Principles of Data Visualisation Seminar
- 2. Outline Know Your Audience Determine the Best Visuals Show the Data K.I.S.S Colour Selection Balance Unveil Connections Intuitiveness Hierarchy Accuracy Data Ethics Storytelling Test and Refine 2
- 3. Know The Audience The first step to create a successful data visualisation is to understand your audience. Who are they? What are their goals, needs and expectations? What level of detail and complexity can they handle? How will they use the information you provide? By answering these questions, you can tailor your data visualisation to suit your audience's preferences, interests and level of expertise. 3
- 4. Determine the Best Visuals There are many types of data visualisations, such as bar charts, line charts, pie charts, scatter plots, heat maps, etc. Each type has pros and cons, and can be used for different purposes and scenarios. An example, bar charts are good for comparing discrete categories, line charts are good for showing trends over time, pie charts are good for showing proportions of a whole etc. The key is to select the type of visual that best matches the data and message, and avoid using visuals that are inappropriate, misleading, or confusing. 4
- 5. Show the Data Present information in a visual format, such as charts or graphs, to make it easier to understand. This approach helps identify patterns and trends, allowing for more informed decision-making. By transforming complex numbers into engaging visuals, it captures attention and enhances comprehension for everyone. 5
- 6. Keep it Simple St**!D (K.I.S.S) This acronym K.I.S.S emphasises the importance of simplicity in presenting data. The goal is to avoid unnecessary complexity that can confuse the audience. To communicate effectively, focus on one idea at a time and avoid overwhelming visuals with excessive details. Use simple, minimal designs with clean layouts and ample white space for better readability. Stick to familiar chart types like bar graphs or pie charts that the audience can easily recognise and understand. 6
- 7. Colour Selection Colours are more than aesthetics – they convey meaning. Proper colour selection can highlight key points, differentiate categories, and evoke emotions, all of which enhance the effectiveness of visualisation. 7
- 8. Balance A balanced design features visual components such as shape, colour, negative space and texture evenly spread throughout the layout. However, this doesn't imply that the design needs to be an exact replica of another. You can achieve an asymmetrical balance by pairing larger graphs and charts with smaller elements to create an interesting contrast. 8
- 9. Unveil Connections: Visualising Relationships & Patterns Showcasing relationships and patterns within complex data, requires revealing underlying links, trends, or correlations that may not be obvious at first glance. Through the use of charts, graphs, and other visual aids, intricate connections between different variables become clearer and help to improve comprehension and make data insights more understandable and appealing. 9
- 10. Intuitiveness This refers to the ease with which viewers can understand and interpret the presented information. Effective visualisations should leverage familiar formats and designs, allowing users to quickly grasp the data's meaning without extensive explanation. By prioritising clarity and simplicity, intuitive visuals enhance user engagement. 10
- 11. Hierarchy Hierarchy in data visualisation organises and prioritises information to direct the viewer's attention. Key elements are emphasised through size, colour, and placement, ensuring that critical insights stand out. Larger or more vibrant visuals attract attention, while strategic layout follows natural reading patterns for better flow. Fonts and typography further distinguish important sections, helping guide the viewer through the data's narrative. This approach makes complex information easier to interpret and understand. 11
- 12. Accuracy Accuracy in data visualisation is crucial to ensure that the representation of data reflects true values and maintains integrity. Misleading visuals, such as distorted scales or inappropriate chart types, can lead to incorrect interpretations and decisions. By prioritising accuracy, designers foster trust in the data and empower viewers to draw valid conclusions based on reliable information. 12
- 13. Data Ethics This refers to the principles and guidelines that govern the responsible collection, use, and sharing of data, prioritising respect for individuals' privacy and rights. It emphasises the importance of transparency, accountability, and fairness in data practices to prevent misuse and discrimination. By adhering to data ethics, analysts and organisations can build trust with users and stakeholders while promoting a culture of ethical decision-making in data handling. 13
- 14. Storytelling Data Visualisation is all about crafting a narrative that resonates with the audience. Frame data within a larger story or business context. Use visuals to emphasise the most important findings or trends. Arrange visualisations in a hierarchy that builds a coherent narrative. Add explanatory text or call outs to guide the audience through the story. Where appropriate, use visuals that evoke emotion or personal connection to the data. Conclude the visualisation with clear takeaways or next steps for the audience. 14
- 15. Test and Refine Before presentation, test and refine the visualisation to ensure they are clear and effective. Ask yourself or others if the data is accurate and reliable, if the visuals are easy to interpret, and if they are engaging and memorable. Ensure that the visualisations align with the audience's goals and expectations, and that they suit the presentation format and medium. 15