How to choose the right chart for your data
The chart type is not decoration — it is the argument. Pick the wrong one and even great data falls flat. Here's the framework I use.
The chart type is not decoration — it is the argument. Pick the wrong one and even great data falls flat. Here's the framework I use.
Most bad charts aren't bad because of colour or fonts. They're bad because the chart type doesn't match the question the data answers. Start with the question, not the chart.
Before touching a charting tool, finish this sentence: "This chart shows that ___." Your answer decides the chart family:
A chart that works for 5 categories breaks at 50. With many entities, don't try to show them all equally — highlight the two or three that carry the story and let the rest recede to a muted background. The reader's eye needs somewhere to land.
If a viewer has to hunt to understand why some lines are coloured and others grey, you've failed. State the rule in a caption ("coloured = the three fastest risers + the biggest faller"). Use full labels, never cryptic codes. Add a legend.
Every headline figure — a total, a percentage, a "leader" — should be calculated from the underlying data, not hand-written. Typed numbers drift out of sync and quietly become wrong. A wrong number is a failed chart, however beautiful.
You can skip the guesswork entirely: drop your CSV into the Nomogram Lab agent and it proposes three chart types matched to your data — previewed with your real numbers — then builds the one you pick to a production standard. See the work for what "production standard" looks like, or read about the services.