When presenting data, the key to making it meaningful lies in how you visualise it. Data visualisation isn’t just about charts and graphs. It’s about transforming information into a compelling narrative that can inform, persuade, or drive action. Choosing the right visualisation technique is essential to convey your story clearly and effectively.

Story telling with data involves more than just selecting a chart type. It’s about turning data into insights and presenting it in a way that resonates with the audience. Whether you’re explaining trends, making comparisons, or breaking down complex points, the right visualisation helps your audience not only see the data but also understand the story behind it.

Know Your Audience

When crafting a story with data, always consider the audience. Are they experts familiar with technical jargon, or are they generalists? Their level of understanding will determine how complex or simplified your visualisations should be. Simple, straightforward visuals may be more effective for non-expert audiences, while detailed, multi-dimensional graphs can work well with technical viewers.

For example, executives might need a high-level overview, where a simple bar or pie charts are perfect. Data scientists, on the other hand, may appreciate scatter plots or heat maps, which allow them to explore deeper relationships within the data.

Choosing the Right Visualisation Technique

Here are some practical pointers to help you select the right visual tools:

• Bar Charts: Suitable for comparing different categories or showing various changes over time. They work best when you need a clear visual representation of how different categories stack up against one another.
• Line Graphs: Perfect for illustrating trends over time, line graphs show data points connected by straight lines, making them great for tracking changes and fluctuations.
• Pie Charts: Best for displaying parts of a whole, but they should be used sparingly. When there are too many categories, the message can get lost.
• Scatter Plots: These are useful for showing relationships between two variables. Scatter plots help identify patterns, correlations, or outliers in the data.
• Heat Maps: Effective for showing intensity or frequency across two variables, they work well when you want to highlight trends across a large data set.
• Histograms: These are excellent for visualising the distribution of data over a continuous interval or certain time period.

Storytelling Through Visual Hierarchy

Once you’ve chosen a visualisation type, it’s important to consider how to present the data with a visual hierarchy. Highlighting key points ensures that your audience focuses on the most important aspects of the data. You can use size, colour, and placement to emphasise the most significant points and guide your audience through the story step by step.

For instance, if you’re illustrating sales growth, you might use a bright colour to highlight a spike in sales. Alternatively, when showing a time-based trend, positioning important time periods prominently can help your audience easily follow the data narrative.

Use Interactivity Where Appropriate

Interactive visualisations can bring an added dimension to story telling with data. If your audience has access to digital presentations, consider offering filters or drill-down options that let them explore different data dimensions on their own. This not only enhances engagement but also allows users to gain insights that are personally relevant to them.

However, it’s crucial to ensure that the interactive elements don’t distract from the main narrative. They should support the story rather than overwhelm the audience with too many options.

Ultimately, the visualisation technique you choose should align with the story you’re trying to tell. Ask yourself: what do you want your audience to take away from this data? Do you want them to notice trends, or do you want them to focus on a specific comparison? Once you’re clear on the message, select the visualisation that delivers it most effectively.