---

name: data-narrative-builder description: Build compelling data-driven narratives and stories. Use when presenting analysis results, creating reports, or communicating data insights through storytelling frameworks.

Data Narrative Builder

Quick Start

Transform data analysis into compelling narratives that engage audiences, build understanding, and drive action using proven storytelling frameworks.

Context Requirements

1. Data/Analysis: Core findings to communicate
2. Audience: Who you're speaking to and their context
3. Goal: What action or understanding you want
4. Format: Presentation, report, email, dashboard
5. Constraints: Time, length, formality level

Context Gathering

"To build your data narrative:

• What's the central insight/message?
• Who's the audience and what do they care about?
• What do you want them to do/understand?
• How will you deliver it?
• Any sensitive topics to navigate?"

Workflow

Step 1: Choose Narrative Structure

class DataNarrative:
    """Build data-driven stories using proven frameworks"""
    
    FRAMEWORKS = {
        'situation_complication_resolution': {
            'structure': ['Situation', 'Complication', 'Resolution'],
            'use_when': 'Presenting problem and solution'
        },
        'hero_journey': {
            'structure': ['Status Quo', 'Incident', 'Quest', 'Discovery', 'Transformation'],
            'use_when': 'Change management, transformation stories'
        },
        'before_after_bridge': {
            'structure': ['Before', 'After', 'Bridge'],
            'use_when': 'Demonstrating impact of change'
        },
        'sparklines': {
            'structure': ['What Is', 'What Could Be', 'Contrast', 'Call to Action'],
            'use_when': 'Inspiring vision, motivating action'
        }
    }
    
    def __init__(self, framework='situation_complication_resolution'):
        self.framework = framework
        self.structure = self.FRAMEWORKS[framework]['structure']
        self.sections = {section: '' for section in self.structure}
        self.data_points = []
    
    def add_section(self, section_name, content):
        if section_name in self.sections:
            self.sections[section_name] = content
    
    def add_data_point(self, value, context, emotional_hook=None):
        self.data_points.append({
            'value': value,
            'context': context,
            'hook': emotional_hook
        })
    
    def build(self):
        narrative = f"# Data Narrative\n\n"
        narrative += f"**Framework:** {self.framework.replace('_', ' ').title()}\n\n"
        narrative += "---\n\n"
        
        for section in self.structure:
            if self.sections[section]:
                narrative += f"## {section}\n\n"
                narrative += f"{self.sections[section]}\n\n"
        
        return narrative

# Example: Situation-Complication-Resolution
narrative = DataNarrative('situation_complication_resolution')

narrative.add_section('Situation', 
    "For the past 3 years, our customer retention has been steady at 85%, meeting industry benchmarks and supporting predictable revenue growth."
)

narrative.add_section('Complication',
    "In Q4, retention dropped to 70% - a 15 percentage point decline. This puts $2M ARR at risk and threatens our growth trajectory. Analysis shows mobile users are churning at twice the rate of desktop users, correlating with our recent app update."
)

narrative.add_section('Resolution',
    "We can recover by rolling back the problematic app update (immediate), launching targeted win-back campaigns (week 1), and investing in mobile UX improvements (month 1). This three-phase approach can restore retention to 82% and save $1.6M ARR within 90 days."
)

story = narrative.build()
print(story)

Step 2: Apply Emotional Arc

def add_emotional_journey(narrative_sections):
    """Layer emotional progression onto data narrative"""
    
    emotional_arc = {
        'Situation': {
            'emotion': 'Stability/Comfort',
            'tone': 'Neutral, establishing context',
            'data_role': 'Baseline, historical performance'
        },
        'Complication': {
            'emotion': 'Tension/Concern',
            'tone': 'Urgent, problem-focused',
            'data_role': 'The surprise, the deviation, the gap'
        },
        'Resolution': {
            'emotion': 'Hope/Confidence',
            'tone': 'Solution-oriented, forward-looking',
            'data_role': 'Projected outcomes, success metrics'
        }
    }
    
    enhanced = {}
    for section, content in narrative_sections.items():
        arc = emotional_arc.get(section, {})
        enhanced[section] = {
            'content': content,
            'emotion': arc.get('emotion'),
            'delivery_notes': arc.get('tone')
        }
    
    return enhanced

enhanced_story = add_emotional_journey(narrative.sections)
print("\n✅ Emotional arc applied")

Step 3: Weave in Data Points

def integrate_data_strategically(narrative, data_points):
    """Place data where it maximizes impact"""
    
    # Principles:
    # 1. Lead with most compelling stat
    # 2. Use round numbers for easy recall
    # 3. Compare to familiar benchmarks
    # 4. Show trend, not just point-in-time
    # 5. Humanize big numbers
    
    enhanced = narrative
    
    # Example transformations
    transformations = {
        'raw': '$2,000,000',
        'rounded': '$2M',
        'contextualized': '$2M (20% of annual revenue)',
        'humanized': '$2M affecting 500 customer relationships',
        'visualized': '⬇️ 15% decline = $2M at risk'
    }
    
    print("\n📊 Data Integration Techniques:")
    for style, example in transformations.items():
        print(f"  {style.title()}: {example}")
    
    return enhanced

enhanced_narrative = integrate_data_strategically(story, narrative.data_points)

Step 4: Build Momentum and Pacing

def structure_pacing(sections):
    """Control information flow and build momentum"""
    
    pacing_guide = {
        'opening': {
            'length': 'short',
            'content': 'hook + context',
            'purpose': 'grab attention, set stage'
        },
        'development': {
            'length': 'medium',
            'content': 'build tension, explore problem',
            'purpose': 'create urgency, show impact'
        },
        'climax': {
            'length': 'short',
            'content': 'key insight or turning point',
            'purpose': 'aha moment'
        },
        'resolution': {
            'length': 'medium',
            'content': 'solution and path forward',
            'purpose': 'inspire confidence, enable action'
        },
        'closing': {
            'length': 'short',
            'content': 'call to action',
            'purpose': 'clear next steps'
        }
    }
    
    print("\n⚡ Pacing Structure:")
    for phase, guide in pacing_guide.items():
        print(f"  {phase.title()}: {guide['length']} - {guide['purpose']}")
    
    return pacing_guide

pacing = structure_pacing(narrative.sections)

Step 5: Add Visual Storytelling

def plan_visual_narrative(narrative_structure):
    """Map visualizations to narrative flow"""
    
    visual_plan = {
        'Situation': [
            {'type': 'line_chart', 'data': '3-year retention trend', 'message': 'Historical stability'},
            {'type': 'benchmark', 'data': 'vs industry average', 'message': 'Meeting standards'}
        ],
        'Complication': [
            {'type': 'before_after', 'data': 'Q3 vs Q4 retention', 'message': 'Dramatic drop'},
            {'type': 'comparison', 'data': 'mobile vs desktop churn', 'message': 'Mobile crisis'},
            {'type': 'timeline', 'data': 'app update correlation', 'message': 'Root cause'}
        ],
        'Resolution': [
            {'type': 'roadmap', 'data': 'three-phase plan', 'message': 'Clear path forward'},
            {'type': 'projection', 'data': 'recovery trajectory', 'message': 'Expected outcomes'}
        ]
    }
    
    print("\n🎨 Visual Narrative Plan:")
    for section, visuals in visual_plan.items():
        print(f"\n  {section}:")
        for v in visuals:
            print(f"    • {v['type']}: {v['message']}")
    
    return visual_plan

visual_plan = plan_visual_narrative(narrative.sections)

Step 6: Craft Opening Hook

def create_compelling_hook(main_insight, audience):
    """Start with attention-grabbing opening"""
    
    hook_techniques = {
        'surprising_stat': "85% retention for 3 years. Then suddenly: 70%. What happened?",
        'question': "What if I told you we lost $2M in 90 days - and almost no one noticed?",
        'bold_claim': "Our mobile app update just cost us $2 million.",
        'story': "Three months ago, Sarah in finance asked a routine question: 'Why are refunds up?' That question led us to discover...",
        'contrast': "While desktop users stayed loyal, mobile users were quietly walking away."
    }
    
    print("\n🎣 Hook Options:")
    for technique, example in hook_techniques.items():
        print(f"  {technique.replace('_', ' ').title()}:")
        print(f"    '{example}'")
    
    # For executives: surprising_stat or bold_claim
    # For technical: question or contrast
    # For general: story
    
    return hook_techniques

hooks = create_compelling_hook("Mobile churn crisis", "executives")

Step 7: End with Clear Call to Action

def craft_call_to_action(recommendations, urgency):
    """Create specific, actionable closing"""
    
    cta = {
        'decision_needed': "Approve immediate app rollback and $150K mobile UX investment",
        'timeline': "Decision needed by Friday (Jan 15) for Feb 1 implementation",
        'accountability': "Engineering: Rollback | Product: UX fixes | Sales: Win-back campaign",
        'follow_up': "Weekly check-ins on recovery metrics starting Jan 22",
        'success_criteria': "Return to >80% retention by March 31"
    }
    
    # Format CTA
    cta_text = f"""
## What We Need From You

**Decision:** {cta['decision_needed']}

**Timeline:** {cta['timeline']}

**Who Does What:**
- Engineering: App rollback (immediate)
- Product: UX improvements (30 days)
- Sales: Win-back campaign (ongoing)

**Success Metrics:** {cta['success_criteria']}

**Next Steps:** {cta['follow_up']}
    """
    
    print(cta_text)
    return cta

cta = craft_call_to_action([], 'high')

Context Validation

• Narrative has clear beginning, middle, end
• Data supports (not overwhelms) story
• Audience perspective considered
• Emotional journey intentional
• Call to action is specific
• Visualizations enhance understanding

Output Template

# [Compelling Title That Hints at Story]

## The Setup (30 seconds)

For 3 years, customer retention held steady at 85% - meeting industry benchmarks 
and supporting predictable $10M ARR growth.

[Visual: 3-year trend line showing stability]

## The Twist (60 seconds)

Then Q4 hit. Retention plummeted to 70% in just 90 days. 

$2M ARR now at risk. Growth projections in jeopardy.

[Visual: Before/after comparison]

But here's what we didn't see coming: It wasn't happening everywhere.

Desktop users? Still 85% loyal.
Mobile users? 35% churning - **2x the rate**.

[Visual: Mobile vs desktop split]

## The Discovery (45 seconds)

What changed? Our data tells a clear story:

• Nov 15: Mobile app update v3.2.0 released
• Nov 20: First spike in mobile churn
• Dec 31: Mobile churn reaches 35%

The timeline is undeniable. The correlation is strong.

[Visual: Timeline with app release and churn spike]

## The Path Forward (90 seconds)

We have a plan. Three phases, 90 days, high confidence.

**Phase 1 (Immediate):** Rollback app to stable version
- Expected impact: Halt churn spike within 2 weeks
- Risk: Low (reverting to proven stable code)

**Phase 2 (Week 1):** Launch mobile user win-back campaign
- Expected impact: Recover 15% of lost users = $300K ARR
- Risk: Medium (depends on offer attractiveness)

**Phase 3 (Month 1):** Invest $150K in mobile UX improvements
- Expected impact: Long-term mobile retention >80%
- Risk: Low (budget approved, team ready)

[Visual: 90-day roadmap with projected recovery curve]

## The Ask (30 seconds)

**We need your approval to move forward.**

Investment: $150K
Return: Save $1.6M of $2M at risk
Timeline: Decision by Friday for Monday rollback

Every week we wait costs $100K in lost revenue.

**Are we approved to proceed?**

Common Scenarios

Scenario 1: "Present analysis to skeptical audience"

→ Lead with their concern → Acknowledge counterarguments → Build credibility with data → Show you understand their perspective → End with low-risk next step

Scenario 2: "Turn monthly metrics into story"

→ Find the narrative thread → What changed and why it matters → Connect metrics to business outcomes → Highlight wins and concerns → Forward-looking action items

Scenario 3: "Explain complex analysis simply"

→ Start with the insight, not the method → Use analogies and metaphors → Progressive disclosure of complexity → Visual storytelling → "So what?" for each finding

Scenario 4: "Rally team around data insights"

→ Hero's journey framework → Team as protagonist → Data as guide/mentor → Action as quest → Success as transformation

Handling Missing Context

Just has data, no story: "Every dataset has a story:

• What changed?
• Why does it matter?
• What should we do? Let's find your narrative thread."

Boring presentation of facts: "Facts alone don't move people. Add:

• Emotional connection (impact on customers)
• Tension (what's at stake)
• Resolution (path forward)
• Call to action (specific next step)"

Too complex to follow: "Simplify the story:

• One main message
• Three supporting points
• Remove everything else Details in appendix for interested."

Advanced Options

Story Templates: Pre-built narrative structures for common scenarios

Data Storytelling Workshops: Train teams on narrative techniques

Narrative Testing: A/B test different story structures

Automated Insights: AI-generated narrative summaries

Interactive Storytelling: Scrollytelling, data-driven experiences