A seminar by Mirko Lorenz @MIRKOLORENZ (EJC European Journalism Center) on Data Driven Journalism topics at Ordine dei Giornalisti del Veneto, Venezia. 14 April 2015 #DDJ

1. A D E E P E R L O O K
F O R B E T T E R S T O R I E S
D A TA D R I V E N J O U R N A L I S M S E M I N A R
O R D I N E D E I G I O R N A L I S TA • V E N E Z I A 2 0 1 5 • E U R O P E A N J O U R N A L I S M C E N T E R

2. O V E R V I E W:
- I N T R O D U C T I O N T O D D J
- E X E R C I S E : 3 X 3 - W H AT, W H Y, H O W
- T O O L : D ATA W R A P P E R
- T I P S : E X C E L F O R M U L A S
- Q U E S T I O N S & D I S C U S S I O N

3. @ M I R K O L O R E N Z
J O U R N A L I S T / I N F O R M AT I O N A R C H I T E C T
F O C U S : C R O S S M E D I A / D ATA - D R I V E N J O U R N A L I S
D ATA W R A P P E R
D ATA D R I V E N J O U R N A L I S M . N E T
D E U T S C H E W E L L E I N N O VAT I O N T E A M
Brief introduction

5. B I G D ATA
S M A L L D ATA
R E L E VA N T D ATA

6. W H AT I S
D ATA - D R I V E N
J O U R N A L I S M ?

8. DEF:
DATA-DRIVEN JOURNALISM
= WORKFLOW
‣ Tasks are to collect, clean, visualize and report
‣ Data-driven journalism is a process

9. Data is a new camera
(Replace as many stock photos with charts as you can)

10. T H R E E K E Y TA S K S
• Collect national, regional, local data
• What does it mean for readers/users?
• Enable comparisons, look for outliers

11. S E E : W I K I P E D I A „ D ATA - D R I V E N J O U R N A L I S M “
data-driven journalism
S E E : D ATA J O U R N A L I S M H A N D B O O K ( F R E E )
Tipp: #ddj (Twitter)

12. #ddj
examples

13. Edward Tufte: To be truthful and revealing, data graphics must bear on the
question at the heart of quantitative thinking: “Compared to what?, from:
The Visual Display of Quantitative Information
START WITH THIS QUESTION:
COMPARED TO WHAT?

14. http://www.businessinsider.com/the-future-of-digital-2013-2013-11?op=1
E X A M P L E

15. E X A M P L E

16. E X A M P L E

17. E X A M P L E

18. http://www.ft.com/intl/cms/s/2/1392ab72-64e2-11e4-ab2d-00144feabdc0.html?mc_cid=2e8df2f2e5&mc_eid=6dffebf6ef#axzz3TV8qJA8c

19. W H Y
D ATA - D R I V E N
J O U R N A L I S M ?

20. IN A DATA-DRIVEN WORLD NEWSROOMS
NEED TO ADAPT
‣ Online Publishing
‣ Data as a source of exclusive reporting positions
Früher
Daten,
Studien,
Umfragen
Redaktion
Heute
Redaktion
Open Data
„Not-so-open“
Data

23. R E D A K T I O N E N U N D A R C H I V E M Ü S S E N
G L E I C H Z I E H E N , A R B E I T V E R E I N FA C H E N …

24. Q U E L L E : V I S U A L LY - H T T P S : / / W W W. Y O U T U B E . C O M / WAT C H ? V = A I V K F N E R B P Q

25. Q U E L L E : V I S U A L LY - H T T P S : / / W W W. Y O U T U B E . C O M / WAT C H ? V = A I V K F N E R B P Q

26. A LIST OF REASONS FOR DATA-DRIVEN WORK:
‣ Help our readers to make important decisions in life:
Election, Economy, Education/Profession, Real Estate, Retirement,
Health
‣ Users expect competent support and guidance
‣ Investivative projects to tackle corruption and big issues
‣ Chance to build a unique regional reporting position
‣ Services for trust and decision making to complement reporting
‣ Outlook: From Attention to Trust

27. BIG OPPORTUNITIES
Let people search less
Reduce the time they need to search
Help to make decisions
Detect big changes/issues earlier
Be part of trust economy

28. A R E A S O F
A P P L I C AT I O N
T E L L I N G S T O R I E S
B A S E D O N D ATA

29. GO THROUGH THE KEY QUESTIONS - FIVE W & ONE H
http://www.slideshare.net/stsanto/the-back-of-the-napkin-dan-roam
WHAT? (WHO’S COUNTING?)
HOW MUCH? (COMPARE)
WHERE? (MAP)
WHEN? (TIMELINE)
HOW? (FLOWCHART)
WHY (ANALYSIS)

30. – C H R I S T I A N B A U E R
„Zitat hier eingeben.“
D ATA R E S E A R C H L E A D
T O T H I S S T O RY

31. John Snow, 1854
Text
C L A S S I C E X A M P L E O F
D ATA / M A P

32. http://www.heise.de/newsticker/meldung/Umsatzsteigerung-und-Gewinnrueckgang-bei-Amazon-1285944.html
T H E C A S E F O R C O L L E C T I N G T H E N U M B E R S O V E R T I M E
C O L L E C T I N G R E L E VA N T,
L O C A L N U M B E R S O V E R T I M E
C R E AT E S VA L U E …

33. Quelle: New York Times
S I M P L E B A R , B I G S T O RY

34. – C H R I S T I A N B A U E R
„Zitat hier eingeben.“
D ATA - D R I V E N S T O RY /
L O C A L N E W S PA P E R

35. „Zitat hier eingeben.“
The project is an excellent example of journalists intervening to put
a largely neglected issue on the political agenda, and providing
decision-makers and the public with the evidence they need to
take action to stop these tens of thousands of deaths at Europe’s
borders. This is data journalism at its best. We need more projects
like this.
B I G I N V E S T I G AT I O N ,
M U LT I - P U B L I C AT I O N A C R O S S
E U R O P E

36. – C H R I S T I A N B A U E R
„Zitat hier eingeben.“
I M P R E S S I V E I N T E R A C T I V E ,
D O N E B Y L O C A L
N E W S R O O M

37. A P P F O R R E A D E R S

38. H O W ?
T H E T H I N K I N G B E H I N D
N O TA B L E D ATA
P R O J E C T S

39. Quelle: Alan McLean, NYT//Amsterdam, 2010

40. Quelle: Alan McLean, NYT//Amsterdam, 2010

41. 1. Data Story 2. Data Special 3. Data App
Find Small datasets Complex data Big data
Clean Excel Open Refine Database
Visualize Line, Bar, Pie Interaction & Filters Dashboards
Publish Embed Special URL App or service
Who? Journalist Journalist + Designer
Journalist + Designer
+ Developer
THREE TYPES OF PROJECTS

42. TEAMS NEEDED
Journalisten/
Dokumentation
DeveloperDesigner
Ideas and Questions
Search and data verification
Project management
Visual quality
Clarity
Depth
Surprise
Simplifying
Automating
Automatisierung
Presentation
Agility
Innovation
CC-BY: Mirko Lorenz, 2012

43. E X A M P L E :
F I N D I N G
T H E C H E AT E R S
I N T H E
N E W Y O R K M A R AT H O N

44. D ATA S T O RY T H I N K I N G

45. D ATA S T O RY T H I N K I N G

47. A P P R O A C H
Start with simple questions:
- How many participants?
- Outliers?
- Winners? Cheaters?
Dig deeper:
- Wie viele wurden disqualifiziert?
- Möglichkeit: U-Bahn nehmen
- Welche anderen Möglichkeiten?
- Weitergabe der Anmeldung

48. W H E N U S I N G D ATA :
G O F I N D A L A N M I L L E R
Source: Andy Lehren, The New York Times
Die Geschichte: Alan Miller

49. E X A M P L E :
O LY M P I C S
1 0 0
M E T E R
R A C E
unknown derivative work by Durova - derivative work of
Image:Jesse_Owens.jpg - reproduction of photograph in
"Die Olympischen Spiele, 1936" p.27, 1936.

50. http://en.wikipedia.org/wiki/
100_metres_at_the_Olympics

52. W I E I S T D I E G E S C H I C H T E
A U F G E B A U T ? PROTAGONIST

53. Wie viel? Gewinner nach Jahr und 100 Meter Zeit

54. Antagonist: Carl Lewis

55. Umrechnung: Rückstand in Metern auf Usain Bolt

56. Vergleich von 116 Medaillen-Gewinnern

57. Analyse: Gewinner nach Nationalität.

58. Vergleich: Wie schnell?

59. Überraschung: Laufzeiten heute (nach Alter)?

60. Ende: Differenz zwischen 1896 und 2012?
<— 3 Sekunden —>

61. http://konigi.com/book/sketch-book/why-we-sketch
From idea to completion: Not a straight line.

62. http://konigi.com/book/sketch-book/why-we-sketch
Geschichten skizzieren - Komplexität entwirren

63. http://konigi.com/book/sketch-book/why-we-sketch
Iterieren, formen, verdichten.

64. http://konigi.com/book/sketch-book/why-we-sketch
Persönlich machen: Protagonist als Spiegel/Bezugspunkt.

65. T O O L S

66. D AT E N :
E X C E L
G O O G L E S H E E T S
O P E N R E F I N E
S C R A P E R
F O R M E L N ( Z . B . I N F L AT I O N , A R B E I T S M I N U T E )
V I S U A L I S I E R E N :
D 3 . J S
J U I C E L A B S C H A R T C H O O S E R
D ATA W R A P P E R

73. Many rules and
recommendations built into the
tool.

74. Was unterscheidet den Datawrapper?
Sehr schnell
Open Source
Qualität der Diagramme
Optimiert für digitale Publikation

75. Example: Good data-to-ink ratio

76. One user, multiple publishing options for 30 days.
All charts published stay online. Full year at 30% lower
price.
Datawrapper Pro
Your own Datawrapper. On site server.
Full customization of data input,
visualizations and data output.
Newsrooms and workgroups.
All charts in one place.
Custom layout. Custom modules.
per month
500€per month
12€30 days
100€Full Year (-30%)

78. Datawrapper API

79. • Coming features:
• Scatterplot
• Tables
• Double Y-Axis
• Datawrapper Source

80. E X E R C I S E :
H O W T O S TA RT A
S T O RY Y O U R S E L F

81. https://medium.com/@tomcavill/3x3-d6202ef7d077
Use the 3x3 rule for better story structure.

82. W H AT ? W H Y ? H O W ?
https://medium.com/@tomcavill/3x3-d6202ef7d077

83. 3 X 3
W H AT W H Y H O W
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85. T H A N K Y O U