Data Visualisation for Business: A Practical Guide

A spreadsheet full of numbers contains information. A well-designed chart contains understanding. The difference matters more than most business owners realise.

Data visualisation isn’t about making things look pretty. It’s about making data legible — turning rows and columns into patterns and trends that a human brain absorbs in seconds instead of minutes. Done well, a single chart replaces a paragraph of explanation. Done badly, it creates false confidence in misleading conclusions.

Match the Chart to the Question

Every chart should answer a specific question. The type of question determines the type of chart.

Comparing values? Use a bar chart.

Bar charts are the workhorse of business data. Revenue by month, job count by technician, expenses by category. Whenever you’re comparing discrete items or time periods, a bar chart is almost always right.

• Vertical bars for time series (revenue by month) — the left-to-right flow matches how we naturally read time.
• Horizontal bars for category comparisons (revenue by service line) — especially when labels are long.

Tracking a trend? Use a line chart.

Line charts show movement. They’re ideal for metrics where the trend matters more than any individual data point: monthly revenue, conversion rates, utilisation over time. The line’s slope tells the story instantly — something a table of numbers never does.

Showing parts of a whole? Use a stacked bar.

Pie charts are the default choice here, and they’re almost always wrong (more on that shortly). A stacked bar chart shows proportions far more clearly, especially with more than three categories.

Showing relationships? Use a scatter plot.

Job size vs. profitability. Quote response time vs. win rate. When you want to see whether two things are correlated, a scatter plot makes the relationship visible immediately.

The Charts That Lie to You

Some chart types are actively misleading. Others become misleading through common mistakes.

Pie charts

Pie charts are popular because they’re familiar. They’re also one of the least effective ways to display data. The human eye is terrible at comparing angles and areas. Is that slice 22% or 27%? You can’t tell without a label — and if you need a label to read the chart, the chart isn’t doing its job.

Use instead: A horizontal bar chart, sorted by value. It uses the one thing the human eye excels at — length — and makes comparisons trivially easy.

Truncated Y-axes

A line chart showing revenue “growth” from $498,000 to $512,000 looks dramatic if the Y-axis starts at $495,000. It looks like what it is — a 2.8% change — if the axis starts at zero.

The rule: If the Y-axis doesn’t start at zero, ask why. For business metrics, there’s usually no good reason.

Dual-axis charts

Revenue on the left axis, customer count on the right. The two lines cross at some point and someone draws a conclusion. But the crossing point is meaningless — it’s entirely determined by how the axes are scaled.

Use instead: Two separate charts side by side, or normalise to percentages so both metrics share one meaningful axis.

3D charts

Never. They add zero information and distort relative sizes. A 3D bar chart makes bars at the back look smaller than bars at the front even when they represent the same value.

Colour, Layout, and Clarity

Use colour with purpose

Colour should encode meaning, not decoration. Use one or two accent colours for data you want to highlight, and grey for everything else. Above target? Green. Below target? Red. At target? Grey. The reader’s eye goes to the colour immediately.

Don’t use a rainbow palette. When every bar is a different colour, nothing stands out.

Put the most important metric top-left

For Western readers, the eye lands on the top-left corner first. Your most important metric — revenue, pipeline coverage, utilisation — goes there.

Label directly, not in legends

Don’t make the reader bounce between a chart and a legend in the corner. Label each line at the end of the line, not in a colour-coded box that requires matching.

Making Data Tell a Story

A dashboard full of charts is not a story. Stories have context, comparison, and implication.

Context means comparison. Revenue of $420,000 means nothing on its own. Revenue of $420,000 against a target of $500,000 means you’re 16% short. Revenue of $420,000 compared to $380,000 last year means you’re growing at 10.5%. Always show data alongside at least one reference point: target, previous period, or benchmark.

Highlight the anomaly, not the norm. If nine service lines are on target and one dropped 30%, make the outlier impossible to miss. Use colour, annotations, or callout boxes.

Lead with the “so what.” If utilisation dropped from 72% to 61%, say so in a headline above the chart. “Utilisation down 11 points — scheduling gaps on Wednesdays.” The chart provides evidence. The headline provides the conclusion.

Tools for Business Data Visualisation

Google Looker Studio. Free, browser-based, connects to Google Sheets and various data sources. Good for marketing reporting and basic dashboards. Limited interactivity, but you can’t beat the price.

Power BI. Microsoft’s business intelligence platform. Connects to almost anything, handles large datasets, polished interactive dashboards. Steeper learning curve and requires a licence. Best if you’re already in the Microsoft ecosystem.

Metabase. Open-source, self-hosted. Connects to databases directly, lets non-technical users build charts with a visual query builder. Good middle ground.

Custom-built dashboards. When your data lives across multiple systems that off-the-shelf tools can’t connect to natively, a custom dashboard pulls everything together. No compromises on data sources, layout, or logic.

The Visualisation Checklist

Before you share any chart or dashboard:

1. Can someone understand it in five seconds? If it needs explanation, simplify it.
2. Does it answer a specific question? If you can’t name the question, remove the chart.
3. Does the Y-axis start at zero? If not, justify why.
4. Is the chart type right for the question? Comparison = bar. Trend = line. Proportion = stacked bar.
5. Are colours meaningful or decorative? Cut any colour that doesn’t encode information.
6. Is there a comparison point? Target, previous period, benchmark — context makes data actionable.

Good visualisation isn’t about aesthetics. It’s about speed — how quickly the right person can understand what’s happening and decide what to do about it.