Choosing Charts That Actually Communicate

Match the Chart to the Question

Every data point on a dashboard answers a question. The chart type should match the nature of that question, not the shape of the data.

Four relationships cover nearly every dashboard scenario, and each has a chart type that communicates it clearly and a common mistake that undermines it. Before reaching for a chart, name the question. Then choose the chart that answers it most directly.

The principle is straightforward: pick the chart that makes the answer to your question visually obvious. Knowing when to use a pie chart (rarely), when to use a bar chart (often), and when to skip charts entirely is the core skill of dashboard design.

One rule applies across all four: if you need a legend to read it, simplify it. Legends on dashboards are a sign that the chart is doing too much. When in doubt about when to use a bar chart over a pie chart, default to bars. Label directly on the chart elements, or reduce to a single series.

Multi-series line charts deserve special mention because they are the single most overused chart type on dashboards. Three or four lines competing for attention, each a different colour, each requiring the viewer to glance between the legend and the data. On an analysis screen where the user is actively investigating, this is fine. On a dashboard where the user wants a five-second status check, it fails.

The fix is straightforward: split into individual sparklines or single-series charts, each with a clear title. One chart per question. A dashboard chart earns its place by being instantly understood, not by being comprehensive.

The Case for Simple Indicators

Sometimes the best visualisation is no chart at all. A number with context often communicates faster than any graphic.

Dashboards that reserve charts for data that genuinely benefits from visual encoding (and use plain indicators for everything else) are consistently more readable than dashboards where every widget contains a chart. The discipline is knowing when to use which.

Three patterns cover most situations where a simple indicator outperforms a chart.

When "no chart" is the right answer, resist the pressure to add one. Not every metric needs a visual. The instinct to add a chart to every widget is strong, especially when the tool makes it easy.

But the discipline of dashboard design is knowing when a number with context does the job better. A useful heuristic: if the metric has a clear target, use a number with comparison or a progress bar. If the metric has a meaningful trend, use a sparkline chart. If the metric needs comparison across categories, use a bar chart. Before asking when to use a pie chart, ask whether a plain number does the job. The best dashboards use a mix: charts where visual encoding adds something, plain indicators where it doesn't.

Sparklines: The Dashboard Workhorse

A sparkline chart is a tiny, word-sized graphic (usually a line) that shows trend over time without axes, labels, or legends. Edward Tufte coined the term: "data-intense, design-simple, word-sized graphics." On dashboards, they are indispensable.

Sparklines answer the most common dashboard question (is this metric going up or down?) in the smallest possible space. They also solve the "compared to what?" problem without requiring a full chart. A single number provides no sense of history. A sparkline provides twelve months of history in a space no larger than a postage stamp.

Sparklines are versatile, but they have clear limits. Understanding where they excel and where they fall short prevents misuse. They are particularly effective in operational dashboards where many metrics need monitoring simultaneously and space is at a premium.

The best sparkline implementations highlight the endpoint (current value) and optionally mark the high and low points. This gives just enough context without turning the sparkline into a full chart. Keep them simple: a line, possibly a shaded area beneath, and one or two reference dots. Anything more defeats the purpose.

Data Ink Ratio in Practice

Edward Tufte's data ink ratio is a simple principle: maximise the share of pixels devoted to actual data, minimise everything else. On dashboards, where space is scarce and attention is brief, this is not theoretical. It's practical survival.

Every non-data element on a dashboard competes with the data for attention. Gridlines, background shading, decorative borders, 3D effects, gradient fills, ornamental icons: they all consume visual bandwidth without adding information.

On an analytical tool with plenty of screen space, a little decoration is harmless. On a dashboard where twenty metrics share one screen, every pixel of decoration steals attention from the data that matters.

The practical test: look at any element on a chart and ask, "If I remove this, do I lose information?" Apply it ruthlessly.

What survives the data ink test is always the same small set of elements.

Everything else is negotiable. And most of it should go.

Colour as Information, Not Decoration

Colour on dashboards deserves specific attention. In charts and indicators, colour should always carry meaning: green, amber, and red for status. A highlight colour for the current period or selected item, against a neutral grey for everything else. A consistent palette that users learn once and recognise everywhere.

What colour should not do: distinguish twelve categories for no reason (the rainbow problem), make charts "look nice" with gradients, or vary between dashboards so that green means different things in different contexts. If green means on-track on the sales dashboard, it must mean on-track on the operations dashboard too. Consistency matters more than aesthetics.

For users with colour vision deficiency (roughly 8% of men), colour must never be the only encoding. Pair it with icons, text labels, or patterns. A red indicator with an exclamation mark works for everyone. A red indicator alone excludes people. The dashboard UX guide covers accessible design in more depth.

Common Dashboard Chart Mistakes

These mistakes appear on dashboards repeatedly, across industries and tools. Each one makes the dashboard harder to read, and each has a straightforward fix.

If your dashboard contains any of these, the chart is actively working against you.

The pattern is consistent: each mistake violates the data ink ratio by adding visual complexity without adding informational value. The fix is always simplification, not replacement with something more elaborate.

Every one of these mistakes has the same root cause: prioritising how the chart looks over how it communicates. On a dashboard, communication is the only thing that matters. A chart that accurately and instantly conveys its message in the plainest possible way is better design than any visually elaborate alternative.

What Good Dashboard Visualisation Gets You

When chart choices are made deliberately (matched to questions, stripped of decoration, tested against the data ink principle), the dashboard becomes a tool people rely on rather than one they tolerate.

These are the outcomes that follow from the principles above. Each one builds trust, and trust is what turns a dashboard from a reporting obligation into a daily habit.

• ✓
  Metrics understood in seconds Each chart answers one question instantly, without study or interpretation.
• ✓
  Trends visible at a glance Sparklines and properly chosen line charts show direction without consuming valuable dashboard space.
• ✓
  Problems surface before anyone asks Semantic colour and clear indicators make exceptions obvious without requiring investigation.
• ✓
  No chart without a purpose Every visualisation on the dashboard has earned its place by communicating something a number alone cannot.
• ✓
  Honest representation of data No distortion from 3D effects, truncated axes, or misleading dual scales. Trust in the data stays intact.
• ✓
  A dashboard that earns its daily check People open it because it helps them, not because they're told to. That's the only success metric that matters.

The common thread: chart choices are not aesthetic decisions. They are communication decisions. The right chart makes understanding instant. The wrong chart makes the dashboard work against its own purpose.

Approach every chart on a dashboard with one question: does this make the answer obvious? If yes, keep it. If no, simplify or replace.

Go Deeper

Chart selection is one piece of dashboard design. The best chart in the wrong position is invisible. The right interaction pattern turns a static chart into an investigative tool. And the principles here extend beyond dashboards into broader data communication.

These guides cover the rest of the picture.