What kind of stories are best told with data? How do you take raw numbers and turn them into an engaging, meaningful story? Thinking Machines' content strategist Pia Faustino delivered this presentation on the data storytelling process at the "Humans + Machines: Using Artificial Intelligence to Power Your People" conference on February 19, 2016 in Bonifacio Global City, Taguig, Philippines.

1. Thinking
Machines
Data Science
Title Text
✦ Body Level One
✦ Body Level Two
✦ Body Level Three
✦ Body Level Four
✦ Body Level Five
From Info to Insight:
Data Storytelling
for Organizations
Thinking
Machines
Data Science
Pia Faustino
Content Strategist, Thinking Machines
@piafaustino

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Machines
Data Science
What we’ll talk about
01
02
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Introductions
What makes a good data story?
How do you turn data into a story?

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Machines
Data Science
About Thinking Machines
We are a team of
data scientists engineers
statisticians storytellers
+ +
+
Data StrategyWe do
Data Engineering
Data Science
Data Storytelling

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Machines
Data Science
Our Experience

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Data Science
Who am I?

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Data Science

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Data Science

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Data Science
Why is an
ex-journalist
talking at an event
about
artificial intelligence?

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Artificial intelligence
is the science of
making computers
that can do tasks that
normally require
human intelligence.

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Data Science
AI example: iPhone’s Siri

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Data Science
AI example: iPhone’s Siri

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AI Example: FB Facial Recognition

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Data Science
Both artificial intelligence
and data storytelling rely on
recognizing patterns in data.

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Data Science
Organizations interested in
using artificial intelligence
must first have a
data-driven culture.

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Data Science
Data storytelling is an
accessible starting point for
any organization.

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Data Science
What makes a
good (data) story?

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Data Science
Emotional engagement
+
Intellectual insight

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Types of data stories
01
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Change over time
Macro to Micro (and vice versa)
Correlation and Causation
Comparisons and Contrasts
Outliers

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“This is How Fast America
Changes Its Mind”
Bloomberg, June 2015
http://www.bloomberg.com/graphics/
2015-pace-of-social-change/
US states legalising same-sex marriage
2004-2016
Change over Time

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Change over Time
What’s really warming the world?
Bloomberg News, June 2015
http://www.bloomberg.com/graphics/2015-whats-warming-the-world/

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Data Science
Correlation and Causation
A woman’s age vs.
the age of men who
look best to her
Source: OK Cupid

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… and Contrast / Comparison
A man’s age vs. the
age of women who
look best to him
OK Cupid, 2013

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Comparison and Contrast
“How that Map You Saw on 538 Underrepresents Minorities”
by Joshua Tauberer, posted on Medium
https://medium.com/@joshuatauberer

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Data Science
Macro to Micro (and VV)
Are you in the Global Middle Class?
Pew Research, July 2015
http://www.pewglobal.org/2015/07/08/a-global-middle-class-is-more-promise-than-reality/

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Micro to Macro (and VV)
1052 mass shootings in 1066 days | The Guardian, Dec 2015
http://www.theguardian.com/us-news/ng-interactive/2015/oct/02/mass-shootings-america-gun-violence

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Outliers
“Ronda Rousey fights
like an outlier”
FiveThirtyEight, July 2015
http://fivethirtyeight.com/datalab/ronda-
rousey-fights-like-an-outlier/
Rousey

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Who is using data storytelling?
M E D I A G OV ’ T & C I V I L S O C I E T Y B U S I N E S S

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How do you get from raw data…

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… to a meaningful story?

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Our Experience:
The Data Storytelling Process

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Our Data Storytelling Process
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Get the data
Choose your questions
Interview the data
Curate your findings
Communicate and visualize

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Data Science
Case Study:
Looking for Dubious Digits
in National Election Data

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Data Science
Thinking Machines collaborated with
Philippine Center for Investigative Journalism
to analyze voter registration and turnout data for a
series of stories on the Philippine elections.

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Data Science
6,928 rows x 9 columns
Voter registration & turnout
4 national elections
1,644 Towns + Cities
Source: COMELEC / PCIJ
Filetype: CSV
About the data:

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Garbage in.
Garbage out.
Data Sanity Check

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Good data analysis
begins with asking
good questions.

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Where could unusual/outlier voting patterns be
found?
Where were voter turnout rates unusually high?
Are poorer places more likely to have high voter
turnout?
Questions

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Data Science
Interviewing the Data
Where were voter turnout rates unusually high?
Our tools: iPython Notebook, Pandas Library
voter_stats[‘turnout_rate’] =
voter_stats[‘voter_turnout’] /
voter_stats[‘registered_voters']

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What’s the
voter turnout
rate per town? Has it always
been this
way?
What’s
normal?
Has it always
been this way
in Luzon?… in the
Visayas?
… Mindanao?
… in Basilan?
… in Antique?
… in San
Juan?
What’s
considered
high / low?
???
???
???
???
???

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Now you have answers.
What matters?
Curate.

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Who cares?
What’s useful?
What’s surprising?
What’s sound and truthful?

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Insight:
There are places where
voter turnout rates were
almost impossibly high
— a possible sign of
vote padding.

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In some towns, for example …
Town 1 124%
Town 2 98.8%
Town 3 98.4%
3-Election Voter Turnout Rate

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Challenge:
How do you efficiently
show patterns in data
for 1,644 towns/cities?

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98.8%
44.5%
Voter Turnout Rates for All Cities/Towns
100%
80%
40%
124%

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Insight:
There were places
where voter turnout
changed in
very weird ways.

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In one town, for example …
2007 2010 2013
10,051 1,337 3,045
Voter Turnout

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Challenge:
How do you efficiently
show change over time
for 1,644 towns/cities?

49. Most towns/cities grew
voter turnout by:
✦Around 14%
from 2007 to 2010
✦Around 21%
from 2007 to 2013
2007 2010 2013
0
100
40
20
-20
-40
60
80

50. Town 1
2007: 9,342
2010: 11,470
2013: 42,698
Town 2
2007: 2,749
2010: 8,377
2013: 9,165
Town 3
2007: 10,051
2010: 1,337
2013: 3,045
+350%
-100%
2007 2010 2013

51.
Are there patterns in where the
“normal” vs “outliers” are
distributed?
What if we break it down by
island group?

52. 2007 2010 2013
+350%
-100%
Luzon

53. 2007 2010 2013
+350%
-100%
Visayas

54. 2007 2010 2013
+350%
-100%
Mindanao

55. How about by region?

56. 2007 2010 2013
+350%
-100%
NCR

57. 2007 2010 2013
+350%
-100%
Cagayan Valley

58. 2007 2010 2013
+350%
-100%
Western Visayas

59. 2007 2010 2013
+350%
-100%
ARMM

60. What if we break things
down by province?

61. Cebu Eastern Samar

62. Pampanga Marinduque

63. Bukidnon Maguindanao

64. Often, the data can tell
us what happened.
But not why.

65. What untapped data does your
organization have?
What stories can you tell?
Why will it matter?
And to whom?

66. Got a story to tell with data?
Contact us at:
hello@thinkingmachin.es.
thinkdatasci