Data visualisation transforms data and facts into clear and engaging visual content. The purpose of data visualisation is to simplify complex information, support communication, and improve comprehension and retention.

General rules

Appearance

Data visualisations follow the same visual principles as the illustration system. Graphics are designed in a clean, two-dimensional style, incorporating corporate colours and typography. Chart elements are generally displayed without outlines, while illustrative elements may use outlines where required. For further guidance, refer to the Instructional Graphics section.

Simplicity

Visualisations should focus on essential information only. All unnecessary elements, decorative effects, and redundant details should be removed to maximise clarity and readability.

Scale

Scales should be reduced to the level of detail necessary to communicate the information accurately. Avoid excessive subdivisions that may distract from the key message.

Legend

Legends must clearly label all relevant categories, values and visual elements. Legends should be separated from the main graphic by a fine dividing line to ensure a clear visual hierarchy and easy navigation.

Workflows visualise processes, procedures and decision paths as clear, step-by-step sequences. They communicate complex information in an intuitive and structured way, enabling users to quickly understand individual steps in a process and how they are related to each other.

Depending on the content, workflows may combine arrows, connectors, icons, illustrations, product images and text. Corporate colours can be used to create a visual hierarchy, highlight key steps and ensure consistency with the overall design system. The choice of shapes and connectors can vary according to process complexity, provided a clear reading direction and logical flow are maintained.

For more visual and presentation-oriented workflows, image motifs and illustrations can be used to increase engagement and improve comprehension. Technical or scientific workflows may also include product images, laboratory equipment, data visualisations or process indicators.

In certain cases, workflows may be embedded directly within body content as supporting explanatory graphics, rather than serving as standalone process diagrams. In these instances, standard copy text sizes should be used to ensure seamless integration with the surrounding content.

Bar charts are used to compare discrete values across different categories. They can be displayed either vertically or horizontally, depending on the available space and how complex the data is.

To ensure readability and visual consistency, all bars in a chart should be the same width. As a general guideline, use a bar width of 4 mm for charts with many categories, and 8 mm for charts with a small number of categories. Maintain adequate spacing between bars to create a clear visual hierarchy and facilitate the comparison of values.

Corporate colours should be used consistently to distinguish between categories, highlight key findings, and maintain alignment with the overall visual system.

Curve diagrams are used to visualise trends, distributions and changes over continuous intervals of time. They enable users to identify patterns, developments and concentrations of values at a glance.

To ensure clarity and readability, curves should be displayed with a clear visual hierarchy, and unnecessary details should be avoided. Corporate colours can be used to distinguish between datasets, highlight key findings, and emphasise areas of interest. Where multiple curves are displayed, sufficient contrast and spacing should be maintained to allow for easy comparison.

The axes, scales and labels should be kept as simple as possible to allow the viewer to focus on the overall trend and the most relevant insights rather than on individual data points.

Line graphs are used to visualise trends, developments and changes over time or across a continuous scale. They are particularly effective when the focus is on patterns, dynamics, and relationships rather than individual values or categorical comparisons.

To ensure clarity, only the data necessary to communicate the key message should be displayed. Corporate colours should be used consistently to distinguish between datasets and highlight important developments. The axes, labels and grid lines should be kept as simple as possible to maintain the focus on the overall trend and data progression.

To ensure clarity and readability, curves should be displayed with a clear visual hierarchy, and unnecessary details should be avoided. Corporate colours can be used to distinguish between datasets, highlight key findings, and emphasise areas of interest. Where multiple curves are displayed, sufficient contrast and spacing should be maintained to allow for easy comparison.

The axes, scales and labels should be kept as simple as possible to allow the viewer to focus on the overall trend and the most relevant insights rather than on individual data points.

Cluster plots are used to segment and explore large datasets by grouping related data points according to their similarities. They help reveal patterns, relationships and outliers, making complex data easier to interpret.

Corporate colours should be used consistently to differentiate clusters, create visual hierarchy and highlight key insights. All labels, legends and annotations must follow the defined corporate typography to ensure consistency and readability across all applications. To maximise clarity, supporting elements should be limited to the information necessary for understanding the overall data structure.