Data Storytelling

Overview

The Data Storytelling skill transforms analytical insights into compelling, actionable business narratives. It bridges the gap between complex analysis and executive decision-making by generating clear, contextual, and persuasive communications tailored to different audiences.

Capabilities

•Insight prioritization and selection
•Narrative structure generation
•Chart annotation automation
•Key takeaway extraction
•Executive summary generation
•Recommendation framing
•Action item identification
•Audience-appropriate language adaptation

Used By Processes

•Insight-to-Action Process
•Executive Dashboard Development
•Decision Documentation and Learning

Usage

Insight Input

python

# Analytical insights to narrate
insights = {
    "context": {
        "analysis_type": "quarterly_performance",
        "period": "Q3 2024",
        "audience": "executive_leadership",
        "objective": "investment_decision"
    },
    "key_findings": [
        {
            "metric": "Revenue",
            "value": 12500000,
            "change": 0.15,
            "benchmark": "above_target",
            "significance": "high",
            "drivers": ["new_product_launch", "market_expansion"]
        },
        {
            "metric": "Customer Acquisition Cost",
            "value": 185,
            "change": 0.22,
            "benchmark": "above_target",
            "significance": "medium",
            "drivers": ["increased_competition", "channel_mix_shift"]
        }
    ],
    "supporting_data": {
        "visualizations": ["revenue_trend.png", "cac_breakdown.png"],
        "tables": ["segment_performance.csv"]
    }
}

Narrative Configuration

python

# Narrative structure configuration
narrative_config = {
    "structure": "situation_complication_resolution",
    "tone": "professional",
    "length": "executive_summary",  # or "detailed_report"
    "format": "markdown",
    "sections": [
        "headline",
        "key_takeaways",
        "context",
        "analysis",
        "recommendations",
        "next_steps"
    ],
    "emphasis": "actionable_recommendations"
}

Audience Adaptation

python

# Audience-specific settings
audience_profiles = {
    "executive_leadership": {
        "detail_level": "high_level",
        "jargon": "minimal",
        "focus": "strategic_implications",
        "format_preference": "bullet_points",
        "time_available": "2_minutes"
    },
    "technical_team": {
        "detail_level": "detailed",
        "jargon": "acceptable",
        "focus": "methodology_and_data",
        "format_preference": "full_narrative",
        "time_available": "15_minutes"
    },
    "board_of_directors": {
        "detail_level": "summary",
        "jargon": "none",
        "focus": "business_impact",
        "format_preference": "visual_heavy",
        "time_available": "5_minutes"
    }
}

Narrative Structures

Structure	Best For	Flow
SCR (Situation-Complication-Resolution)	Problem-solving	Context -> Challenge -> Solution
Pyramid	Executive updates	Conclusion -> Supporting points -> Details
Before-After-Bridge	Change proposals	Current state -> Future state -> How to get there
STAR	Case studies	Situation -> Task -> Action -> Result
What-So What-Now What	Quick insights	Finding -> Implication -> Action

Input Schema

json

{
  "insights": {
    "context": "object",
    "key_findings": ["object"],
    "supporting_data": "object"
  },
  "narrative_config": {
    "structure": "string",
    "tone": "string",
    "length": "string",
    "sections": ["string"]
  },
  "audience": {
    "profile": "string",
    "detail_level": "string",
    "time_available": "string"
  }
}

Output Schema

json

{
  "narrative": {
    "headline": "string",
    "executive_summary": "string",
    "sections": {
      "section_name": "string (markdown)"
    },
    "key_takeaways": ["string"],
    "recommendations": ["string"],
    "next_steps": [
      {
        "action": "string",
        "owner": "string",
        "timeline": "string"
      }
    ]
  },
  "annotations": {
    "visualization_id": "string annotation"
  },
  "metadata": {
    "word_count": "number",
    "reading_time": "string",
    "complexity_score": "number"
  }
}

Best Practices

•Lead with the most important insight (inverted pyramid)
•Use specific numbers, not vague descriptors
•Connect data to business outcomes
•Include clear calls to action
•Acknowledge limitations and uncertainties
•Use active voice and strong verbs
•Test narrative with representative audience member

Annotation Guidelines

For chart annotations:

•Highlight the key insight, not just describe the data
•Use arrows and callouts sparingly
•Provide context (comparisons, benchmarks)
•Include "so what" implications

Integration Points

•Receives insights from all analysis skills
•Connects with Decision Visualization for annotated charts
•Feeds into Decision Journal for documentation
•Supports Insight Translator agent for communication