The presentation by Tom Johnson discusses the evolution of journalism from traditional methods to analytic journalism, emphasizing the shift from narrative-based reporting to data-driven analysis. It outlines how the digital transformation has democratized access to information and changed the role of journalists to that of data analysts. Johnson advocates for higher standards in journalism education and the incorporation of collaboration tools to better equip journalists in the digital age.

1. "Analytic Journalism:
Digital Evolution in the
Datasphere"
Tom Johnson
Managing Director
Inst. for Analytic Journalism
Santa Fe, New Mexico USA
tom@jtjohnson.com

2. Presentation to the
South African National Editors Forum
22 November 2010
Johannesburg, South Africa

3. Today’s conversation
• Journalism past [briefly] and present
• What is Analytic Journalism
• Where we think journalism is (should be?)
heading
3

4. What’s Changed in Journalism?
• Old:
“News” = spoken word, text, images
• Today: “News” = Data = 1s & 0s
• Global ubiquity and fluidity of data
• 5 billion cell phones [July ’10]
• Africa: 1/3 pop. have cell phones
• Metadata
• Editors  Database administrators
• Is the Mass Media dead?
4

5. What’s Changed in Journalism?
• Datasphere Basic Process:
Data In  Analysis  Info Out
• Former “basic process”
– Superficial research
– Gather anecdotes & quotes & AV
– Write, i.e. String together in “acceptable
form”
– Publish/broadcast soonest
– Do it again tomorrow
5

6. What’s Changed in Journalism
Industrial Age Journalism
– High capital investment: presses, paper, delivery,
broadcasting equipment
– Low investment in “wet ware,” i.e. employees and
their skills
Digital Age Journalism
• Low capital threshold for data, analytic tools,
“publishing”
• High educational threshold for data, analytic
tools, “publishing”
• Power shift from authority and institutional
power to sub-groups and individuals

7. What’s Changed in Journalism?
• Constant deadlines, but …
• Breaking news has already broken
• If people already have the “news,” what do
we have to contribute?
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8. What is “Analytic Journalism”
Traditional Journalism
•Quick & Dirty research
•Gather quotes
•Harvest anecdotes
•Get some video
•String together in a 30,
60, 90-sec story
•Repeat tomorrow
8
Analytic Journalism
•Not events, but context & trends
•Define phenomena of interest as a
SYSTEM
•Employ RRAW-P process
Research  Reporting Analysis 
Write  Publish”‟
•Non-traditional analytic tools:
GIS, data mining, forensic
accounting, Social Network Analysis

9. Cornerstones of IAJ
• General Systems Theory
• Statistics
• Data visualization
- www.niceone.org/lab/refugees/
• Simulations
- Simtable Fire Model
- St. Marks Square
- Venice boats
- Zozobra park

10. Cornerstones: IAJ version of Analytic
Journalism
• General Systems Theory
1. System boundaries
2. Variables
3. Relationships between variables
4. System has goals
5. System capable of “learning”
6. System exists in an environment
• “System” can be self defined; can be
defined by outside observer(s)
• System is “scaleable”
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11. Newspaper as a system

12. Theory of Journalistic Process
12
Data In Analysis  Info
Out

13. Theory. It’s important, valuable.
13
Data In  Analysis  Info Out
•Notes
•Text
•Numeric
•Images
•Charts/Graphs
•Maps
•Audio
•Video
•Atoms  Bits
•How? Who?

14. Theory. It’s important, valuable.
14
Data In  Analysis  Info
Out• Notes
• Text
• Numeric
• Images
• Charts/Graphs
• Maps
• Audio
• Video
• AtomsBits
How? Who?
• What are we looking
for? How can we be
surprised?
• Source
• Definition
• Context
• Estimating
• Counting
• Statistical
• Geostatistical
• Social Network Analysis
• Forensic accounting

15. Theory. It’s important, valuable.
15
Data In  Analysis  Info
Out• Notes
• Text
• Numeric
• Images
• Charts/Graphs
• Maps
• Audio
• Video
• AtomsBits
How?
• What are we
looking for? How
can we be
surprised?
• Source
• Definition
• Context
• Estimating
• Counting
• Statistical
• Geostatistical
• Social Network
Analysis
• Forensic
accounting
• Broadcast
• Web
• Audio
• Video
• Text
• Data visualization
• Maps
• Dynamic databases
• Archives

16. Theory. It’s important, valuable.
16
Data In  Analysis  Info
Out
• Notes
• Text
• Numeric
• Images
• Charts/Graphs
• Maps
• Audio
• Video
• AtomsBits
How?
• What are we
looking for? How
can we be
surprised?
• Source
• Definition
• Context
• Estimating
• Counting
• Statistical
• Geostatistical
• Social Network
Analysis
• Forensic
accounting
• Broadcast
• Web
• Audio
• Video
• Text
• Data visualization
• Maps
• Dynamic databases
• Archives
Contributed content
Contributed content
& analysis

17. Developing Tools for Collaboration
• Who is using wikis? Google Docs? Zoho?
• Intra-nets
• Inter- and intra-nets

18. Goals for Journalism Education
• Raise basic admission standards
– Applicants already can write; can do basic arithmetic
• Develop/improve tools for newsroom and citizen
journalism collaboration
– Intranets
– Wikis
– Google Docs
– Blogs, social media, Flickr, etc.
– Research customization for journalism
• Develop tools for story telling

19. Community & Collaboration
• Citizen Journalism and Democracy
in Africa:
“Periodismo Ciudadano y Democracia en África”: Un libro en línea de
Fackson Banda
• UK: The Guardian uses the citizen
journalist to great effect in
Expenses Scandal
• Investigate your MP’s expenses
• Oldweather.org Oldweather.org
• NYTimes Knowledge Network
El New
York Times ofrece un curso online de periodismo ciudadano
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20. Online learning; online tools
• ICFJ Anywhere
Global source for high-quality, online journalism courses
taught by seasoned professionals.
• Google Refine
Google Refine is a power tool for working with messy
data, cleaning it up, transforming it from one format into
another, extending it with web services, and linking it to
databases like Freebase.
• Many Eyes
Site is set up to allow the internet community to upload
data, visualize it, and talk about their discoveries with
other people.
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21. Storytelling R&D? Innovation?
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22. Storytelling R&D? Innovation?
22
• Potential for “drill-down” dynamics
• Reach URLs
• Show data for statistical analysis
• Ask questions; capture answers

23. Tomorrow?
• Our job is to “monitor the centres of
power.”
-- Amira Haassaid
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24. Tomorrow?
• In a democracy, news media
harvest data; become central
warehouse of citizen’s data
• A “public library” on steroids
• Digital resources for in-house
journalists
• Create “dashboards” to monitor gov’t.
• Potential to create specialized, non-
journalism “products”
• Provide data to app creators

25. Tomorrow?
• Paying for news rapidly declining
market.
• So what can journalists offer?
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26. "Analytic Journalism:
Digital Evolution in the
Datasphere"
Tom Johnson
Managing Director
Inst. for Analytic Journalism
Santa Fe, New Mexico USA
tom@jtjohnson.com

27. Theory. It’s important, it’s valuable.
27
Nothing is as valuable as a
good theory.