Adobe Summit - Data Storytelling

© 2016 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.© 2015 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.
Data Storytelling Chris Haleua | Sr. Product Marketing Manager | Adobe
Eduard Latour | Marketing Insights Manager | Lego

3
Chris Haleua
Product Marketing Manager
chaleua@adobe.com
Edward Latour
Marketing Insights
Manager
Edward.Latour@LEGO.co
m

Summary
4
1 | The Purpose of Data Storytelling
2 | The Psychology of Data Storytelling
3 | Data: The Foundation of Your Data Story
4 | Narrative: The Structure of Your Data Story
5 | Visuals: The Key Scenes of Your Data Story
Data
Narrative Visuals
Engage
CHANGE

Numbers have an important story to tell.
They rely on you to give them
a clear and convincing voice
Stephen Few

© 2016 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.© 2016 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential.6
value from data by
processing it,
visualizing it, and
communicating it is
going to be an
essential skill in the
next decade." Hal Varian
Chief Economist
Google

The
Psychology of
Storytelling

© 2016 Adobe Systems Incorporated. All Rights Reserved. Adobe Confidential. 8
Inform or
tell a story?

Ignaz Semmelweis
1818-1865
True
Valuable
Actionable
Adopted
Childbed Fever Mortality:
10% with doctors vs. 4% with midwives

Childbed Fever Mortality Rates
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
20%
J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M
1846 1847 1848 1849
Handwashing policy
introduced mid-May
Strict controls
enforced on
negligent students
Semmelweis
dismissed
12%
4%
5%
82%
2%
0% 0%

MAIN
POINT
EXPLAN.
FOCUS
LINEAR
SEQ.
NARR.
ELEMENTS VISUALS
Data Storytelling Often Often Often OftenYes
Curated Reports &
Dashboards Often Often Often YesMaybe
Infographics Often Maybe Maybe YesMaybe
Data Visualizations Maybe Rarely No YesMaybe
Automated Reports No No No OftenNo
Automated Dashboards No No No YesNo
Not All Delivery Tools Are Created Equal
Effort
Required

How Much Effort Should You Invest in Data Storytelling?
Expected
Simple
Inexpensive
Intuitive
Disruptive
Complex
Costly
Counterintuitive
Insight
Type
Low
Value
High
ValueBusiness Impact
Story Zone

Stories Beat Statistics
More Memorable More Persuasive
5% vs. 60%statistics stories
$1.40 vs. $2.40statistics stories

Analytics Path to Value

side of reason, but they help us
to reach decisions
Dr. Antonio Damasio

Data
LOGIC EMOTION
Why Data Stories?
Story

We hear statistics,
but we feel stories

DATA
Shields Up
When we read
dry, factual
arguments, we
read with our
dukes up. We
are critical and
Stories Have a Unique Effect on Audiences

Stories Have a Unique Effect on Audiences
DATA
STORY
Shields Down
absorbed in a story
we drop our
intellectual guard.
We are moved
emotionally and
this seems to leave
Jonathan Gottschall
Author, The Storytelling Animal
Shields Up

Narrative Visuals3 Keys to Data
Storytelling
Data

Data
Narrative VisualsExplain:
Narrative + Data

Narrative
Data
Enlighten:
Data + Visuals
Visuals

Narrative
Data
Engage:
Narrative + Visuals
Engage
Visuals

Data
Narrative Visuals
Engage
CHANGE
Influence Change
With Data Stories

MAIN
POINT
Five Essential Elements of a Data Story
have a point! It makes
it so much more
interesting for the

MAIN
POINT
EXPLANATORY
FOCUS
Descriptive Explanatory
Describe
To represent or give an
account of in words or
pictures
Who?
What?
When
?
WHY?
HOW?
Explain
To make plain or clear;
render understandable or
intelligible

MAIN
POINT
EXPLANATORY
FOCUS
LINEAR
SEQUENCE
Story
An account of a
connected series
of events.

MAIN
POINT
EXPLANATORY
FOCUS
LINEAR
SEQUENCE
NARRATIVE
ELEMENTS

What Do We Learn about Carl & Ellie?
Dream
Vacation
Diverse
Backgrounds
Do-It-
Yourselfers
Childhood
Romance
Different
Styles
Work Together
at Zoo
Renovated
Dream Home
Wanted a
Family
Unable to
Have Kids
Financial
Setbacks
Bucket List Grieving
Widower
Still
Romantic
Grow Old
Together

MAIN
POINT
EXPLANATORY
FOCUS
LINEAR
SEQUENCE
NARRATIVE
ELEMENTS
VISUALS
lady screamed.
Bring her on and
Mark Twain

Customer Journey
We were lost &
confused
on shop.LEGO.com

Navigation Confusion
34
?
40% Exit10%
Ninjago
22% Global
Navigation

70% Lower Satisfaction Projected Loss: $8 Million
40% Exit10%
Ninjago
22% Global
Navigation

70% Lower Satisfaction Projected Loss: $8 Million
40% Exit10%
Ninjago
22% Global
Navigation
NARRATIVE
ELEMENTS
EXPLANATORY
FOCUS
VISUALS
LINEAR
SEQUENCE
MAIN
POINT

DATA

Data
Narrative Visuals
Engage
CHANGE
Data is the Foundation of Your Data Story

The Two Sides of Indiana Jones
Field Archaeologist Professor

Exploratory Explanatory
Flexibility & speed are critical.
I am the audience.
I know the data.
Clarity is critical.
I am NOT the audience.
I have a story to tell.

What I look at

Context
Columbus, Ohio
Orders: 33% YoY
Email Traffic: 12%
Email Orders: 6%
SEM Traffic: 8%
SEM Orders: 39%

Data
Director s
Cuts
Data
Data
Storytelling Insight
Explore Explain
43%
Audience
Data
Explore
Insight Audience
Data
Cameos
Select
Data Story
Support
Audience
98%
Different Processes Shape Data That Is Shared

NARRATIVE

Data
Narrative Visuals
Engage
CHANGE
Narrative Connects Insights and Adds Emotion

Right Story for the Right Audience
Who is the right audience for my data story?
How do I adjust my data story to my audience?

Why
Should I
Care?

How Well Do You Know Your Audience?
Key business goals &
personal priorities?1
Specific needs &
questions?
Attitudes, beliefs, biases,
& preferences?
Topic familiarity?
How data-savvy?
Seniority level?
2
3
4
5
6
Ensure data stories are relevant
and meaningful
Anticipate what they expect to
hear or learn
Less familiar More context &
conceptual
Less savvy Less detail & jargon
More senior Lead with
high-level summary
Considerations
Recognize negative views or
conflicting opinions
Audience Attributes

Edward Tufte
boring, you ve got
the wrong

Structure Your Analysis Findings As A Story

into the business
are expanded
Beginning Middle End
Solution
& Next Steps
Share
recommendations
and discuss next
steps
Rising Insights
Share findings that
reveal deeper insights
into the problem or
opportunity
Aha Moment
Present major finding
or key insight
Set-up
Background
on current
situation,
character(s),
and the hook
Structure Your Analysis Findings as a Story
Gustav Freytag
(1816-1895)

How to Build Your Story
Aha Moment
Present major finding
or key insight
Set-up
Background on
current situation,
character(s), and
the hook
Rising Insights
Share findings that
reveal deeper insights
into the problem or
opportunity
Solution
& Next Steps
Share recommendations
and discuss next steps

How Does This Structure Compare to Other Approaches?
Past Present Future
Set-Up
Rising
Insights
Aha
Moment
Solution &
Next Steps
Problem Solution Benefit
What So What What Now

Start by Storyboarding Your
Key Data Points
Insight
#1
Insight
#2
Insight
#7
Insight
#8
Insight
#5
Insight
#4
Insight
#6

Organize Your Insights
Insight
#1
Insight
#2
Insight
#7
Insight
#8
Insight
#5
Insight
#4
Insight
#6
*Aha Moment

Find Your Starting Point
Insight
#1
Insight
#2
Insight
#7
Insight
#8
Insight
#5
Insight
#4
Insight
#6
*Aha Moment

Edit Your Story
Insight
#1
Insight
#2
Insight
#7
Insight
#8
Insight
#5
Insight
#4
Insight
#6Insight
#9
*Aha Moment
Rising Insights

Have Your Data Story Drive Action
Insight
#1
Insight
#2
Insight
#7
Insight
#8 Insight
#4Insight
#9
*Aha Moment
Insight
#10
#1
#2
#3
#4
Rising Insights
Recommendations

Humanize Your Insights
59
Who is your

How to Insert Heroes Into Your Data Story
Determine which
user segment
matters to your
story
Build a rich,
data-driven
profile of
your hero
Give your
hero a face
Give your
hero a voice
Show their
digital journey
Qualitative dataStock photos Screenshots
54321

VISUALS

Data
Narrative Visuals
Engage
CHANGE
Visuals Can Simplify and Explain the Complex

What Patterns Do You Notice in This Table?
I II III IV
x y x y x y x y
10 8.04 10 9.14 10 7.46 8 6.58
8 6.95 8 8.14 8 6.77 8 5.76
13 7.58 13 8.74 13 12.74 8 7.71
9 8.81 9 8.77 9 7.11 8 8.84
11 8.33 11 9.26 11 7.81 8 8.47
14 9.96 14 8.1 14 8.84 8 7.04
6 7.24 6 6.13 6 6.08 8 5.25
4 4.26 4 3.1 4 5.39 19 12.5
12 10.84 12 9.13 12 8.15 8 5.56
7 4.82 7 7.26 7 6.42 8 7.91
5 5.68 5 4.74 5 5.73 8 6.89

Anscombe's Quartet
2
4
6
8
10
12
14
2 4 6 8 10 12 14 16
y1
x1
2
4
6
8
10
12
14
2 4 6 8 10 12 14 16
y2
x2
2
4
6
8
10
12
14
2 4 6 8 10 12 14 16
y3
x3
2
4
6
8
10
12
14
2 4 6 8 10 12 14 16 18 20
y4
x4
I II III IV

Two Cognitive Processing Systems
Lazy
Controller
Pattern-seeking
& Heuristics
Autopilot Pilot
System 2
 Slow
 Analytical
 Logical
 Controlled
 Effortful
System 1
 Fast
 Intuitive
 Emotional
 Automatic
 Effortless

Exercise: System 1 and 2 in Action
A bat and ball cost $1.10.
The bat goes for one dollar more than the ball.
How much does the ball cost?
$1.00
Ball
Bat
$0.10
$0.90

Exercise: System 1 and 2 in Action
A bat and ball cost $1.10.
The bat goes for one dollar more than the ball.
How much does the ball cost?
$1.05
Ball
Bat
$0.05
$1.00

Data Storytelling Frequently Involves Comparisons
Product A Product B
Time Period X Time Period Y
Segment 1 Segment 2

-
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
-
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
Jan Feb Mar April May June July Aug Sept Oct Nov Dec
What Data Conveys Your Insights Effectively?
New Visitors Orders
New Visitors
Orders

20%
25%
30%
35%
40%
45%
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
7,000,000
1 2 3 4 5 6 7 8 9 10 11 12
% of Orders
Ensure You Have the Right Data
New Visitors % of Orders

The Right Data Includes Insightful Context
0%
10%
20%
30%
40%
50%
60%
70%
80%
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
7,000,000
Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec
New Visitors % Traffic
% of Orders
New Visitors

23%
12%
26% 7%
12%
18%
Classic
LEGO
Ideas
Color Can Highlight What Is Important
Q1 2014
7,000
Units on
Backorder
Color change signifies a
change in information
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
20000
Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9
LEGO Functions
Creator
Mixels
Ninjago

Gestalt Principles Govern How We Interpret Content
EnclosureProximity
Connection Similarity

Pre-Attentive Attributes Signal Similarities & Differences
Color Orientation Size Intensity
Position &
Alignment Shape
Motion Line Length Line Width Added MarksEnclosureCurvature

Not All Attributes Are Equal
Which attribute
is stronger?
Which attribute
is stronger now?
How about
now?

Many Different Chart Options
Comparisons
Vertical Bar Stacked
Vertical Bar
Horizontal
Bar
Stacked
Horizontal
Bar
Table Heatmap
Trends
Vertical Bar
Line
Stacked Area
Slopegraph
Parts-to-Whole
Vertical Bar Horizontal
Bar
Pie Chart
& Donut
Stacked
Bar
100% Stacked
Bar
Waterfall
Relationships
Scatterplot
Bubble

Graphical Methods Vary in Effectiveness
More accurate
comparisons
More generic
comparisons
2D position along
common but
unaligned scales
Length Angle Volume Shading
Direction Area Curvature Color Hue
2D position along
common, aligned
scale
Graphical Perception: Theory, Experimentation, and
Application to the Development of Graphical Methods
(Cleveland & McGill, 1984) via The Functional Art
(Alberto Cairo, 2013)

Signal
Noise

Direct vs. Indirect Data Delivery
DIRECT
32%
INDIRECT
Flexibility
& Control
Detail
(Text)
Two-way
communication
One-way
communication
HYBRID:

80-90%
Wasted

Signal Amplification
Identify the right data1
Choose matching visualizations2
Calibrate visuals to message3
Remove unnecessary noise4
Highlight what is important5
Make interpretation easy6

Noise Sources
Ignoring audience priorities1
Using unfamiliar jargon2
Providing too much detail3
Giving in to one-sided talking4
Leaving out essential context5
Forgetting impact projections6

Brain Facts
Multitasking is a myth
Patterns over details
10-minute attention span
Vision is the dominant sense
Suspense sharpens focus
Memory has finite capacity

Chart Staging
1. Select entire chart
2. Create a copy
3.
4. Ungroup chart
5. Convert to Drawing object
6. Add animations to parts of the
chart
Donut
& Pie
Line
Bar &
Column
Scatterplot
Bitmap

Good Analogies
1. Can your audience relate to the analogy?
2. Does your analogy clarify your concept?
3. Is your analogy short and simple?
4. Is your analogy boring?
5. Can you use strong visual images with your analogy?
6.
0101101011

What Will You Do With Your Truth?
Data
Narrative Visuals
Engage
CHANGE
Those who tell
the stories rule
Hopi Indian Proverb

Q&A
89
Chris Haleua
chaleua@adobe.com
@chrishaleua
Edward Latour
Edward.Latour@LEGO.com
linkedin.com/in/edwardlatour
@edward_latour

Adobe Summit - Data Storytelling

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