STORIES BEAT STATISTICS - Domo · 2019. 4. 22. · FROM 2000 TO 2015, THE US HAS FALLEN FURTHER...

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%


MATH Scores (PISA 2015)

RICH

POOR

Oscar4th Grade


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


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%



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


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


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



490

486

486

477

470

455

454

427

408

OECD Average

Luxembourg

US: 25-49.9%

Hungary


US: 50-74.9%

Greece

US: 75% or more

Mexico

US: 50-74.9%

US: 75% or more


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


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

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VISUALS

DATA

NARRATIVE

Influencechange withdata stories

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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

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3

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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

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


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)


Revenue Visits

Revenue Visits

Calculated metrics may be more insightful than total values.

Online Revenue & Visits Revenue per visit (RPV) & Revenue


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


Choose the right visualizations2


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%

Facebook

YouTube

LinkedIn

Twitter

Google+

Facebook

Twitter

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



Calibrate visuals to your message3


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




Remove unnecessary noise4


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





Focus attention on what’s important5


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


February 2019

TEXT DIRECTS THE FOCUS OF YOUR AUDIENCEText can be used to steer attention to what’s most important in a chart.


February 2019

In February, Article B was the second most popular article for total page views.

February 2019







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%







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

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

[email protected]

DATA STORIES BEAT STATISTICS.

DATA

NARRATIVE VISUALS

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CHANGE

STORIES BEAT STATISTICS - Domo · 2019. 4. 22. · FROM 2000 TO 2015, THE US HAS FALLEN FURTHER...

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