1. OKCON 2011
OPEN DATA IN THE NEWSROOM
WHAT‘S THE
STORY?
Mirko Lorenz • Information Architect/Journalist • Deutsche Welle/Innovation Projects
June 30, 2011 • Kalkscheune Berlin

2. THIS IS ALL
BETA
needs work

3. WELCOME.
IF YOU ARE NEW, THIS IS THE CONTEXT
This is a presentation from at OK Con 2011 in Berlin.
The topic is „data-driven journalism“ (DDJ).
Our idea of data-driven journalism is to view the analysis of
data as a rich source for good stories.
Data-driven journalism is inclusive, meaning that other forms of
journalism (investigative, computer-assisted reporting) are
surely part of the broader quest to find ways for relevant,
insightful journalism in a world driven by data.
Plus, we hope that by adding data to the newsroom there will
be new models to finance journalism. And why not, if the story
is good.
Data-driven journalism @ Wikipedia:
http://en.wikipedia.org/wiki/Data_driven_journalism

4. ME: TURNED
JOURNALIST
INFORMATION ARCHITECT
FOCUS: TURNING IDEAS INTO
PLATFORMS, WORKFLOWS, STORIES
WORKING@DEUTSCHE WELLE
INNOVATION PROJECTS
JOURNALISM TRAINER

5. DATA IN THE NEWSROOM?

6. DATA IN THE NEWSROOM?
NOW: OPEN DATA IS A WINNER

7. DATA IN THE NEWSROOM?
NOW: OPEN DATA IS A WINNER
NEXT: CHALLENGES AHEAD

8. DATA IN THE NEWSROOM?
NOW: OPEN DATA IS A WINNER
NEXT: CHALLENGES AHEAD
FUTURE: PATTERN CHANGE?

9. #1:
OPEN DATA IS A WINNER

10. WE ALL
NEED THE
DATA

11. THE IDEA ACCEPTED
ON HIGH LEVELS

12. DATA INSIGHTS FOR EVERYONE?
WORLD
COUNTRY
PEOPLE CITY
GROUP
OPEN + NOT-SO-OPEN DATA

13. DATA INSIGHTS FOR EVERYONE?
MEDIA
WORLD
COUNTRY
PEOPLE CITY
GROUP
OPEN + NOT-SO-OPEN DATA

14. #2:
DATA-DRIVEN JOURNALISM:
WHY BOTHER?

15. OBSERVATIONS:
MUCH PROGRESS LAST YEAR
SIMON ROGERS IS AN OUTLIER
NEED TO TRAIN JOURNALISTS
WHAT ARE LONG-TERM GOALS?

16. ON A GLOWING RECTANGLE NEAR YOU
THE
MOVIE? DATA STORY
DIGGING DEEPER
„Surprisingly
helpful“
-- User
„Our city is now a better place“
-- Local Authority
„It‘s a wonder how we could do without it“
-- Government
AN OPEN DATA PRODUCTION

17. CHALLENGES:
DIRTY DATA
DATA NEEDS STORY
CREATING A NEW „CAMERA“

18. HOW DIRTY IS THE DATA?
Tar Sands of Alberta. Image: Some rights reserved by NWFblogs

19. HOW DIRTY IS THE DATA?
EXTRACTING MEANING FROM DATA WILL BE HARD WORK
WON‘T COME AT ZERO-COST OR WITHOUT UNWANTED EFFECTS
Tar Sands of Alberta. Image: Some rights reserved by NWFblogs

20. NOT ALL DATA-SETS CREATED EQUAL
INCOMPLETE WRONG
FOCUS COMPETITION
WRONG
FORMAT
CREATED FOR
DIRTY
Text MARKETING,
MISTAKES DATA NOT INSIGHTS
WHEN
COLLECTED INTENTIONALLY
FORGED
INSIGHTS KEPT SECRET

21. Journalists,
where do you
add value?
- Jeff Jarvis

22. DATA + STORY = VALUE TO PUBLIC

23. DATA + STORY: EXAMPLE
Source: Information is Beautiful, by David McCandless

24. A „NEW CAMERA“ TO VIEW THE WORLD
Source: Information is Beautiful, by David McCandless,
quoted from talk of David in Brussels @ Visualizing Europe, 2011

25. A „NEW CAMERA“ TO VIEW THE WORLD
Source: Information is Beautiful, by David McCandless,
quoted from talk of David in Brussels @ Visualizing Europe, 2011

26. DATA + STORY
„NEVER COMING HOME“
Source: http://mediastorm.com//publication/never-coming-home

27. #3:
WHAT‘S KEEPING US?
BARRIERS FOR DATA
JOURNALISM

28. REBUILDING THE NEWSROOM -
WHY AND HOW?

29. TODAY‘S MEDIA MODELS
Print Web Blogs *
iPad Mobile
Most media companies
still think of data and
digital formats as an
extension, not as the
foundation.
*Blogs are red, because most media did not get the idea

30. FROM SINGLE STORIES...

31. ...TO DATA-BASED CONTEXT

32. DATA AS A FOUNDATION
Database Web Print
Overview
Article
Depth
Articles
Crime Maps
Dossier Competency
Background
Data Statistics
Context Relevance
Analysis
Audio Trustability
Photo
Video

33. SITUATION OF JOURNALISTS

34. MOST JOURNALIST‘S SITUATION
NO ACCESS TO
FAILING
SERVER
MODELS
NO SUPPORT
RISING NO
Text
WORKLOAD
TRAINING TOO MANY
DATA TOOLS
TIME
PRESSURE OLD-SCHOOL
CMS

35. STRANGE TIMES:
LAY-OFFS IN NEWSROOMS VS.
TALENT SHORTAGE IN DIGITAL
BUSINESS

36. #4:
DEVELOPING THE FIELD

37. WHAT ARE THE TRAINING NEEDS?

38. WHAT WOULD BE A GOOD WORKFLOW?
A workflow for video and data journalism using next-gen cloud storage
Team Ingestion Filter Produce Publish Distribute Popularity
Goals
Teamwork vs. one-man-show
Enabling use of data in newsrooms
Workflow as driver of value, better integration
Note: Deutsche Welle is a partner in this EU-project. Website here: http://www.visioncloud.eu
Visualization inspired by „A Web Site Designed“, created by John Furness of Simple Square

39. WHAT WOULD BE A GOOD WORKFLOW?
VISION Cloud Media Use Case
A workflow for video and data journalism using next-gen cloud storage
Team Ingestion Filter Produce Publish Distribute Popularity
Goals
Teamwork vs. one-man-show
Enabling use of data in newsrooms
Workflow as driver of value, better integration
Note: Deutsche Welle is a partner in this EU-project. Website here: http://www.visioncloud.eu
Visualization inspired by „A Web Site Designed“, created by John Furness of Simple Square

40. Text
WHAT ARE THE STEPS?
Team Ingest Filter Produce Publish Distribute Measure
Note: Deutsche Welle is a partner in this EU-project. Website here: http://www.visioncloud.eu
Visualization inspired by „A Web Site Designed“, created by John Furness of Simple Square

41. SMART INTERACTION
Scraperwiki Open Calais Video editing Data Visualization Tools Story Presentation Multi-platform Collecting
STORLETS Needlebase Google Refine Final Cut R Studio Storify Wordpress Distribution usage data over time,
Protovis Document Cloud Memolane File Conversion based on
& APIs Fullstory Flash files, not pages
Gapminder
Other CMS in use HTML5
iTunes
VIDEO/DATA
VIDEO/DATA PRODUCTION WITH TEAMS iPod
FILTERING
INGESTION Android
METADATA
SET-UP Nokia
TEAM RIM
PROJECT NAME
Team Ingestion Filter/Metadata Produce video oder data story Publish Multi-platform distribution Popularity
FIND SNIPPETS OF VIDEO/DATA FOR RE-USE
ING F
RAIS TION O
C NAL DAT
STORAGE TRA E ) ATIO A MO
ABS TORAG SULES ADVANCED CAPABILITIES
FOR CLOUD-BASED STOR
PUT GE
COM ORA FED BILITY A
ACCESS S
AC AP AGE ST ERA
TION ND
RIGHTS DAT
(M ETA
SECURITY
CLOUD PURE STORAGE LAYER - KEEPING FILES AND VERSIONS OF FILES OVER TIME
Visualization inspired by „A Web Site Designed“, created by John Furness of Simple Square

42. #5:
PATTERN CHANGE

43. Q: PATTERN CHANGE?
A: LOOKING INTO THE DATA,
USING WHAT WE FIND FOR
GOOD PURPOSES
CAN RESULT IN A BIG SHIFT OF
POWER

44. QUIZ:
WHAT ARE AR THE INSIGHTS &
TOOLS FUTURE GENERATIONS
WILL THINK ARE OBVIOUS?

45. WHAT CAN BE DONE? (MOST EXAMPLES ON THIS PAGE ARE LINKED)
SURPRISE
CLARITY
TRUST

46. #HOPE:
MEDIA COMPANIES BECOMING
TRUSTED DATA HUBS

47. THE FUTURE: INSTANT CLARITY BEFORE WE MAKE DECISIONS
Source: Visua.ly

48. THE FUTURE: INSTANT CLARITY BEFORE WE MAKE DECISIONS
Source: Visua.ly

49. THE FUTURE: INSTANT CLARITY BEFORE WE MAKE DECISIONS
Source: Visua.ly

50. FINALLY: THE CALL TO ACTION
(1) LET‘S WORK TOGETHER
(2) DEVELOPERS/JOURNALISTS/VISUALIZERS
(3) SMALL TEAMS CAN ROCK THIS PLACE

51. Caution:
There’s no standard
career path to become
a deconstructor of
wrongness
Quote from David H. Freedmann, „Wrong“, Review of Book http://www.nytimes.com/2010/06/11/books/excerpt-wrong.html

52. THANK YOU!
Brian Storm Simon Rogers Walter Mossberg Rafat Ali Ariana Huffington
Aron Pilhofer Jeff Jarvis Nicolas Kayser-Bril Michelle Minkoff Mercedes Bunz
Mathias Eberl Lorenz Matzat Tracy Boyer Paul Bradshaw Amanda Cox

53. HOUSEKEEPING: HOW TO CONNECT
Twitter: #ddj
Please use this hashtag for „data-driven journalism“ - it‘s nicely short
Survey:
The survey to find out about training needs for data journalists is still open
(as of July 2011)
http://www.surveymonkey.com/s/data_journalism
Data-driven journalism group @ EJC
+150 people discussing and developing.
http://community.ejc.net/group/datadrivenjournalism
Data-driven journalism website
http://datadrivenjournalism.net/
My website:
http://www.mirkolorenz.com

54. IDEAS, DISCUSSION, ARGUE?
TWITTER: #DDJ
@MIRKOLORENZ
Some ideas and concepts presented here have received funding from the
European Community's Seventh FrameworkProgramme (FP7/2007-2013)
under grant agreement n° 257019 (VISION Cloud)