Create a Data Storytelling Brief

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.