Before sharing a visualization, reviewing it systematically reduces errors, improves clarity, and protects against misleading communication. This lesson provides a practical checklist for reviewing and improving visualizations before publication.
Why Review Matters
Errors in visualizations are easy to miss when you are close to the work. A chart that looks correct to the person who built it may contain a calculation error, a mislabelled axis, a missing source, or an accessibility problem that only becomes visible when someone else looks at it. Systematic review catches these issues before they reach an audience.
Accuracy Review
- Do the values in the chart match the source data?
- Are calculations correct? Check totals, averages, percentages, and rates independently.
- Are filters applied correctly? Does the chart show the intended subset of data?
- Are missing values handled appropriately — excluded, noted, or shown as zero where zero is the correct value?
- Are aggregations correct? Does "monthly total" mean the sum of daily values, or the average?
Clarity Review
- Does the title clearly state what the chart shows?
- Are axes labelled with units?
- Is the scale appropriate? Does a bar chart start at zero?
- Are categories ordered meaningfully?
- Is the legend clear, or would direct labels be better?
- Is the chart free of unnecessary decoration?
- Can a reader understand the chart without additional explanation?
Honesty Review
- Does the visual framing match the actual magnitude of the finding?
- Is the date range representative, or has it been selected to make a trend look more significant?
- Are percentage changes and percentage-point changes correctly distinguished?
- Is uncertainty or provisional status noted where relevant?
- Are comparisons made on a fair basis — same time period, same population, same definition?
Accessibility Review
- Is there sufficient colour contrast for all text and labels?
- Does the chart use redundant cues — not colour alone — to distinguish categories?
- Is the chart readable in greyscale?
- Are any tables properly structured with headings?
- Are interactive elements keyboard-accessible?
- Is the chart readable at the intended display size — screen, print, or projection?
Source and Attribution Review
- Is the data source credited?
- Is the reporting period or data date shown?
- Are licence conditions for the source data respected?
- If the data was transformed or derived, is that noted?
Privacy Review
- Does the chart display data about individuals or small groups that could allow identification?
- Are suppression or rounding rules applied for small counts?
- Does the chart reveal sensitive locations, health information, or other personal data?
Final Export Review
- Does the exported file look correct at the intended size?
- Are fonts and labels readable in the exported format?
- If the chart is part of a report or dashboard, does it fit correctly in context?
- Has the file been named clearly with a date or version?
When Not to Publish
Sometimes the right outcome of a review is deciding not to publish. If the data does not support the conclusion, if the chart cannot be made accurate and clear, or if publishing would create privacy risks, it is better to delay or revise than to share something misleading or harmful.
Visualization Review Checklist
| Area | Check |
|---|---|
| Question | Does the chart answer a clear question? |
| Audience | Is the chart appropriate for the intended audience? |
| Source | Is the data source credited? |
| Definitions | Are key terms and metrics defined? |
| Calculations | Have totals, averages, and rates been verified? |
| Chart type | Is the chart type appropriate for the data? |
| Scale | Does the axis start at zero (bar charts)? |
| Labels | Are all labels clear and correctly placed? |
| Units | Are units shown on axes and in labels? |
| Context | Is the date range and reporting period shown? |
| Accessibility | Sufficient contrast, redundant cues, readable in greyscale? |
| Privacy | No small-group counts that allow identification? |
| Uncertainty | Is provisional or estimated data noted? |
| Attribution | Are licence conditions respected? |
| Export | Does the exported file look correct at intended size? |
Key Takeaways
- Systematic review catches errors that are easy to miss when you are close to the work.
- Review accuracy, clarity, honesty, accessibility, source attribution, and privacy before publishing.
- Check the exported file at the intended display size.
- Sometimes the right outcome of a review is deciding not to publish.
Suggested Search Terms
- visualization review checklist
- data visualization quality check
- chart accuracy review before publishing
- accessible chart review process