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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...
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 ...
@ 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 A...
B I G D ATA 

S M A L L D ATA
R E L E VA N T D ATA
W H AT I S
D ATA - D R I V E N
J O U R N A L I S M ?
DEF:
DATA-DRIVEN JOURNALISM
= WORKFLOW
‣ Tasks are to collect, clean, visualize and report
‣ Data-driven journalism is a p...
Data is a new camera
(Replace as many stock photos with charts as you can)
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 comparis...
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...
#ddj
examples
Edward Tufte: To be truthful and revealing, data graphics must bear on the

question at the heart of quantitative thinking...
http://www.businessinsider.com/the-future-of-digital-2013-2013-11?op=1
E X A M P L E
E X A M P L E
E X A M P L E
E X A M P L E
http://www.ft.com/intl/cms/s/2/1392ab72-64e2-11e4-ab2d-00144feabdc0.html?mc_cid=2e8df2f2e5&mc_eid=6dffebf6ef#axzz3TV8qJA8c
W H Y
D ATA - D R I V E N
J O U R N A L I S M ?
IN A DATA-DRIVEN WORLD NEWSROOMS
NEED TO ADAPT
‣ Online Publishing
‣ Data as a source of exclusive reporting positions
Frü...
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 …
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
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
A LIST OF REASONS FOR DATA-DRIVEN WORK:
‣ Help our readers to make important decisions in life:
Election, Economy, Educati...
BIG OPPORTUNITIES
Let people search less
Reduce the time they need to search
Help to make decisions
Detect big changes/iss...
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
GO THROUGH THE KEY QUESTIONS - FIVE W & ONE H
http://www.slideshare.net/stsanto/the-back-of-the-napkin-dan-roam
WHAT? (WHO...
– 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
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
http://www.heise.de/newsticker/meldung/Umsatzsteigerung-und-Gewinnrueckgang-bei-Amazon-1285944.html
T H E C A S E F O R C ...
Quelle: New York Times
S I M P L E B A R , B I G S T O RY
– 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
„Zitat hier eingeben.“
The project is an excellent example of journalists intervening to put
a largely neglected issue on ...
– 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...
A P P F O R R E A D E R S
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
Quelle: Alan McLean, NYT//Amsterdam, 2010
Quelle: Alan McLean, NYT//Amsterdam, 2010
1. Data Story 2. Data Special 3. Data App
Find Small datasets Complex data Big data
Clean Excel Open Refine Database
Visual...
TEAMS NEEDED
Journalisten/

Dokumentation
DeveloperDesigner
Ideas and Questions
Search and data verification
Project manage...
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
D ATA S T O RY T H I N K I N G
D ATA S T O RY T H I N K I N G
A P P R O A C H
Start with simple questions:
- How many participants?
- Outliers?
- Winners? Cheaters?
Dig deeper:
- Wie v...
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 M...
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_...
http://en.wikipedia.org/wiki/
100_metres_at_the_Olympics
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
Wie viel? Gewinner nach Jahr und 100 Meter Zeit
Antagonist: Carl Lewis
Umrechnung: Rückstand in Metern auf Usain Bolt
Vergleich von 116 Medaillen-Gewinnern
Analyse: Gewinner nach Nationalität.
Vergleich: Wie schnell?
Überraschung: Laufzeiten heute (nach Alter)?
Ende: Differenz zwischen 1896 und 2012?
<— 3 Sekunden —>
http://konigi.com/book/sketch-book/why-we-sketch
From idea to completion: Not a straight line.
http://konigi.com/book/sketch-book/why-we-sketch
Geschichten skizzieren - Komplexität entwirren
http://konigi.com/book/sketch-book/why-we-sketch
Iterieren, formen, verdichten.
http://konigi.com/book/sketch-book/why-we-sketch
Persönlich machen: Protagonist als Spiegel/Bezugspunkt.
T O O L S
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 ...
Many rules and
recommendations built into the
tool.
Was unterscheidet den Datawrapper?
Sehr schnell
Open Source
Qualität der Diagramme
Optimiert für digitale Publikation
Example: Good data-to-ink ratio
One user, multiple publishing options for 30 days.
All charts published stay online. Full year at 30% lower
price.
Datawra...
Datawrapper API
• Coming features:
• Scatterplot
• Tables
• Double Y-Axis
• Datawrapper Source
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
https://medium.com/@tomcavill/3x3-d6202ef7d077
Use the 3x3 rule for better story structure.
W H AT ? W H Y ? H O W ?
https://medium.com/@tomcavill/3x3-d6202ef7d077
3 X 3
W H AT W H Y H O W
_ _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _
_ _ _ _...
T H A N K Y O U
Mirko Lorenz Data Driven Journalism Overview Seminar Ordine dei Giornalisti del Veneto European Journalism Center 14 apr 2...
Mirko Lorenz Data Driven Journalism Overview Seminar Ordine dei Giornalisti del Veneto European Journalism Center 14 apr 2...
Mirko Lorenz Data Driven Journalism Overview Seminar Ordine dei Giornalisti del Veneto European Journalism Center 14 apr 2...
Mirko Lorenz Data Driven Journalism Overview Seminar Ordine dei Giornalisti del Veneto European Journalism Center 14 apr 2...
Mirko Lorenz Data Driven Journalism Overview Seminar Ordine dei Giornalisti del Veneto European Journalism Center 14 apr 2...
Mirko Lorenz Data Driven Journalism Overview Seminar Ordine dei Giornalisti del Veneto European Journalism Center 14 apr 2...
Mirko Lorenz Data Driven Journalism Overview Seminar Ordine dei Giornalisti del Veneto European Journalism Center 14 apr 2...
Mirko Lorenz Data Driven Journalism Overview Seminar Ordine dei Giornalisti del Veneto European Journalism Center 14 apr 2...
Mirko Lorenz Data Driven Journalism Overview Seminar Ordine dei Giornalisti del Veneto European Journalism Center 14 apr 2...
Mirko Lorenz Data Driven Journalism Overview Seminar Ordine dei Giornalisti del Veneto European Journalism Center 14 apr 2...
Mirko Lorenz Data Driven Journalism Overview Seminar Ordine dei Giornalisti del Veneto European Journalism Center 14 apr 2...
Mirko Lorenz Data Driven Journalism Overview Seminar Ordine dei Giornalisti del Veneto European Journalism Center 14 apr 2...
Mirko Lorenz Data Driven Journalism Overview Seminar Ordine dei Giornalisti del Veneto European Journalism Center 14 apr 2...
Mirko Lorenz Data Driven Journalism Overview Seminar Ordine dei Giornalisti del Veneto European Journalism Center 14 apr 2...
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Mirko Lorenz Data Driven Journalism Overview Seminar Ordine dei Giornalisti del Veneto European Journalism Center 14 apr 2015 #DDJ

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

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Mirko Lorenz Data Driven Journalism Overview Seminar Ordine dei Giornalisti del Veneto European Journalism Center 14 apr 2015 #DDJ

  1. 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. 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. 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
  4. 4. B I G D ATA 
 S M A L L D ATA R E L E VA N T D ATA
  5. 5. W H AT I S D ATA - D R I V E N J O U R N A L I S M ?
  6. 6. DEF: DATA-DRIVEN JOURNALISM = WORKFLOW ‣ Tasks are to collect, clean, visualize and report ‣ Data-driven journalism is a process
  7. 7. Data is a new camera (Replace as many stock photos with charts as you can)
  8. 8. 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
  9. 9. 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)
  10. 10. #ddj examples
  11. 11. 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?
  12. 12. http://www.businessinsider.com/the-future-of-digital-2013-2013-11?op=1 E X A M P L E
  13. 13. E X A M P L E
  14. 14. E X A M P L E
  15. 15. E X A M P L E
  16. 16. http://www.ft.com/intl/cms/s/2/1392ab72-64e2-11e4-ab2d-00144feabdc0.html?mc_cid=2e8df2f2e5&mc_eid=6dffebf6ef#axzz3TV8qJA8c
  17. 17. W H Y D ATA - D R I V E N J O U R N A L I S M ?
  18. 18. 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
  19. 19. 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 …
  20. 20. 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
  21. 21. 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
  22. 22. 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
  23. 23. 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
  24. 24. 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
  25. 25. 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)
  26. 26. – 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
  27. 27. 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
  28. 28. 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 …
  29. 29. Quelle: New York Times S I M P L E B A R , B I G S T O RY
  30. 30. – 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
  31. 31. „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
  32. 32. – 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
  33. 33. A P P F O R R E A D E R S
  34. 34. 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
  35. 35. Quelle: Alan McLean, NYT//Amsterdam, 2010
  36. 36. Quelle: Alan McLean, NYT//Amsterdam, 2010
  37. 37. 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
  38. 38. 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
  39. 39. 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
  40. 40. D ATA S T O RY T H I N K I N G
  41. 41. D ATA S T O RY T H I N K I N G
  42. 42. 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
  43. 43. 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
  44. 44. 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.
  45. 45. http://en.wikipedia.org/wiki/ 100_metres_at_the_Olympics
  46. 46. 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
  47. 47. Wie viel? Gewinner nach Jahr und 100 Meter Zeit
  48. 48. Antagonist: Carl Lewis
  49. 49. Umrechnung: Rückstand in Metern auf Usain Bolt
  50. 50. Vergleich von 116 Medaillen-Gewinnern
  51. 51. Analyse: Gewinner nach Nationalität.
  52. 52. Vergleich: Wie schnell?
  53. 53. Überraschung: Laufzeiten heute (nach Alter)?
  54. 54. Ende: Differenz zwischen 1896 und 2012? <— 3 Sekunden —>
  55. 55. http://konigi.com/book/sketch-book/why-we-sketch From idea to completion: Not a straight line.
  56. 56. http://konigi.com/book/sketch-book/why-we-sketch Geschichten skizzieren - Komplexität entwirren
  57. 57. http://konigi.com/book/sketch-book/why-we-sketch Iterieren, formen, verdichten.
  58. 58. http://konigi.com/book/sketch-book/why-we-sketch Persönlich machen: Protagonist als Spiegel/Bezugspunkt.
  59. 59. T O O L S
  60. 60. 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
  61. 61. Many rules and recommendations built into the tool.
  62. 62. Was unterscheidet den Datawrapper? Sehr schnell Open Source Qualität der Diagramme Optimiert für digitale Publikation
  63. 63. Example: Good data-to-ink ratio
  64. 64. 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%)
  65. 65. Datawrapper API
  66. 66. • Coming features: • Scatterplot • Tables • Double Y-Axis • Datawrapper Source
  67. 67. 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
  68. 68. https://medium.com/@tomcavill/3x3-d6202ef7d077 Use the 3x3 rule for better story structure.
  69. 69. W H AT ? W H Y ? H O W ? https://medium.com/@tomcavill/3x3-d6202ef7d077
  70. 70. 3 X 3 W H AT W H Y H O W _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
  71. 71. T H A N K Y O U

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