According to the latest estimates, 402.74 million terabytes of data are created daily.

Never before has so much information been generated and consumed. We live in a hyperconnected world where every click, interaction, or decision feeds a data stream. From social networks to smart devices and financial transactions to IoT sensors, we are surrounded by data that grows exponentially daily.

On the one hand, the abundance of data can be overwhelming, making it difficult to extract valuable insights. On the other hand, it has invaluable potential to transform industries, drive innovation, and solve complex problems. This is where data visualisation comes in. It is a combination of science and art that transforms complex information into clear, accessible representations. When used correctly, it facilitates decision-making, tells stories, and reveals decisive insights.

This article explores the essential elements of building visualisations and highlights how to create efficient and meaningful reports.

Know your target audience

The first step in any reporting project is understanding who will consume the information. The audience directly influences the design and content of the visualisations.

Personalisation and Relevance

• Adapt the language and graphics to the audience’s level of knowledge: A data science expert may require more technical depth, while managers need more summarised insights.
• Choose information that matters: Avoid unnecessary graphics or irrelevant data distracting from the central point.

Commitment and Feedback

• Use attention-grabbing visual elements, such as icons or bright colours, to create an emotional connection with the audience.
• Request constant feedback and implement improvements that meet the needs of end users.

Understanding the audience’s preferences and expectations helps build a solid foundation for creating powerful and relevant visualisations.

Choosing Relevant Information

One of the biggest challenges in building a report is identifying the most relevant data. A careful and detailed requirements assessment avoids excessive information that can confuse rather than clarify.

Metrics and KPIs

• Identify the key metrics aligned with the project’s objectives.
• Focus on simplicity without sacrificing detail: reduce redundant details and highlight important trends or patterns.

Navigation Planning

• Structure report navigation logically and intuitively. For example, start with overviews before drilling down to specific information.
• Maintain consistency in design so that the end user feels comfortable exploring the content.

Avoiding information overload and structuring straightforward navigation transforms complex graphics into accessible and usable insights.

Maintaining Visual Clarity

A clean and organised layout makes interpretation quick and efficient.

• Highlight key elements:Use contrasting colours, larger sizes or visual hierarchy to emphasise key points.
• Avoid visual pollution: Limit decorative elements that don’t add value.
• Consistency in design: Use the same colour palette, typography and graphic style throughout the project.

Maintaining visual clarity helps convey complex messages simply, which is essential for engaging the audience.

Choosing the right Visual

Not all visuals are suitable for all types of analysis. Choosing the right type of visualisation depends on several factors:

• Purpose of analysis: Bar charts are ideal for comparing categories, but line charts are better for visualising trends.
• Type of data: Continuous data can best be represented with scatter plots, while categorical data is better suited to circle or column diagrams.
• Audience: Adapt the visuals so that they are intuitive and understandable.

To find the most effective representation, performing iterative tests with different designs is a good practice.

Filters, Scales and Labels

Well-implemented filters, scales and labels can turn static visualisations into interactive experiences.

Provide options for the end user to explore the data in a personalised way:

• Position the filters in a consistent and easily accessible way throughout the report.
• Use relevant attributes to segment information.

Suitable scales

• Choose appropriate scales to avoid misinterpretation of the data.
• Reduce decimal places and use abbreviations to improve readability.

Subtitles and Labels

• Use clear and concise descriptions.
• Position labels strategically to avoid overlapping or confusion.

These strategies help with navigation and ensure that data is presented in an intuitive and clear way.

Highlight Insights and Contextualise

A good visual goes beyond numbers; it tells a story. It’s good practice to use annotations, icons and/or tooltips to highlight important information and provide context.

• Add short explanatory texts to simplify complex concepts.
• Create a contextual introduction: Explain how to navigate the Reports and what the main KPIs are

The strategic use of contextualising elements keeps the end-user involved and informed.

Test the Interpretation

Testing the interpretation of a visualisation is essential. Feedback should be sought constantly, and open questions should be asked, such as:

• Is the visual clear and easy to understand?
• Does the data presented correspond to the proposed objectives?
• Are there any elements that could be simplified or optimised?

Comparing the initial design/requirement with the end user’s interpretation is a very relevant practice for guaranteeing the quality of a delivery.

Data visualisation is both a science and a creative expression.

By following good practices—such as knowing your audience, selecting relevant information, maintaining visual clarity, and testing iteratively—you can turn raw data into essential decision-making tools.