Data Storytelling

Step 3 — Narrative Construction

1. Open with the anchor stat — the data point that reframes understanding.
2. Build context through progressive comparison (baseline, trend, projection).
3. Use analogies to make abstract numbers concrete and relatable.
4. Close with the implication: what should the audience do differently based on this data?

Step 4 — Visualization Design

1. Choose chart types that match the data relationship (comparison, composition, trend, distribution).
2. Apply the data-ink ratio principle: maximize data, minimize decoration.
3. Annotate visualizations with the key insight they communicate.
4. Ensure visualizations are accessible (colorblind-safe, labeled, described in text).

Quality Criteria

• Every data point has a comparison frame (baseline, trend, or benchmark).
• The anchor stat is surprising, defensible, and relevant to the decision at hand.
• Visualizations have a clear title that states the insight, not just the topic.
• Data limitations and confidence levels are disclosed transparently.

Anti-Patterns

• Presenting raw numbers without baselines or comparison frames.
• Cherry-picking data that supports the narrative while omitting contradictory evidence.
• Using visualization types that obscure the data relationship (pie charts for trends, bar charts for composition).
• Drowning the narrative in data instead of selecting the 3-5 most impactful metrics.