Data Stories: Essential Data Visualisation Tips and Chart Choices

Data visualisation is more than just making numbers look attractive; it’s about telling clear, compelling stories with data. The right visual can reveal patterns, highlight trends, and make complex information accessible at a glance. Yet, choosing the wrong chart or overcomplicating your visuals can confuse your audience and obscure your message. This article explores best practices for data visualisation, explains when to use popular chart types like pie charts, bar charts, line charts, and scatter plots, and offers practical tips for making your data stories impactful and easy to understand.

Why Data Visualisation Matters

Humans are visual creatures. Our brains process visual information far faster than text or numbers, allowing us to spot patterns, outliers, and relationships with ease. Effective data visualisation leverages this natural ability, making data-driven insights more accessible and actionable. Good visualisations help audiences:

• Grasp high-level trends quickly
• Compare categories or values easily
• Identify relationships or outliers
• Make informed decisions based on evidence

But achieving these outcomes requires more than just picking a chart at random. It demands thoughtful design, a clear understanding of your audience, and a careful choice of chart type.

General Principles for Effective Data Visualisation

Before diving into specific chart types, keep these foundational principles in mind:

• Know your audience: Tailor your visualisation to the knowledge level and interests of your viewers. What is obvious to a data scientist may not be to a business executive or the general public.
• Clarify your message: Decide what story you want your data to tell. Every visual should have a clear takeaway.
• Keep it simple: Avoid clutter, unnecessary elements, and “chartjunk.” Simplicity aids comprehension.
• Use colour and size purposefully: Colour should highlight or differentiate, not distract. Size can indicate value or importance.
• Be consistent: Use predictable layouts, colour schemes, and labels to help viewers navigate your data story.
• Make it accessible: Ensure your visuals are readable by people with colour vision deficiencies and accessible on different devices.

Choosing the Right Chart: When to Use What

1. Pie Charts

Best for: Showing proportions or parts of a whole.

When to use:

• When you have a small number of categories (ideally 3–5).
• When you want to show how each category contributes to a total.

When to avoid:

• When you have many categories or very small differences between them.
• When precise comparison between categories is important.

Tips:

• Always label slices clearly.
• Avoid 3D effects—they distort perception.
• Don’t use multiple pie charts for comparison; use a bar chart instead for easier comparison.

Example:
Displaying the percentage breakdown of a company’s revenue by product line.

2. Bar Charts

Best for: Comparing quantities across categories.

When to use:

• When you want to compare values between discrete groups (e.g., sales by region, survey responses).
• When categories are not naturally ordered.

Types:

• Vertical bar chart (column chart): Good for a small number of categories.
• Horizontal bar chart: Useful when category names are long or there are many categories.

Tips:

• Start the axis at zero to avoid misleading the viewer.
• Sort bars logically (e.g., descending order, alphabetical).
• Use colour to highlight key categories, but avoid overuse.

Example:
Comparing the number of votes received by different candidates in an election.

3. Line Charts

Best for: Showing trends over time.

When to use:

• When you want to display data points in chronological order (e.g., monthly sales, temperature changes).
• When you want to show how one or more variables change over a continuous interval.

Tips:

• Use a consistent time interval (e.g., days, months, years).
• Limit the number of lines to avoid clutter.
• Use markers or labels for clarity if lines cross.

Example:
Tracking website traffic over the course of a year.

4. Scatter Plots

Best for: Showing relationships or correlations between two quantitative variables.

When to use:

• When you want to explore potential associations (e.g., height vs. weight, advertising spend vs. sales).
• When you want to identify clusters, trends, or outliers.

Tips:

• Use colour or shape to indicate categories or groups.
• Add a trend line if you want to highlight correlation.
• Avoid overlapping points by using transparency or jittering.

Example:
Examining the relationship between study hours and exam scores among students.

5. Bubble Charts

Best for: Displaying relationships between three variables.

When to use:

• When you want to show two quantitative variables (position on x and y axes) and a third variable as bubble size.

Tips:

• Don’t use too many bubbles—visuals can get crowded.
• Label key bubbles or provide tooltips for clarity.
• Use colour to add a fourth variable, but be careful not to overload the chart.

Example:
Comparing countries by GDP (x-axis), life expectancy (y-axis), and population (bubble size).

6. Treemaps

Best for: Showing proportions within a hierarchy.

When to use:

• When you want to visualise parts of a whole, especially when categories are nested or hierarchical.

Tips:

• Use colour and size to indicate different levels or values.
• Keep labels readable; consider interactive tooltips for detailed information.

Example:
Visualising a company’s revenue by division and product line.

7. Heatmaps

Best for: Displaying magnitude of values across two dimensions.

When to use:

• When you want to show patterns, concentrations, or anomalies in large data sets (e.g., website click maps, correlation matrices).

Tips:

• Use a clear and intuitive colour scale.
• Avoid using red-green combinations for accessibility.
• Provide a legend for colour interpretation.

Example:
Showing the correlation between different variables in a dataset.

Design Tips for Telling Effective Data Stories

1. Use Visual Hierarchy

Guide your audience’s attention by making the most important information stand out. Use size, bold fonts, or colour highlights to create a clear path through your visual.

2. Incorporate Context

Provide enough context so viewers understand what they’re seeing. This includes clear titles, axis labels, legends, and source notes.

3. Avoid Distortion

Don’t manipulate axes, proportions, or colours in ways that mislead. Always start bar and line chart axes at zero unless there’s a compelling reason not to (and then, clearly note it).

4. Make It Interactive (When Possible)

Interactive elements like tooltips, filters, and drill-downs help users explore data and discover insights relevant to them. This is especially useful for dashboards or web-based visualisations.

5. Test for Accessibility

Ensure your visuals are readable by people with colour vision deficiencies and accessible on various devices. Use high-contrast colour schemes, patterns, and test with accessibility tools.

Common Pitfalls and How to Avoid Them

• Overusing Pie Charts: Pie charts are often misused. If you need to compare more than a few categories or show small differences, use a bar chart instead.
• Cluttered Visuals: Too many colours, shapes, or data points can overwhelm viewers. Simplify and focus on what matters most.
• Missing Labels or Legends: Always label axes, data points, and include a legend if necessary.
• Ignoring Audience Needs: Visuals should be tailored to your audience’s expertise and interests.
• Inconsistent Scales: Use consistent scales across charts to avoid confusion.

Summary Table: When to Use Popular Chart Types

Conclusion

Great data stories are built on clear, purposeful visualisations. By choosing the right chart for your data, keeping your designs simple and accessible, and focusing on your audience’s needs, you can turn raw numbers into insights that inspire action. Whether you’re using a classic pie chart or a complex heatmap, remember: the best visualisation is the one that makes your data’s story impossible to miss.