dmoreq Data Storytelling Skill
Transform validated analysis into a decision-ready narrative for stakeholders.
When to Use
- Presenting insights to executives or non-technical audiences
- QBRs or business recommendations that need a "so what"
- Turning statistical / ML findings into an actionable ask
When Not to Use
- Numbers are not yet validated (
data-sciencefirst) - Pure exploratory analysis still in progress
- Technical deep-dives for other data scientists (
technical-reportingif a file is requested) - Simple metric dump with no decision
Related Skills
- Required: base the story on validated findings from data-science. Do not invent numbers.
- Use data-visualization for supporting charts (layout/overlap live there).
- Use technical-reporting only when the user asked to save a formal technical file.
Core Structure
- Hook — Surprising or high-impact finding (with specific numbers).
- Context — Baseline and why it matters.
- Insight — What the data reveals (quantitative evidence).
- Implication — Business meaning and stakes.
- Recommendation — Next actions + expected impact / ROI.
- Ask / Next Step — Specific decision needed from stakeholders.
Key Principles
- Lead with the insight, not the methodology.
- Every number must serve the narrative (no data dumps).
- Prefer simple comparisons and deltas over complex charts.
- State confidence and limitations briefly when relevant.
- Match language to the audience; end with a concrete ask.
Output Expectations
- Start with a headline or TL;DR containing the key insight and proof.
- Charts: one pointer to
data-visualization; tables by default. - Separate findings from recommendations.
Final Checklist
- Insights come from validated
data-scienceoutput - Hook / TL;DR has the core number
- Flow: Context → Insight → Implication → Recommendation → Ask
- Every metric serves the narrative
- Recommendation includes expected impact
- Limitations stated where they change the decision