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From Information to Insight: Data Storytelling for Organizations

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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.

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From Information to Insight: Data Storytelling for Organizations

  1. 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
  2. 2. Thinking Machines Data Science What we’ll talk about 01 02 03 Introductions What makes a good data story? How do you turn data into a story?
  3. 3. Thinking 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
  4. 4. Thinking Machines Data Science Our Experience
  5. 5. Thinking Machines Data Science Who am I?
  6. 6. Thinking Machines Data Science
  7. 7. Thinking Machines Data Science
  8. 8. Thinking Machines Data Science Why is an ex-journalist talking at an event about artificial intelligence?
  9. 9. Thinking Machines Data Science Artificial intelligence is the science of making computers that can do tasks that normally require
 human intelligence.
  10. 10. Thinking Machines Data Science AI example: iPhone’s Siri
  11. 11. Thinking Machines Data Science AI example: iPhone’s Siri
  12. 12. Thinking Machines Data Science AI Example: FB Facial Recognition
  13. 13. Thinking Machines Data Science Both artificial intelligence and data storytelling rely on recognizing patterns in data.

  14. 14. Thinking Machines Data Science Organizations interested in using artificial intelligence must first have a data-driven culture.
  15. 15. Thinking Machines Data Science Data storytelling is an accessible starting point for any organization.
  16. 16. Thinking Machines Data Science What makes a good (data) story?
  17. 17. Thinking Machines Data Science Emotional engagement + Intellectual insight
  18. 18. Thinking Machines Data Science Types of data stories 01 02 03 04 05 Change over time Macro to Micro (and vice versa) Correlation and Causation Comparisons and Contrasts Outliers
  19. 19. Thinking Machines Data Science “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
  20. 20. Thinking Machines Data Science Change over Time What’s really warming the world? Bloomberg News, June 2015 http://www.bloomberg.com/graphics/2015-whats-warming-the-world/
  21. 21. Thinking Machines Data Science Correlation and Causation A woman’s age vs. the age of men who look best to her Source: OK Cupid
  22. 22. Thinking Machines Data Science … and Contrast / Comparison A man’s age vs. the age of women who look best to him OK Cupid, 2013
  23. 23. Thinking Machines Data Science Comparison and Contrast “How that Map You Saw on 538 Underrepresents Minorities” by Joshua Tauberer, posted on Medium https://medium.com/@joshuatauberer
  24. 24. Thinking Machines 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/
  25. 25. Thinking Machines Data Science 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
  26. 26. Thinking Machines Data Science Outliers “Ronda Rousey fights like an outlier” FiveThirtyEight, July 2015 http://fivethirtyeight.com/datalab/ronda- rousey-fights-like-an-outlier/ Rousey
  27. 27. Thinking Machines Data Science 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
  28. 28. Thinking Machines Data Science How do you get from raw data…
  29. 29. Thinking Machines Data Science … to a meaningful story?
  30. 30. Thinking Machines Data Science Our Experience: The Data Storytelling Process
  31. 31. Thinking Machines Data Science Our Data Storytelling Process 01 02 03 04 05 Get the data Choose your questions Interview the data Curate your findings Communicate and visualize
  32. 32. Thinking Machines Data Science Case Study: Looking for Dubious Digits in National Election Data
  33. 33. Thinking Machines 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.
  34. 34. Thinking Machines 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:
  35. 35. Thinking Machines Data Science Garbage in. Garbage out. Data Sanity Check
  36. 36. Thinking Machines Data Science Good data analysis begins with asking good questions.
  37. 37. Thinking Machines Data Science 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
  38. 38. Thinking Machines 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']
  39. 39. Thinking Machines Data Science 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? ??? ??? ??? ??? ???
  40. 40. Thinking Machines Data Science Now you have answers. What matters? Curate.
  41. 41. Thinking Machines Data Science Who cares? What’s useful? What’s surprising? What’s sound and truthful?
  42. 42. Thinking Machines Data Science Insight:
 There are places where voter turnout rates were almost impossibly high — a possible sign of vote padding.
  43. 43. Thinking Machines Data Science In some towns, for example … Town 1 124% Town 2 98.8% Town 3 98.4% 3-Election Voter Turnout Rate
  44. 44. Thinking Machines Data Science Challenge: How do you efficiently show patterns in data for 1,644 towns/cities?
  45. 45. Thinking Machines Data Science 98.8% 44.5% Voter Turnout Rates for All Cities/Towns 100% 80% 40% 124%
  46. 46. Thinking Machines Data Science Insight:
 There were places where voter turnout changed in very weird ways.
  47. 47. Thinking Machines Data Science In one town, for example … 2007 2010 2013 10,051 1,337 3,045 Voter Turnout
  48. 48. Thinking Machines Data Science Challenge: How do you efficiently show change over time for 1,644 towns/cities?
  49. 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. 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. 51. 
 Are there patterns in where the “normal” vs “outliers” are distributed? What if we break it down by island group?
  52. 52. 2007 2010 2013 +350% -100% Luzon
  53. 53. 2007 2010 2013 +350% -100% Visayas
  54. 54. 2007 2010 2013 +350% -100% Mindanao
  55. 55. How about by region?
  56. 56. 2007 2010 2013 +350% -100% NCR
  57. 57. 2007 2010 2013 +350% -100% Cagayan Valley
  58. 58. 2007 2010 2013 +350% -100% Western Visayas
  59. 59. 2007 2010 2013 +350% -100% ARMM
  60. 60. What if we break things down by province?
  61. 61. Cebu Eastern Samar
  62. 62. Pampanga Marinduque
  63. 63. Bukidnon Maguindanao
  64. 64. Often, the data can tell us what happened. But not why.
  65. 65. What untapped data does your organization have? What stories can you tell? Why will it matter? And to whom?
  66. 66. Got a story to tell with data? Contact us at: hello@thinkingmachin.es. thinkdatasci

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