Keynote at the Nordic data journalism conference #NODA16 - an outline of issues facing data journalism which journalists and academics need to focus on in the next decade.

1. ACTION!
DATA!
11
@PaulBradshaw
MA Online Journalism
Birmingham City University

2. 2
“We are moving from the
knowledge/power nexus portrayed
by Foucault to a data/action nexus
that does not need to move
through theory: All it needs is
data together with preferred
outcomes”
“
Geoffrey Bowker

3. Shangri-La

4.
What is journalism for?

5. Independence
from data

6. http://www.independent.co.uk/student/student-life/Studies/half-of-uk-university-students-are-losing-marks-
for-not-referencing-correctly-survey-finds-a6983716.html

7. First duty? Public debate?

12. 12
Are news values
changing?

13. https://www.youtube.com/watch?v=UNQteT9Bu2w&t=2m

14. Voice to the
voiceless

15. Who has a voice
in data?

16. Who has a voice
in data?
How is data used as a
proxy for the voiceless?

17. Who has a voice
in data?
How is data used as a
proxy for the voiceless?
How is data used to
amplify voices?

22. First duty? Public debate?
http://www.theguardian.com/news/datablog/2014/oct/29/todays-key-fact-you-are-probably-wrong-about-almost-everything

23. Truth to power

24. 24
DATA JOURNALISM
≠ INVESTIGATIVE
JOURNALISM

25. 25
Using data to tell stories in the
best possible way, combining the
best techniques of journalism:
including visualizations, concise
explanation and the latest
technology. It should be open,
accessible and enlightening.
“
Simon Rogers

26. Data dystopia

27. Data dystopia
39.8333333,
-98.585522

29. http://www.buzzfeed.com/ryanhatesthis/who-watches-more-porn-republicans-or-democrats#.mtoR5PAWV

32. 32
Flowing Data’s NYC map
offers a great example of how three
mapping traps — seasonal bias,
location fuzziness, and tagging
bias — can lead to a map that
does not reflect reality.
“
Margaret McKenna,

34. http://fivethirtyeight.com/datalab/mapping-kidnappings-in-nigeria/

35. 35
Data journalism is like sex at
university – everyone talks about it;
few do it; fewer still do it well.”“
Neil McIntosh

36. Acquisition
Cleaning
Loading
Verification
Analysis
Cherry-picking
Presentation
MaintenanceJake Harris

37. 37
DATA
≠
TRUTH

38. 38
ACTION!
DATA!

39. …Bad data

40. https://theintercept.com/drone-papers/firing-blind/

41. 41
When a measure becomes a
target, it ceases to be a good
measure”.“
Goodhart’s Law

43. *

45. 45
“My husband couldn’t
understand why people were
taunting him and calling him a
child molester”
“
Texas Tribune

46. “Statistics are
like a bikini…”

47. 47
Statistics are like a bikini.
What they reveal is suggestive,
but what they conceal is vital.”“
Prof. Aaron Levenstein
http://charonqc.wordpress.com/2011/03/27/guest-post-any-complaints-why-the-ipcc-is-failing-us-all/

49. http://www.bloomberg.com/news/articles/2011-12-01/correlation-or-causation

52. 52
HOLY MULTIPLE
COMPARISONS
PROBLEM!

55. 55
ALGORITHM
DYNAMICS!
BIG DATA
HUBRIS!

56. *
Abraham Wald
http://www.motherjones.com/kevin-drum/2010/09/counterintuitive-world

58. Routes

59. Telling stories

61. 61
Having a simple data vis
on the page increases dwell time
by a third”“
Trinity Mirror Editorial Conference 2014

62. “Photos give the biggest lift with
quotes coming in a close second.
Tweets containing numbers, a video url and hashtags also lead to double-digit boost.”
https://media.twitter.com/best-practice/news-the-impact-of-tweeting-with-photos-videos-hashtags-and-links

64. 64
The death of 1 man = tragedy,
the death of millions is a statistic
“
Not Stalin.

65. 65
Courts punish people less
harshly when they harm more
people.”“
Ben Goldacre

67. Andy Kirk, Data Visualisation

69. Robot journalism
- or computational
journalism?

70. 70
DRAMA!
TECHNOLOGICAL!

71. http://nytlabs.com/projects/editor.html

73. Daniel Ellsberg + co:
more than 1 year

74. Daniel Ellsberg + co:
more than 1 year
=18 years for Bradley
Manning leaks

75. Daniel Ellsberg + co:
more than 1 year
=18 years for Bradley
Manning leaks
=2880 years for
Offshore leaks

76. =28,800 years for
Panama Papers

77. 77
If we don’t address software
itself, we are in danger of always
dealing only with its effects rather
than the causes”
“
Lev Manovich

78. Data integrity,
journalist
integrity

79. Fake adverts
Spyware
Backdoors
App permissions
Passwords
Phishing

80. Rituals of
transparency &
culture shifts

81. Mark Coddington

82. http://www.buzzfeed.com/johntemplon/how-we-used-data-to-investigate-match-fixing-in-tennis#.eiR89lLA2

83. Wilson Lowrey http://www.tandfonline.com/doi/figure/10.1080/1461670X.2015.1052537
http://www.poynter.org/2016/there-are-96-fact-checking-projects-in-37-countries-new-census-finds/396256/

84. 84
“This clash of logics has
required journalists to reshape
forms and practices in order to
regain lost legitimacy, and the fact
checking site is a result of this
reshaping [because it takes sides].”
“
Wilson Lowrey

85. Lighthouses

86. 1. How independent?
2. How do we choose?
3. Who has a voice?
4. Myth vs truth?
5. What mistakes do we make?
6. How do we engage?
7. What are (and can) we automating?
8. How do we ensure integrity?
9. What cultures and why?
10. Data as power!
Everybody likes a list

87. 87
ACTION!
DATA!

88. 88
@PaulBradshaw
Thank you._
MA Online Journalism
Birmingham City University