Free tier
Grade your own visualisation first
Everything below is free and public. It is also where every paid engagement starts, so running it yourself costs you an hour and tells you whether you need us at all. Work through the tools, then the two checklists.
1. Self-assessment using tools
Four free tools, each covering a different failure mode. Run your visualisation and its surrounding text through all four.
Harper
Writing and content analysis — catches the prose around the chart.
Open tool(opens in a new tab)Data Visualization Checklist
Stephanie Evergreen's graphic analysis checklist, scored item by item.
Open tool(opens in a new tab)Visual Vocabulary
The Financial Times' guide to matching a chart type to the relationship you are showing.
Open tool(opens in a new tab)FlawViz
A catalogue of documented visualisation flaws and how they mislead.
Open tool(opens in a new tab)
2. Self-assessment by type of design flaw
Three categories of visualisation flaw. For each one, ask whether your chart could be read the way the category describes.
M1–M4
Misinformation
Instances where the visualisation design delivers distorted or deceptive messages.
I1–I4
Uninformativeness
A lack of meaningful information for users to process the data and grab insights.
S1–S2
Unsociability
Where a visualisation makes people feel uncomfortable, offended, or socially awkward.
3. Stephanie Evergreen's checklist
Thirteen items. A visualisation that clears all thirteen is rarely the one holding a talk back.
- Descriptive title and text, subtitles and annotations
- Text size is hierarchical and readable
- Data are labeled directly
- Redundant information removed
- Proportions are accurate
- Display data in an order that makes logical sense to the viewer
- Graph is free from clipart or other illustrations used solely for decoration — some graphics, like icons, can support interpretation
- Color scheme is intentional and used to highlight key patterns
- Color is legible when printed in black and white
- Color is legible for people with colorblindness
- Text sufficiently contrasts background
- The type of graph is appropriate for the data
- Graph has an appropriate level of precision
Adapted from Stephanie Evergreen's Data Visualization Checklist. The original, scoreable version is worth using directly.