Post on 24-Sep-2020
STORIES BEAT STATISTICSThe Power Of Data Storytelling
Brent Dykes | Sr. Director, Data Strategy | Domo
“To train and educate the rising generation will at all times be the first object of society, to which every other will be subordinate.”
Robert Owen (1771-1858)
“If an unfriendly foreign power had attempted to impose on America the mediocre educational performance that exists today, we might well have viewed it as an act of war.”
1983
In 1989, President George H. W. Bush set a goal for US students to be first in the world for math and science by the year 2000.
BY 2000, THE UNITED STATES WASN’T #1—NOT EVEN CLOSE0
1
2
3
4
350 400 450 500 550 600
READING
MATH
SCIENCE
USA
USA
USA
15th
18th
14th
PISA Score (0-1000)27 OECD nations
In 2002, Pres. George W. Bush set a goal for all students to be 100% proficient at grade level by 2014, including disadvantaged students.
NCLB INTRODUCED TRANSPARENCY & ACCOUNTABILITY
“No Child Left Behind . . . changed the way the American educational system collects and uses data.”
FiveThirtyEight
50 41 37 35 23 20 18 18 18 17 18
37 41 43 42 45 44 43 43 42 41 42
12 16 19 21 29 31 34 33 34 34 331 2 2 3
45 6 6 7 8 7
1990 1992 1996 2000 2003 2005 2007 2009 2011 2013 2015
48 42 39 37 32 31 29 27 27 26 29
37 37 38 38 39 39 39 39 39 38 38
13 18 20 21 23 24 25 26 26 27 252
3 4 5 5 6 7 8 8 9 8
1990 1992 1996 2000 2003 2005 2007 2009 2011 2013 2015
100% PROFICIENCY WAS NOT ACHIEVED BY 2014
Below Basic
Basic
Proficient
Advanced
MATH 4TH GRADE MATH 8TH GRADE
NCLB +7Pre-NCLB +20
40% 60%
Proficient Not Proficient
33% 67%
37%
12%
1%
50%
BUSH CLINTON BUSH OBAMA
Source: NAEP
NCLB Drove Minor Gains For Minority Students
-27-24
-21 -18
2003 2005 2007 2009 2011 2013 2015
-36 -32
-29-22
2003 2005 2007 2009 2011 2013 2015
-31
-26-29
-24
2003 2005 2007 2009 2011 2013 2015
-28
-26-27-21
2003 2005 2007 2009 2011 2013 2015
MATH SCORES
8TH GRADE
4TH GRADE
READING SCORES
White
Hispanic
Black
White
Hispanic
Black
+3 pts.
+3 pts.
+7 pts.
+4 pts.
+6 pts.
+2 pts.
+5 pts.
+5 pts.
NAEP data: Variance from white student resultsNAEP data: Variance from white student results
Source: NAEP
WHAT WAS NCLB’S IMPACT ON THE US’S PISA RANKINGS?
FROM 2000 TO 2015, THE US HAS FALLEN FURTHER BEHIND
350
400
450
500
550
600
350
400
450
500
550
600
2000 2015
350
400
450
500
550
600
350
400
450
500
550
600
2000 2015
READINGMATH SCIENCE
350
400
450
500
550
600
350
400
450
500
550
600
2000 201520152000 20152000 20152000
496497 499504
470493(18th)
(29th*)
(15th)(19th) (14th) (18th)
*2015 PISA included 34 OECD nations compared to 27 in 2000.
OECD Average
490
US STUDENTS ARE STRUGGLING WITH MATHEMATICSMATH Scores (PISA 2015)
490
470
0 100 200 300 400 500 600
Japan
Korea, Republic of
Switzerland
Canada
Finland
Denmark
Belgium
Germany
Ireland
Poland
Norway
Austria
New Zealand
Australia
Sweden
France
United Kingdom
Czech Republic
Portugal
Italy
OECD Average
Iceland
Spain
Luxembourg
Hungary
United States
Greece
Mexico
2/3 of a grade level behind
OECD Average
United States
CHILD POVERTY IS HOLDING THE UNITED STATES BACK
530
514
490
486
470455
427
0 100 200 300 400 500 600
Japan
US: Less than 10%
Korea, Republic of
Switzerland
Canada
US: 10-24.9%
Finland
Denmark
Belgium
Germany
Ireland
Poland
Norway
Austria
New Zealand
Australia
Sweden
France
United Kingdom
Czech Republic
Portugal
Italy
OECD Average
Iceland
Spain
Luxembourg
US: 25-49.9%
Hungary
United States Average
US: 50-74.9%
Greece
US: 75% or more
Mexico
US: Less than 10%
OECD Average
US: 25-49.9%
US: 50-74.9%
US: 75% or more
US: 10-24.9%
United States Average
MATH Scores (PISA 2015)
RICH
POOR
Oscar4th Grade
CHILD POVERTY IS HOLDING THE UNITED STATES BACK
530
514
490
486
470455
427
0 100 200 300 400 500 600
Japan
US: Less than 10%
Korea, Republic of
Switzerland
Canada
US: 10-24.9%
Finland
Denmark
Belgium
Germany
Ireland
Poland
Norway
Austria
New Zealand
Australia
Sweden
France
United Kingdom
Czech Republic
Portugal
Italy
OECD Average
Iceland
Spain
Luxembourg
US: 25-49.9%
Hungary
United States Average
US: 50-74.9%
Greece
US: 75% or more
Mexico
OECD Average
US: 25-49.9%
US: 50-74.9%
US: 75% or more
US: Less than 10%
US: 10-24.9%
United States Average
MATH Scores (PISA 2015)
532
530
524
521
516
514
511
490
Japan
US: Less than 10%
Korea, Republic of
Switzerland
Canada
US: 10-24.9%
Finland
OECD Average
UNITED STATES AT THE TOP…
US: Less than 10%
OECD Average
US: 10-24.9%
RICH
POOR
Oscar4th Grade
CHILD POVERTY IS HOLDING THE UNITED STATES BACK
530
514
490
486
470455
427
0 100 200 300 400 500 600
Japan
US: Less than 10%
Korea, Republic of
Switzerland
Canada
US: 10-24.9%
Finland
Denmark
Belgium
Germany
Ireland
Poland
Norway
Austria
New Zealand
Australia
Sweden
France
United Kingdom
Czech Republic
Portugal
Italy
OECD Average
Iceland
Spain
Luxembourg
US: 25-49.9%
Hungary
United States Average
US: 50-74.9%
Greece
US: 75% or more
Mexico
US: Less than 10%
US: 10-24.9%
OECD Average
US: 25-49.9%
US: 50-74.9%
US: 75% or more
United States Average
MATH Scores (PISA 2015)
490
486
486
477
470
455
454
427
408
OECD Average
Luxembourg
US: 25-49.9%
Hungary
United States Average
US: 50-74.9%
Greece
US: 75% or more
Mexico
US: 50-74.9%
US: 75% or more
United States Average
US: 25-49.9%
OECD Average
UNITED STATES AT THE BOTTOM…
RICH
POOR
Oscar4th Grade
US MUST RE-EXAMINE AND CHANGE ITS APPROACH
$0
$5,000
$10,000
$15,000
$20,000
$25,000
400 420 440 460 480 500 520 540
Education Spending per Student in 2015(US$/student)
OECD Average: $10,220
OECD Average: 490
MATH Scores (PISA 2015)
USA
ESTONIA
SOUTH KOREA
JAPAN
CANADA
LUXEMBOURG
“Better instruction won’t come from more detailed information, but from changing what people do . . . convincing teachers of the need to change and focusing where they need to change.”
Simon RodbergEducation Expert
Data will accomplish nothingif it doesn’t inspire change.
DATA ACTION
VISUALS
DATA
WHY DATA STORYTELLINGMATTERS
NARRATIVE
EXPLAIN: Narrative + Data
VISUALS
DATA
NARRATIVE
ENLIGHTEN:Data + Visuals
VISUALS
DATA
NARRATIVE
ENGAGE:Narrative + Visuals
En
ga
ge
VISUALS
DATA
NARRATIVE
Influencechange withdata stories
En
ga
ge
CHANGE
VISUALS
DATA
NARRATIVE
STORIES BEAT STATISTICS IN TWO KEY WAYS
5%statistics stories
VS 63% $1.14statistics story
VS $2.38
More Memorable More Persuasive1 2
WHAT INFLUENCES DECISION MAKING?
KIRK
LOGIC EMOTION
SPOCK BONES
INPUTS
Analytical Logical Effortful Conscious
PilotLazyController
Intuitive Emotional Automatic Subconscious
AutopilotPattern-seeking & Heuristics
FURTHERPROCESSING
System 1FAST
System 2SLOW
HOW WE PROCESS INFORMATION
Our bedroom. Two voices. I knock.
Paramedics finished her text, “…love you.”
OUR BRAINS ARE DRIVEN BY NARRATIVE“The human mind is a story processor, not a logic processor.”
Jonathan Haidt, American social psychologistFAST
OUR BRAINS EXPECT A COHERENT STORY
EMPTY
CORRECTIONVOLATILEMATERIALS
EMPTY
CORRECTIONSUSPICIOUSMATERIALS
1?
2?
VOLATILEMATERIALS
FAST
FORMEDNARRATIVE
FAST
FORMEDNARRATIVE
Johnson & Seifert, 1994.
IT’S NOT ENOUGH TO JUST PROVIDE INSIGHTS
FACTMYTH
CORRECTIONGAP
RESIDUAL NARRATIVE
FACT
“No one ever made a decision because of a number. They need a story.”
Daniel Kahneman
NEW INSIGHT
SUPPORTINGNARRATIVE
CLARIFICATION: STORYFRAMING VS. STORYTELLING
COLLECTION #1
DASHBOARD (STORYFRAMING)
3.5% 4.5%
PAST INSIGHTS
DATA STORY (STORYTELLING)
MANAGER KEY STAKEHOLDERS
NEW INSIGHT
COMPARISON: STORYFRAMING VS. STORYTELLING
STORYFRAMING STORYTELLING
Purpose Exploratory: frames information to generate potential insights
Explanatory: explains specific insights
Focus Broader: key metrics & dimensions
Narrower: related set of findings that support a main insight
Structure Hierarchical,non-linear layout
Linear sequence
Preparation Automated Curated
Delivery Dynamic (rolling) Static (snapshot)
Key features Filters / drills Annotations / highlighting
Usage Multi-use Single use
COLLECTION #1
DASHBOARD(STORYFRAMING)
3.5% 4.5%
DATA STORY(STORYTELLING)
NARRATIVE: THE STRUCTURE OF YOUR DATA STORY
TURNING YOUR FINDINGS INTO A DATA STORY
Rising InsightsSupporting details that reveal deeper insights into the problem or opportunity
Aha MomentMajor finding or central insight
Set-upBackground on current situation, character(s) & the hook
Solution & Next StepsPotential options & recommendation
1
2
3
4
Audience’s knowledge is enriched & likelihood to act is increased
DATA STORYTELLINGARC
Beginning Middle End
Gustav Freytag(1816-1895)
BEHIND THE SCENES: CRAFTING A DATA STORY
Set-up & Hook
Rising Insights
AhaMoment
Solution & Next Steps
BEHIND THE SCENES: CRAFTING A DATA STORY
Set-up & Hook
Rising Insights
AhaMoment
Solution & Next Steps
BEHIND THE SCENES: CRAFTING A DATA STORY
Set-up & Hook
Rising Insights
AhaMoment
Solution & Next Steps
BEHIND THE SCENES: CRAFTING A DATA STORY
Set-up & Hook
Rising Insights
AhaMoment
Solution & Next Steps
VISUALS: THE SCENES OF YOUR DATA STORY
WHAT ARE THE SCENES OF YOUR DATA STORY?
EXPLORATORY EXPLANATORY
1. DATA STORYTELLING > TRANSITION TO EXPLAINING
“The fundamental task in data analysis is to make smart comparisons—we’re always trying to answer the question ‘Compared with what?’ . . . It always comes down to making and showing smart comparisons.”
Edward Tufte
2. DATA STORYTELLING > ENABLING COMPARISONS
Identify the right data1
7 STEPS FOR BETTER VISUAL STORYTELLING
IDENTIFY THE RIGHT DATA FOR YOUR DATA STORY
TOTAL VALUES % Change Variance
CalculatedMetric
AddedContext
IDENTIFY THE RIGHT DATA FOR YOUR DATA STORY
Revenue Visits
Revenue Visits
Percent change may convey the insight more clearly than total values.
Revenue % Change
Visits % Change
Online Revenue & Visits % Change in Online Revenue & Visits
Revenue per visit (RPV)
IDENTIFY THE RIGHT DATA FOR YOUR DATA STORY
Revenue Visits
Revenue Visits
Calculated metrics may be more insightful than total values.
Online Revenue & Visits Revenue per visit (RPV) & Revenue
IDENTIFY THE RIGHT DATA FOR YOUR DATA STORY
Revenue Visits
Revenue Visits
Contextual data may make your visual more insightful.
RPV (YoY)
Revenue per visit (RPV)
Online Revenue & Visits Revenue per visit (RPV) & Revenue
IDENTIFY THE RIGHT DATA FOR YOUR DATA STORYVariance may better highlight key differences that you’re trying to expose.
Safety Incidents by Plant: 2017 vs. 2018 Year-to-Year Variance in Safety Incidents by Plant
IDENTIFY THE RIGHT DATA FOR YOUR DATA STORYVariance may better highlight key differences that you’re trying to expose.
FY2017
FY2018
Running Total Sales by Year
FY2017
FY2018
Running Total Variance between FY2017 & FY2018
Identify the right data1
Choose the right visualizations2
7 STEPS FOR BETTER VISUAL STORYTELLING
GRAPHICAL METHODS VARY IN EFFECTIVENESS
More accuratecomparisons
More genericcomparisons
Length
Angle
Shading
Direction
Curvature
Color Saturation
Position along non-
aligned scalePosition along common scale
Area Volume
Cleveland & McGill (1984). Graphical perception: Theory, experimentation, and application to the development of graphical methods. Journal of American Statistical Association. 79(387): 531-554.
ALL CHARTS ARE NOT CREATED EQUAL
5%
32%
29%
25%
9%
YouTube
Google+
YouTube
LinkedInGoogle+
5%
29%
32%
25%
9%
32%
29%
25%
9%
5%
Bar charts don’t necessarily need value labels to convey differences.
VARIETY IS SPICE OF VISUALIZATION—BUT BE CAREFUL
5%
32%
29%
25%
9%
Product penetration within key segments
Product penetration within key segments
Waffle charts allow for more precise comparisons for binary values.
https://bit.ly/2Jobux8
Identify the right data1
Choose the right visualizations2
Calibrate visuals to your message3
7 STEPS FOR BETTER VISUAL STORYTELLING
ANTICIPATE THE REQUIRED COMPARISONSPanel bar charts offer each category its own baseline for easier visual inspection.
Marketing Spend by Channel
2017
2018
2019
TV Digital Print Radio
TV Spend Digital Spend
Print Spend Radio Spend
90K
85K
75K
Identify the right data1
Choose the right visualizations2
Calibrate visuals to your message3
Remove unnecessary noise4
7 STEPS FOR BETTER VISUAL STORYTELLING
4 WAYS TO REDUCE THE NOISE IN YOUR VISUALS
Remove Surplus Data
1Aggregate Less Important Data
2Remove
Chartjunk
4Separate Data
Layers
3
REMOVE SURPLUS DATA THAT ISN’T NEEDED
Page Views
February 2019
Page Views
February 2019
SAVE AS
Click on legend label
Ask yourself what is essential to making your point. Remove what’s unnecessary.
USA
UK
Germany
Japan
Canada
France
Spain
Mexico
Brazil
China
AGGREGATE LESS IMPORTANT INFORMATION
Aggregate
19,631unitssold
30%
13%
9%9%8%
7%
6%
6%6%
6%USA
UK
GermanyJapan
Other30%
13%
9%9%
39% 19,631unitssold
To simplify charts, you can aggregate less critical data to reduce the cognitive load.
SEPARATE DATA LAYERSTo reduce noise, you can break apart data series into separate charts.
Sales Leads
Campaign A
Campaign D
Campaign C
Campaign B
Campaign A Campaign B
Sales Leads
Campaign DCampaign C
REMOVE CHARTJUNKRemove non-essential chart elements to help the data communicate more clearly.
Revenue
Units Sold
Product Sales in Q4Revenue
Units Sold
Product Sales in Q4
Product J
Product H
Product U
Identify the right data1
Choose the right visualizations2
Calibrate visuals to your message3
Remove unnecessary noise4
Focus attention on what’s important5
7 STEPS FOR BETTER VISUAL STORYTELLING
COLOR IS YOUR FRIEND FOR HIGHLIGHTING KEY POINTSUse color and grayscale to draw attention to focus area while still providing context.
Top 10 Articles by Page Views
February 2019
Top 10 Articles by Page Views
February 2019
TEXT DIRECTS THE FOCUS OF YOUR AUDIENCEText can be used to steer attention to what’s most important in a chart.
Top 10 Articles by Page Views
February 2019
In February, Article B was the second most popular article for total page views.
February 2019
Identify the right data1
Choose the right visualizations2
Calibrate visuals to your message3
Remove unnecessary noise4
Focus attention on what’s important5
7 STEPS FOR BETTER VISUAL STORYTELLING
Make your data accessible & engaging6
MAKE YOUR DATA ACCESSIBLEWhenever possible, use a horizontal orientation for data labels.
Employees by Location Employees by Location
EASY
HA
RD
When appropriate, sort results to make the information easier to consume.
Employees by Location Employees by Location
MAKE YOUR DATA ACCESSIBLE
Bob Beamon
http://www.nytimes.com/interactive/2012/08/04/sports/olympics/bob-beamons-long-olympic-shadow.html
29 feet 2 ½ inchesNY TIMES
MAKE YOUR DATA RELATABLE
Images can bring your key points to life and humanize your data.
88% of Crossover customers were satisfied with their car-buying experience.
MAKE YOUR DATA ENGAGING
88% of Crossover customers were satisfied with their car-buying experience.
88%
Identify the right data1
Choose the right visualizations2
Calibrate visuals to your message3
Remove unnecessary noise4
Focus attention on what’s important5
7 STEPS FOR BETTER VISUAL STORYTELLING
Make your data accessible & engaging6
Instill trust in your numbers7
AVOID TRUNCATING THE AXES OF BAR CHARTSBar charts should have a zero baseline or clearly indicate they’ve been altered.
Page ViewsAnnual Conversion Rate Annual Conversion Rate
3.12%3.12%
Annual Conversion Rate
USE A LINE CHART TO ZOOM INLine charts don’t need to have a zero baseline and can provide a narrower range.
Page ViewsAnnual Conversion Rate
3.12%3.12%
2.80%2.84%
2.89%
2.96%
CHOOSE TIMEFRAMES WISELY
Quarterly Profits
Choose a timeframe that provides adequate context when it is needed.
Page ViewsQuarterly Profits
$575K
$575K
CHOOSE TIMEFRAMES WISELY
Quarterly Profits
Choose a timeframe that provides adequate context when it is needed.
Page ViewsQuarterly Profits
$575K
$575K
MAXIMIZE YOUR VISUAL STORYTELLING IMPACTRIGHT DATA
RIGHT VISUALIZATIONS
RIGHT CONFIGURATION
REMOVE NOISE
FOCUS ATTENTION
MAKE ACCESSIBLE
INSTILL TRUST
FINAL THOUGHT
OUR GUIDED TOUR OF POMPEII
GUIDE
AS A DATA STORYTELLER, YOU ARE THE GUIDE
DRIVECHANGE
ENLIGHTEN
ENGAGE
EXPLAIN
YOU
QUESTIONS?
@analyticshero
Brent.dykes@domo.com
DATA STORIES BEAT STATISTICS.
DATA
NARRATIVE VISUALS
En
ga
ge
CHANGE