This document outlines an agenda for a two-day workshop on data storytelling for social change. Day 1 focuses on asking questions of data, finding stories within data, and techniques for telling stories. Activities include analyzing case studies, sketching stories, and building a data sculpture. Day 2 covers making arguments with data and hands-on practice with sample datasets in Tableau. The goal is to provide a process for using data to further social causes from brainstorming questions to assessing the impact of stories.

1. Data Storytelling
for Social Change
Rahul Bhargava

2. Agenda
Day 1
• Introductions
• Asking Questions
• Finding Stories
• Telling Stories
• Wrap-up
Day 2
• Opening
• Making Arguments
• Hands-on Practice
• Data Culture
• Wrap-up

3. Introductions

4. About You
What are your hopes
and fears
for data storytelling?

5. About Me

6. MIT Center for Civic Media

7. Data Storytelling?

8. Going from Data to Story
What is a story?What is data?

9. 1. What datasets are used?
2. How are they shown?
3. What is the story?
4. Is it well told?
The Global State of Agriculture by Lemon.ly for USAID, 2011
Deconstruct a Dataviz

10. • Data must supports your narrative
• Support layers of reading
• Consistent text and visual
Takeaways

11. Abelson’s MAGIC Criteria
• Magnitude - size of the claim
• Articulation - how precise is your claim
• Generality - is it valid in multiple contexts
• Interestingness - can this change beliefs in a way that
matters?
• Credibility - do you believe it?
Robert Abelson. 1995. Making Claims with Statistics. In Statistics as Principled Argument.
Hillsdale, NJ: Lawrence Erlbaum Associates, 1–16. (PDF)

12. Experience the Data
City Life / Vida Urbana
2015

13. Weakness → Strength
The Library of Missing Datasets
Mimi Ohnuoha
2016

14. Who Tells the Story & How?
Data Khanga
Tanzania dLab, Tanzania Bhora Initiative,
Data Zetu, IREX,

15. The Old is New Again
Exhibit of American Negroes
W.E.B. Du Bois, Thomas
Calloway, Booker T. Washington
1900

16. Don’t Lie
Pandey, Anshul Vikram and Rall, Katharina and Satterthwaite, Margaret L.
and Nov, Oded and Bertini, Enrico, How Deceptive are Deceptive
Visualizations?: An Empirical Analysis of Common Distortion Techniques
(February 18, 2015). Proceedings of the ACM Conference on Human
Factors in Computing Systems 2015.
Zer-Aviv, M. Disinformation Visualization: How to lie with datavis.
Visualizaing Informaiton for Advocacy. 2014

17. A Process

18. Ethical Backdrop
vs.

19. Using Data
Improving Operations
Use data inside your organization to assess operational efficiency and
measure impact.
Spreading Your Message
Use data in your marketing and outreach materials to communicate the
impact of your work.
Bringing People Together
Use data to gather people from different sectors together and
strengthen partnerships.

20. Data Culture
Data centric? Data driven? Data informed?

21. A Process
Asking
questions
brainstorming,
hypothesis forming,
audience and goals
Gathering
data
scraping, collecting,
cleaning
Finding a
story
cleaning and analyzing
Telling
your story
picking a technique
Trying it
out
assessment

22. Separate Technology from Process
Excel doesn’t help you learn to ask better
questions.
R won’t pick the most appropriate chart
for telling your story.
Tableau doesn’t tell you which narrative
arc will convince your audience.

23. Asking Questions
and getting data

24. Paper Spreadsheet

25. Consistency: are observations always entered the
same?
Completeness: do you have coverage of the topic?
Usability: machine readability?
Atomicity: is each row an observation?
Clean vs. Dirty Data

26. An Example
Source: data.gov.in
India: State/UT-wise Production of Milk from 2010-11
to 2015-16
What is being observed? How is it described?

27. Tools for Getting Digital Data
does lots of things
does one thing
easy to learn
hard to learn
BeautifulSoup
mechanize
JQuery in browser
console
requests
Chrome Scraper
extension
copy and paste

28. Storage Strategies
• .csv files
• .xls files
• .txt files
• .pdf files
• HTML tables
• relational databases
• non-relational databases

29. Activity: Asking Good Questions
1. Partner up
2. Visit wtfcsv.databasic.io
3. Pick a dataset
4. Identify:
• a question you’d like to ask
• other datasets you’d need
• how you’d get those

30. • Ask yourself some questions to reveal your biases
• 80% of this is about finding the right questions
• Don’t let your dataset limit you
Takeaways

31. Finding Stories

32. What do these help us do?
• min
• max
• mean / average
• median
• quartiles
• standard deviation
• normalization
StatisticalVocabulary

33. Types of Stories
• Changes over Time
• Interesting Factoids
• Surprising Interactions
•Personal Experiences
•Revealing Comparisons

34. Data: Trees in Somerville

35. Interesting
Factoids
Just one hazelnut
tree in the whole
town!
wikipedia.org

36. Surprising Interactions

37. Revealing Comparisons

38. Changes
Over Time

39. Personal
Experiences
You live on that
street? All 9 spruce
trees in town are on
that street.

40. • Your narrative arc is key
• Stuck? Use these story archetypes as scaffolding
• Meet your audience where they are
Takeaways

41. Genres of Narrative Visualization
Segel, E., & Heer, J. (2010). Narrative visualization: Telling stories with data. Visualization and
Computer Graphics, IEEE Transactions On, 16(6), 1139–1148.

42. Activity: Sketch a Story
1. Partner up
2. Visit wordcounter.databasic.io
3. Pick a sample dataset
4. Find a story to tell
5. Sketch a visual of your story

43. Techniques You Can Use

44. Being an Editor
Source: excel-easy.com

45. The Usual Inspirations
Periscopic
2013
Florence Nightingale
1858
William Playfair
1786

46. A Richer Set of Inspirations
Elizabeth Peabody
1856
British Abolitionists
1788
Rahul Bhargava
2014

47. A More Creative Set
• Personal Stories
• Traditional Charts
• Creative Charts
• Data Sculptures
•Traditional Maps
•Creative Maps
•Be The Data

48. Personal
Stories

49. Traditional Charts

50. Creative Charts
Groundwork Somerville data mural
2010

51. Creative Charts The Carbon Bathtub
Nigel Holmes
2009

52. Data Sculptures
Black Cloud
Ogilvy/Beijing
2007

53. Traditional Maps
Million Dollar Blocks
Spatial Information Design Lab
2012

54. Lets Tesselate
NPR visuals team
2015

55. Creative Maps
L’Etat-Archipel de Palestine
Julien Bousac
2013

56. Data Games The Ribbons Experiment
Prof. Dan Gilbert & Prudential
2015

57. Charting In Depth

58. Chart Tools
does lots of things
does one thing
easy to learn
hard to learn
Comic Life
Super Lame
rawgraphs.io
processing
Minitab
Illustrator
skrollr
infogr.am
Excel
Tableau
Google Charts
JMP
gephiD3.js

59. Choose the Right Chart
Andrew Abela

60. Visual
Vocabulary
Financial
Times

61. Activity: Viz Hunt
• Form a team of 3
• Pick a chart type
• Read about it on datavizcatalogue.com
• Scan http://bit.ly/vizzoo if you want to
• Prepare a 2-minute summary on what
the chart is and why/when we should
use it

62. Day 1 Wrap-Up

63. Agenda
Day 1
• Introductions
• Asking Questions
• Finding Stories
• Telling Stories
• Wrap-up
Day 2
• Opening
• Making Arguments
• Hands-on Practice
• Data Culture
• Wrap-up

64. A Process
Asking
questions
brainstorming,
hypothesis forming,
audience and goals
Gathering
data
scraping, collecting,
cleaning
Finding a
story
cleaning and analyzing
Telling
your story
picking a technique
Trying it
out
assessment

65. Activity: Build a Data
Sculpture

66. Ice Cream
Consumption
Happiness in
Somerville

67. Evidence vs. Argument
Zer-Aviv, M. Disinformation Visualization: How to lie with datavis.
Visualizaing Informaiton for Advocacy. 2014
Making an evidence presentation is a moral
act as well as an intellectual activity. To
maintain standards of quality, relevance, and
integrity for evidence, consumers of
presentations should insist that presenters be
held intellectually and ethically responsible for
what they show and tell. Thus consuming a
presentation is also an intellectual and a moral
activity.
—Beautiful Evidence, Edward Tufte

68. Activity: Gallery Walk
1. Make a pair
2. Visit an example
3. Discuss:

69. Activity: Remix

70. Activity: Remix

71. Activity: Remix
1. Make a group
2. Pick a technique card
3. Sketch out how you’d
present this story using that
technique

72. Activity: Convince Me
practice making effective arguments to different audiences

73. Activity: Remix

74. Practice Time
let’s practice this stuff with some data

75. Tableau Demo

76. Sample Datasets
New York City Dog Registrations
Breed, name, age, gender, borough
bit.ly/dogdata2017
Trees in Somerville, MA, USA
Species, condition, location, etc.
bit.ly/treedata2
Blue Bikes Bike Sharing Trips
Date, start/end station, etc
bit.ly/hubwaydata123

77. Find a Story
Look for a story within the dataset to share:
The data say ___________________ ,
we want to tell that story because __________.

78. Write a Storybook

79. Data Storytelling
for Social Change
Rahul Bhargava