Source: Wikipedia. Anscombe's quartet.
Always remember it's not a data story you're telling, it's a value story. To make that happen, you must demonstrate clarity and establish credibility.
First put together this checklist and review it several times: What is the message? Why is this valuable, or at least interesting, to your audience? Where did the data come from? Why are the data believable?
Follow these 5 tips to get to clarity and credibility:
1. Bold opening statement or question. Begin with a crisp, clear message. If a reader's time is cut short, what key point should they remember? When opening with a question, be sure to answer it explicitly in closing summaries/conclusions (sounds simple, but oftentimes it's missed, draining impact from the content).
2. Inverted pyramid. Follow your opening statement with a summary of the key points: What, who, when, where, why. Use the journalism approach of giving away the ending, and then filling in background. Apply the inverted pyramid concept to both writing and data; so for example, present important charts or tables first, and raw data or other supporting data later.
3. Data visualization. Give them some 'Ooh, shiny', but not too much (I'm growing weary of the hero worship of artistic data viz creators). Visuals can tell a story that writing cannot: Reference the classic Anscombe's Quartet graphic above. Anscombe illustrated beautifully how four distinct data sets can have the same mean x, mean y, sample variance, etc. – and that only through visuals do we see their notable differences. A simple presentation of the statistics would not tell the whole story.
4. Explain the source. Writing must tell the rest of the value story: Where did the data come from? Why were they analyzed this way? Why is this a valid and useful finding? After providing clarity, now you're establishing credibility.
5. Engage the skeptics. Essential to establishing credibility. Identify potential challenges and tough questions expected from the audience. When possible, discuss the limitations and acknowledge the gaps in your findings. What questions remain? What further research is needed? By addressing these directly, you can spark a conversation with the audience.
Examples & Sources
Writing about data: Excellent journalist – Jason Zweig Health economics analytics – Context Matters Health consultants – Evidera Business and trade groups American Medical Writers Association ISPOR (International Society For Pharmacoeconomics and Outcomes Research)
Presenting data / Data visualization: Stephen Few Flowing Data – Nathan Yau Business intelligence tech vendors, such as Tableau; Great article – Why the beautiful, time-tested science of data visualization is so powerful Edward Tufte's book – Beautiful Evidence