Purpose of this Guide
How to communicate insights clearly, confidently, and effectively.
Great data visualization is more than well-designed charts. It’s about making sure your audience understands the right message, remembers what matters, and can take informed action.
These guidelines focus on communication, narrative, and delivery, the parts of data work that help insights land.
Clarify the Message
- Lead with the conclusion, the core point you want remembered
- Write a clear one to two sentence insight
- Define the message before choosing visuals
- Begin with meaning rather than the chart
Narrative Flow and Framing
- Use a simple structure such as Problem to Insight to Action
- Use a simple structure such as Insight to Evidence to Implication
- Provide context so values make sense
- Use comparisons such as before and after or trend versus benchmark
- Explain why the change or result matters
Choosing the Right Level of Detail
- Match detail to the audience
- Executives want high level trends, implications, and decisions
- Operational leaders want drivers, causes, and recommended actions
- Analysts want methods, definitions, and assumptions
- Students and public audiences want simple, clear language
- Include only the information needed to understand or act
Knowing More Than You Share
- Remember that analysts always know more than the audience
- Your job is to make the insight understandable, not to present everything you discovered
- Include only the information that supports the core message
- Hold back details that create confusion or distract from the point
- Share complexity only when it changes the interpretation
- Trust that clarity matters more than completeness
Supporting Self Service Analytics
- Remember that self service analytics is not the same as self interpretation
- Your goal is to guide users toward meaning rather than make them search for it
- Provide simple summaries so users understand the insight without exploring every filter or page
- Use clear labels, consistent color meaning, and descriptive titles to reduce confusion
- Anticipate common questions and answer them directly in the content
- Avoid creating dashboards that rely on expert knowledge to understand
- Design for clarity so users can trust what they are seeing and act confidently
Using Pre Attentive Attributes for Storytelling
- Use color to highlight the key point
- Use contrast to guide the eye
- Use white space to separate ideas
- Use position to establish hierarchy
- Reduce noise by dimming background elements
Annotation and Explanation
- Explain insights where they appear
- Call out spikes, drops, transitions, and important events
- Use short plain language explanations
- Use movement verbs such as rises, increases, declines, or stabilizes
Feedback and Collaboration
- Work with others early in the process
- Share drafts to catch misunderstandings
- Collaborate with both data savvy and new users
- Check that others can restate the insight in clear language
- Be transparent about sources, definitions, and refresh schedules
- Remember that visualization creators do their best work when they do not work alone
Managing Cognitive Load
- Present the big picture first
- Reveal details only when needed
- Keep each visual focused on a single idea
- Avoid crowded dashboards
- Support the reading path with spacing and layout
Story Delivery
- Begin with the insight and then show the evidence
- Use the sequence Insight to Evidence to Implication
- Guide the audience from high level to drivers to recommendations
- Prepare for questions about definitions, timing, and methods
Sharing Your Work
- Include a clear summary in plain language
- Provide update notes, refresh timing, and key caveats
- Link to documentation or methodology when appropriate
- Choose formats such as dashboards, PDFs, slide decks, or workspaces
- Ensure accessibility with alt text, color safe choices, descriptive summaries, and keyboard friendly navigation