A visualization is worth a million data points

Nobody thinks in words or numbers. We think in images because we are all visual beings

More than 70% of all the sensory receptors in the body are in the eyes

Evolution has transformed our brains to identify patterns

Data is growing bigger

Computers are getting faster

Analyses are becoming more complex

The results from the insights found can no longer be explained with simple charts

Gain Chart

Parallel Coordinates

Heatmap

Streamgraph

When done right

Intuitive to comprehend the whole picture

View both simple and complex data

See abnormalities, dependencies and trends not apparent from tables

January

April

July

October

What are the usual pitfalls?

Unclear visual encodings
Too many variables
Too many data points

Too much information at once

What

can we do?

Storytelling

with

In listening to stories we tend to suspend disbelief in order to be entertained

Whereas in evaluating statistics we generally have an opposite inclination to suspend belief in order not to be beguiled

John Allen Paulos

Stories match the way the human brain understands information

You cannot avoid telling stories to yourself

The Sweet Spot

Stories are | emotional
Stories are | memorable
Stories are | impactful

Structuring your

data story

Structure of a Story

Define your audience

Set the scene

Introduce the characters

Create tension

Provide resolution

Define your Audience

Novice

Is new to the subject but doesn't want oversimplification

Manager

Seeks in-depth, actionable understanding of intricacies with access to detail

Expert

Wants more exploration and discovery and less business

Executive

Needs to know the significance and conclusions

Don’t be your own audience

(you already know the story)

Set the Scene

Contextualize by giving the necessary introduction, what will the audience be looking at, before showing any data

Make it clear what to look at, especially where to start looking

Keep in mind that stories have a beginning, middle, and end

Introduce the Characters

Explain gradually what each visual encoding means, e.g.

The essence of a data visualization is converting data into visual elements - bars, lines, points, colors, etc.

Colors

Shapes

Axes

Add meaningful annotation 

(don’t just label)

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Create Tension

Limit complexity at first by gradually exposing your data

Unfold it, don't dump

Reveal as needed

Start by showing one interesting datapoint or insight and explain this in full detail

Provide Resolution

Inviting readers to draw their own conclusions is risky because even simple data sets can convey different messages

In the end give the audience either

Solid Conclusion

Platform where the audience can  explore the data themselves

Wrapping

things up

Why do data stories work?

Unclear visual encodings
Too many data points
Too many variables

Gradual introduction of information

Explicit visual encodings
Gradual introduction of data points
Independent introduction of variables

Storytelling will bring

Engagement

Focus

Memorability

For lasting effect you need to persuade the rational brain

But also resonate with the emotional brain

Data tell Stories

Measles heatmap: http://graphics.wsj.com/infectious-diseases-and-vaccines/

Gain chart: http://sten.tamkivi.com/2013/05/week-37-small-world-churn-measuring-sales-startup-hr/

Streamgraph: http://antheawhittle.com/post/336352214/this-stream-graph-visualisation-shows-your

Parallel Coordinates: http://stackoverflow.com/questions/19213961/parallel-coordinates-program-written-with-processing-cant-show-anything-in-mac

SOM heatmap: http://www.visualcinnamon.com/portfolio/heatmaps

Facebook Tree: http://www.onformative.com/work/4010-facebook-tree/

Flickr Flow: http://hint.fm/projects/flickr/

The color of paintings: http://blog.martinbellander.com/post/115411125748/the-colors-of-paintings-blue-is-the-new-orange

Buildings in the Netherlands: http://code.waag.org/buildings/

Genome similarities: http://www.swissinfographics.com/archives/308

Data Sweetspot: Cannot remember, sorry

Brazil's demographic opportunity: by Alberto Cairo from The Functional Art

Driving shifts into reverse: http://www.nytimes.com/2010/05/02/business/02metrics.html?_r=0

Phone brand Switching: http://www.visualcinnamon.com/portfolio/phone-brand-switching

U.S. Gun Deaths: http://guns.periscopic.com/?year=2013

Apple stock: http://www.nickdiakopoulos.com/2013/09/17/storytelling-with-data-visualization-context-is-king/

Out of Sight, Out of Mind: http://drones.pitchinteractive.com/

Urbanization in East Asia: http://www.visualcinnamon.com/portfolio/urbanization

Brain visualization: http://nxxcxx.github.io/Neural-Network/

General 5 step concept based upon and inspired by Michael Freeman's talk at Strata: http://mfviz.com/strata

Visualization examples:

Blue is the new Orange

Pie charts are bad

(there is always a better option)

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3D is not effective

(don't assume that just because a program offers a certain chart, that it's a good choice)

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