Story Framework

Data Story

A data-first narrative that builds from evidence to insight to action, ideal for analysts and data-driven teams.

Beat Diagram

  1. 1Contextagenda / text
  2. 2Key Findingstats-metrics / chart
  3. 3Analysisdata-visualization / chart
  4. 4Implicationscomparison / text
  5. 5Recommendationthank-you-cta / text

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OPF Config

narratives:data-story
Open Presentation Format
{
  "$schema": "https://pptx.dev/schema/opf/v1",
  "version": "1.0",
  "meta": {
    "title": "PPTX.gallery — narratives/data-story",
    "narrative": {
      "id": "data-story"
    }
  },
  "design": {},
  "slides": [
    {
      "id": "gallery-preview-1",
      "layout": "title-slide",
      "elements": []
    }
  ]
}
Open in OPF Playground

Preview this config live at pptx.dev/playground.

View registry.json

Pace

6-15 slides

10-30 minutes

6 baseline beat slides

Audience Fit

Data-fluent teamsOperations and financeProduct managersAnalytics stakeholders

Tone

Analytical and inductive - builds up from data to insight.

Best For

Analytical briefings, data team presentations, research readouts, business reviews

Example Deck Types

Analytics deep-dive, Research readout, Business review

Example

Set the analytical context, reveal the headline finding, walk through the supporting analysis, explain what it means for the business, then make a specific recommendation.

Beat-by-Beat Guide

1

Context

Frame why this analysis matters now. What question is this data answering?

Instruction: Set up the business question and data scope.

2

Key Finding

State the single most important thing the data shows. One slide, one insight.

Instruction: State the one finding the audience must remember.

3

Analysis

Unpack the data rigorously. Show sources, methodology, and supporting charts.

Instruction: Explain the drivers behind the finding.

4

Implications

Translate findings into business language. What does this mean for strategy, operations, or customers?

Instruction: Translate the analysis into business consequences.

5

Recommendation

Make an explicit, actionable recommendation backed by the data. Include confidence level and assumptions.

Instruction: Recommend the next decision or action.

Recommended Pairings

Layouts

Data VisualizationStats & MetricsMatrix / QuadrantComparison

Color Schemes

Cool HorizonDeep Purple