An introduction to the art & science of Data Visualisation. A whistle-stop tour, with some bad examples and some good examples. Key lessons and a case study (deep dive).

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