal	Output
Explanatory	Yes	Guide viewer to a conclusion	Annotated dashboard/report
Exploratory	No (yet)	Let viewer discover patterns	Interactive dashboard

Default to explanatory for executives and general audiences. Use exploratory for analysts.

Module 7: Annotation as Narration

Annotations carry the argument. The chart is evidence; the annotation is the lawyer.

Annotation Type	Purpose	Example
Title	State the insight (not the metric)	"Revenue grew 34% but growth is slowing"
Subtitle	Provide context	"Quarterly actuals, FY2024-FY2025"
Callout	Highlight the key data point	Arrow + "Q3 peak: $4.2M"
Caption	Add nuance below the chart	"Note: Q1 2025 includes one-time adjustment"
Footnote	Source, methodology, caveats	"Source: Internal CRM, excludes returns"

Module 8: Orchestration Protocol

When /datastory is invoked, execute these phases in order:

Phase 0: Ingest

• Activate data-ingest.cjs (or inline parse)
• Output: clean columnar data + metadata

Phase 1: Discover

• Activate data-analysis skill
• Output: 3-5 insight statements with story intents

Phase 2: Visualize

• Activate data-visualization skill for each insight
• Match story intent → chart type
• Output: chart specs with titles-as-insights

Phase 3: Arrange

• Activate dashboard-design skill
• Choose layout by audience
• Place KPIs, hero, supporting, table, drill-down
• Output: dashboard scaffold

Phase 4: Narrate

• Apply three-act structure
• Write Big Idea sentence
• Add annotations as narration
• Validate: top-to-bottom read tells the story
• Output: self-contained HTML with narrative

Module 9: Quality Checks

Before delivering the final output:

Check	Pass Criteria
Big Idea exists	One sentence captures the entire story
Three-act present	Setup → Conflict → Resolution identifiable
Titles are insights	No chart titled "Revenue by Quarter" (must be a sentence)
Color consistency	Same category = same color across all visuals
Source attribution	Every chart cites its data source
Audience match	Executive gets 5 visuals, analyst gets drill-down
3-second test	Each chart conveys its point in 3 seconds
Annotation coverage	Hero chart has at least 1 callout annotation
Data freshness	Date range stated; "as of" timestamp included

Anti-Patterns

Anti-Pattern	Problem	Fix
Chart collection	Charts exist but don't connect	Apply three-act structure
Data dump	Every metric shown "just in case"	Write Big Idea first, cut everything else
Insight-free annotations	"This is a bar chart showing revenue"	State what the data means, not what it is
Wrong audience depth	Analyst-level detail for C-suite	Match artifact to audience time budget
No recommendation	Story ends at "here's what happened"	Always include "so here's what we should do"
Over-designed	Fancy visuals, weak argument	Substance over aesthetics