Principles of Data Visualisation Seminar

Data Visualisation

1. Principles of Data
Visualisation

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
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.

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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.
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