Making data readable isn't just a visual task — it's a product decision about what matters and what doesn't.

Data visualization in product design is the practice of representing information through charts, graphs, and visual formats in a way that helps users understand what's happening and decide what to do. In a product context, this usually means dashboards, reporting screens, analytics features, and any interface where the core value is making data readable.

The challenge is that most data collected in products was collected for operational reasons — not for display. Turning it into something a user can act on requires decisions about what to show, what to hide, and what relationships to highlight. Those decisions are design decisions, not just data decisions.

Chart type is the first design decision and the one most often made carelessly. A few anchoring rules:

• Compare quantities across categories: Bar charts. Almost always bar charts — easy to read, scalable, works in nearly every context.
• Show change over time: Line charts. One line per metric; more than four lines requires a different approach.
• Show proportions of a whole: Pie or donut charts, but only when there are four or fewer segments. Beyond that, use a bar chart with percentages.
• Show correlation between two variables: Scatter plots. Useful in analytics and research features.
• Show distribution: Histograms. Underused in product interfaces, high value in data-heavy tools.

The question to ask before choosing: what comparison am I asking users to make?

A chart can be technically correct and still be unreadable. The most common cause is overloading a single visualization in the name of completeness.

Every element should either encode data or support the reading of data. Gridlines, tick marks, legends, and background colors should be light enough to support without competing. The visual weight hierarchy should run: data first, axis labels second, supporting elements third, decoration never.

Edward Tufte's principle of maximizing the data-ink ratio — minimizing every element that doesn't directly encode information — is the most useful single mental framework for cleaning up cluttered visualizations. If removing an element doesn't reduce the information content, it probably shouldn't be there.

Truncated y-axes. Starting a bar chart at 900 instead of 0 makes small differences look dramatic. Users rarely check axis scales and trust the visual impression instead.

Dual y-axes. Two different scales on one chart imply a relationship between the data series that may not exist. Use them sparingly, if at all.

3D charts. Three-dimensional charts consistently distort comparisons — the depth effect makes bars at the back look smaller. No chart is improved by adding a dimension that isn't representing data.

Missing context. A metric that looks alarming might be seasonal. A spike might be expected. Annotations, reference lines, and period comparisons prevent misinterpretation that damages user trust in the data.

Charts that rely solely on color to encode data fail users with color vision deficiencies — roughly 8% of men and 0.5% of women. Use both color and shape, pattern, or position to distinguish data series. Ensure sufficient contrast between data colors and backgrounds.

Provide text alternatives — data tables or descriptive captions — for users relying on screen readers. An accessible chart is also a more reliable chart for users viewing it in poor lighting, on a low-contrast display, or under time pressure.

Accessibility considerations in data visualization are often skipped because charts are visual by nature. That reasoning is exactly why they get missed — and exactly why they matter.