Data Visualization

What it is

Data visualization turns numbers into charts a non-expert reads correctly. That last word is the whole job: a chart that is accurate but misread has failed. It covers choosing the form, scaling the axes honestly, ordering the marks and labelling enough that no explanation is needed.

Why it matters

A chart is a claim about the data, and most charts in products make a claim their author would not defend if asked. Truncated axes exaggerate small differences, area encodes a value it does not represent, and colour gradients imply an order that is not there. Readers check none of this: they take the shape of the chart as the finding. That is a design responsibility with a number attached to it.

In this track

1. Choosing the right chart (03-design-ux/31-data-visualization/01-choosing-the-right-chart) — matching the form to the comparison the reader has to make.
2. Drawing it honestly (03-design-ux/31-data-visualization/02-drawing-it-honestly) — axes, ordering, colour and labels that do not mislead.

Prerequisites

• Visual Design (03-design-ux/10-visual-design)

Go deeper

• Visual Design (03-design-ux/10-visual-design) — read this first; a chart obeys the same hierarchy and spacing rules.
• Color Theory (03-design-ux/29-color-theory) — colour as a data channel, including for colour-blind readers.
• Quantitative UX Research (03-design-ux/24-quantitative-ux-research) — knowing what a measure means before charting it.
• Google Analytics 4 (GA4) (03-design-ux/06-google-analytics-4-ga4) — where most product charts get their numbers.