The document introduces data visualization, emphasizing its role in making complex data understandable and engaging. It outlines effective visualization practices and principles, stressing the importance of clarity, context, and unbiased representation of data. The author encourages the use of storytelling techniques and interaction to enhance user experience and insight generation.

1. Data Visualisation
An introduction to the art & science…
Ben Logan
@VisualVolumes

2. Definition?
• Still, after almost a decade of being popularised, under
discussion…
• Here is a definition that is broadly agreed upon within the
community;
• Based on (non-visual) data.
• Produce an image.
• The result must be readable and recognisable.
• https://eagereyes.org/criticism/definition-of-visualization

3. Aim?
• Allow the user to draw meaning from large, unwieldy,
data sets.
• Speed - rapid interpretation of the data.
• Depth - interpretation at many levels.
• Insight - genuine discovery of information.
• Recall - images are easier to remember.
• Engagement - encourage interaction and discovery.

4. – David McCandless
“In an endless jungle of websites with text-
based content, a beautiful image with a lot of
space and colour can be like walking into a
clearing. It's a relief.”

5. – Edward Tufte
“The minimum we should hope for with any
display technology is that it should do no
harm.”

6. In the field of Data Visualisation there is a
growing idea that you need to fall into one of
these two camps;
• Scientific and evidence based - Tufte
• Fun, bold and colourful - McCandless
You don’t - you need to be in both…

7. Bad Examples

10. Good Examples

12. Why?
• Why is it so important?
• “Big Data”
• Impatience - people just don’t look at your data!
• let’s go through a quick example…

13. Before
• Tell me about the distribution of earthquakes across the
globe in the first month of 2015?
• The USGS ATOM data file for the last 24 hrs is over 200
lines long;

14. After
• Now take a look at the visualisation and see if you feel
more able to answer the question?
• http://fathom.info/quakes/

15. How? In Theory
• Use traditional story telling techniques - a
beginning, middle and end. It works!
• Photography. Proven techniques, e.g. blur.
• Standard patterns!
• Elements from the natural world (blue is sea, not
land!).

16. How? In Practice
• Excel
• Tableau
• D3. Really? Is it interactive?
• Static - Photoshop
• Does it matter?

17. Deep Dive
• Let’s take a look at an example in a bit more
detail…
• “Male vs Female membership of the UK
Parliament”
• You kind of already know the answer before I show
you?
• This is a common weakness in many visualisations
- they aren’t showing you anything new!

19. What do you notice?
• It was definitely a leading question. The author had a
clear agenda - to highlight discrimination against
women.
• What about the context? How many women actually
stood for election?
• We are implying that people aren’t voting for women,
but we aren’t backing that up with evidence.
• This is only part of the picture. Don’t leave your users
with more questions than answers!

20. • In the context of UK Politics there is heavy colour
bias, so you need to consider that in your design.
• Detail - I expect to see the differences between
political parties.
• If we are showing scale accurately, I would have
matched either the X or Y.
• You can read more about this case study on Visual
Volumes, where it is fully deconstructed;
• https://visualvolumes.wordpress.com/2015/05/21/
houses-of-parliament/

21. Key Lessons
• What’s the story? What are you really trying to tell people,
or hope that they discover themselves?
• Visualisations can and should be powerful - they should
prompt a debate and discussion.
• They should also form a level playing field for that debate,
with no obvious bias.
• Don’t take sides and don’t choose a leading question - let
the user explore the data and draw their own conclusions.
• Make it as easy as possible to reach those conclusions.

22. • Try to paint a complete picture. This is not always
easy, but at least be honest about the gaps in your
data.
• Don’t leave them wanting more, or more confused
than they were to begin with.
• Always disclose your data source.
• Be careful to not distort or miss-represent the data
(e.g. uneven scales).

23. • Focus on giving the user the ability to digest and
interpret the numbers, not the medium you use to
visualise the numbers.
• User experience and good design is essential.
• Be wary of information overload.
• Remember your goal and the original story and try
to tie your user back to that - stay focussed.

24. – Bill Gates
“Content is King.”

25. – Maya Angelou
“Content is of great importance, but we must
not underestimate the value of style.”

26. Ben Logan
• CV and visualisation portfolio online…
• http://www.benlogan.co.uk
• @VisualVolumes
• https://visualvolumes.wordpress.com