Create a Data Storytelling Brief
Translate M&E findings into a structured data storytelling brief with a narrative arc, key messages, supporting data points, and audience-specific framing.
Fill In Your Details (7 Blanks)
Add your information to get a tailored prompt. You can also use it as is.
Sector and Donor Context (Optional)
Your Prompt (With Your Details)
This is the complete prompt, ready to copy and paste.
You are a senior MEAL specialist with expertise in data communication and evidence-based storytelling. Your task is to create a Data Storytelling Brief that translates M&E findings into a compelling narrative for a specific audience.
Context:
- Program name: the program whose findings are being communicated
- Findings source: the evaluation or monitoring report providing the data
- Sector: the program's sector
- Geographic scope: the program's coverage area
- Target audience: the primary readers of this brief
- Key findings to communicate (list 3-6): the main results to translate into a story
Deliverables:
1. Audience Profile
- Who they are and their decision-making role
- What they already know about this program
- What decisions or actions this brief should influence
- Their data literacy level and format preferences
- Emotional and political context (what motivates them, what concerns them)
2. Core Narrative Arc Structure the story using the following framework (per Cole Nussbaumer Knaflic's storytelling with data approach):
- Setup: What was the problem or need? What did the program set out to do?
- Tension: What challenges emerged? What was at stake? Where did results diverge from expectations?
- Resolution: What did the evidence show? What worked, and what needs attention?
- Call to action: What should the audience do with this information?
Write a 150-200 word narrative draft that connects the findings into a coherent story. Avoid jargon. Lead with the most compelling finding.
3. Key Messages (3-5) For each key message:
- The message in one sentence (clear, active voice)
- The supporting data point with exact figures
- A comparison or benchmark that gives the number meaning
- A brief human-interest hook or contextual detail that makes the number memorable
Present as a structured table.
4. Data Points for Visualization For each finding, recommend:
| Finding | Headline Statistic | Recommended Visual | Why This Visual Works | Data Needed |
|---|
Follow Stephen Few's principles: choose chart types based on the relationship being shown (comparison, trend, composition, distribution, or correlation). Avoid pie charts for more than 4 categories. Use bar charts for categorical comparisons and line charts for trends over time.
5. Audience-Specific Framing Provide alternative framings for at least 2 additional audiences beyond the primary target:
- Technical M&E audience (methodology-focused framing)
- Community or beneficiary audience (plain language, outcome-focused)
- Media or public audience (headline-driven, human interest)
For each, specify: lead message, tone, level of detail, and one recommended format.
6. Do's and Don'ts List 5 do's and 5 don'ts specific to this audience and these findings, drawing on best practices from Stephanie Evergreen's data visualization guidance and Jonathan Schwabish's work on communicating with data.