enlightenment 2.0: a new era of collaboration and storytelling with data
DESCRIPTION
Presentation to Gartner Business Intelligence Summit, May 3, 2011TRANSCRIPT
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A New Era of Collaboration and Storytelling with Data
Geoff McGheeGartner Business Intelligence Summit, Los AngelesMay 3, 2011
Enlightenment 2.0
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http://www.stanford.edu/group/toolingup/rplviz/
Visualizing the Enlightenment
“Mapping the Republic of Letters,” Stanford University (2009)
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http://www.stanford.edu/group/toolingup/rplviz/
Visualizing the Enlightenment
“Mapping the Republic of Letters,” Stanford University (2009)
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• Great age of inquiry and discovery, from roughly 1680 to 1820
• Triumph of evidence based, empirical reasoning
• Collaboration and communication across borders, disciplines
• Self-sustaining/accelerating as insight begets more insight
Enlightenment 1.0What Was the Age of Enlightenment?
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“Mankind’s final coming of age, the emancipation of the human consciousness from an immature state of ignorance and error.”
– Immanuel Kant
Enlightenment 1.0
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• Development of scientific method
• Aftermath of blockbuster discoveries, from Galileo to Isaac Newton
• Exploration of the world
• Growth of American colonies, path to independence and self determination
• End of Thirty Years’ War
Origins of the Enlightenment
Enlightenment 1.0What Was the Age of Enlightenment?
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Interaction Across DisciplinesPhilosophers, essayists, botanists, mathematicians, economists, composers grappling individually – and together – with revolutionary ideas, inventions and movements.
Enlightenment 1.0What Was the Age of Enlightenment?
John Locke
Thomas Paine
Adam Smith
Thomas Jefferson
Jean-Jacques Rousseau Benjamin Franklin
Voltaire Carl Linnaeus
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What about today?
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• Explosion of electronic data collection and storage
• Moore’s law: increasingly powerful processors, including GPUs
• Digitization of media (photos, music, books...)
• Remote sensors, RFID tags, POS systems
• Cheap storage
• Data formats (XML, JSON, RDF… ), APIs
• Social media, open source, crowdsourcing
Conditions for a “Second Enlightenment”
Enlightenment 2.0?
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• Finding meaning in a flood of dataEstimated 5 exabytes a day… in 2003!
• Increasing velocity of decision-making, advent of real-time (or close) data
• Responsible use of powerful information technology: smartphone tracking, facial recognition, other data collection methods
• Government, business and individuals rethinking long-held habits in light of new, actionable information
Enlightenment 2.0?
Challenges of the 21st Century
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“We are confronted today with insurmountable opportunities.”
– Walt Kelly, Pogo
Enlightenment 2.0?
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Enlightenment 1.0
Coffee houses as gathering places
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Enlightenment 2.0?
“Laptopistan”
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Physicist, Inventor of the Hypertext ProtocolTim Berners-Lee
• Envisioned the World Wide Web as a tool for sharing data
• Published influential article, “the semantic web,” in 2001
• End result of a “semantic web” – machine-readable documents, all the web’s content as (or convertible into) structured data
Enlightenment 2.0?Heroes of a Second EnlightenmentData Journalism“Programmer Journalists”
Rise of programmer-journalists who “know their CSV
from their RDF, can throw together some quick
MySQL queries for a PHP or Python output … and
discover the story lurking in datasets released by
governments, local authorities, agencies, or any
combination of them – even across national
borders.”
“Journalists need to be data-savvy. It used to be that you would get stories by chatting to people in bars...”
“But now it's also going to be about poring over data and equipping yourself with the tools to analyse it and picking out what's interesting. And keeping it in perspective, helping people out by really seeing where it all fits together, and what's going on in the country.” – Tim Berners-Lee, Nov. 2010
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Computer Scientists, Data Visualization PioneersBen Shneiderman, Jock Mackinlay
Developed science of human-computer interaction, built on principles of information graphics going back to 1700s and early works of Charles Minard, Edward Playfair.
Also adapted rules from Jacques Bertin’s “semiology of graphics,” left, to computer visualization
Heroes of a Second EnlightenmentEnlightenment 2.0?
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Programmers, ArtistsBen Fry and Casey Reas
• Developed Processing, Java-based tool for authoring generative art and data visualizations
• Popular with computer scientists AND artists
• Instrumental in making visualization sexy
Heroes of a Second EnlightenmentEnlightenment 2.0?
Processing.org
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Chief Economist, GoogleHal Varian
“I keep saying the sexy job in the next ten years will be statisticians…. The ability to take data - to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it’s going to be a hugely important skill in the next decades.”
– Interview with McKinsey Quarterly
Heroes of a Second EnlightenmentEnlightenment 2.0?
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Public Health ResearcherHans Rosling
Heroes of a Second EnlightenmentEnlightenment 2.0?
The Joy of Stats on BBC Television
• Known for exuberant TED Talks on human development statistics over time
• Model for narrative data storytelling with heart – Jacques Cousteau of data?
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160-Year-Old Newspaper CompanyThe New York Times
• Using visualization for news
• Tapping into academic and scientific literature
• Developing new gold standard for narrative vis
Enlightenment 2.0?Heroes of a Second Enlightenment
How Different Groups Spend Their Day (2009)
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The Unknown Coder
Heroes of a Second EnlightenmentEnlightenment 2.0?
• People who contribute countless hours to open source projects from Open Street Maps to Wikipedia to Linux OS to… to…
• Hackers and tinkerers who develop and share tools to make high-end data processing and visualization possible for little or no cost
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What Does This Enlightenment Look Like?
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Everyday Use of Data to Make DecisionsEnlightenment 2.0?
Maps, GPS with live traffic
Donation Dashboard
Farecast search results
Greatschools.org
Zillow, Trulia, real estate search engines
SF Park
= We’re learning things that used to be unknown, unknowable or impractical to know.
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Twitter visualizations Google Public Data Explorer
But also larger insights about society ...
Politifact Obameter U.S. State Department: “Opinion Space”
Enlightenment 2.0?
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Google N-Grams Baby Name Voyager
… and new insights about the past.
Gapminder: Human Development Over Time Immigration Explorer
Enlightenment 2.0?
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Enlightenment 2.0?
Mapping, Text Mining Texas Newpaper Archives “Personal Annual Report”
Data Vis For, and By Everyone
LinkedIn Maps Assessing NFL Draft Picks Through their Words
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The Challenge of Data
We are mining information to find insights and make decisions... But how do we do this at such a large scale? As individuals? As companies?
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• Bypass language centers, go direct to the visual cortex
• Leverage ability to recognize patterns, visual sense-making
• Powerful graphics chips enable animation, live data processing possible
The Promise of Data Visualization
Using the Eye-Brain Connection
Map of New Brainland by Unit Seven via Flickr
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http://flightpatterns.com
The Promise of Data Visualization
“Flight Patterns,” Aaron Koblin (2005)
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http://hint.fm/projects/flickr/
“Flickr Flow,” Fernanda Viégas and Martin Wattenberg (2009)
The Promise of Data Visualization
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http://blog.blprnt.com/blog/blprnt/goodmorning
“Good Morning,” Jer Thorp (2009)
The Promise of Data Visualization
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The Challenge of Data Visualization
“God Mode” visualizations keep the data at arm’s length. Why? Production time, technology used to produce them.
How do we get closer? How do we provide texture and story?
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Journey of Discovery
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10 Years in Infographics and Multimedia
ABC News, 1999-2000The New York Times, 2000-2008
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10 Years in Infographics and Multimedia
Le Monde, 2008-2009
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Study Year at Stanford
2009-2010 John S. Knight Journalism Fellowship
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Study Year at Stanford
2009-2010 John S. Knight Journalism Fellowship
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20-odd people in journalism, academia, research and art
Interviews About Data Visualization
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Video Documentary
datajournalism.stanford.edu
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Podcast Available on iTunes
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Video Documentary
How are journalists using data vis?Question:
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“Amanda studied stats in college and she's brought both a little bit more of a focus on data to the department and also just sort of an amazing ability to process data.”
New Players
Data Visualization in Journalism
Budget Forecasts, Compared with Reality (2009)
All of Inflation’s Little Parts (2009)
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“We have some open positions right now, and I would love for those positions to be geared towards somebody that had that skill set: ‘Here’s what the data is and the story is, here’s how we’d want to shape the story based on this data.’”
Re-tooling
Data Visualization in Journalism
Seeds of Recovery (2008)
Stimulus Tracker (2008)
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Re-tooling
“We've changed the focus of our team to concentrate more on database journalism.”
Data Visualization in Journalism
Traffic Deaths (2009)
Afghan Casualty Tracker (2008)
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Collaboration
“What you see is this incredibly fluid flow of ideas, and to me that’s really what's exciting. Maybe 15 years ago, there were these very distinct silos and people in completely different fields had no idea–everyone was working toward the same thing but today there's starting to fuse, it’s fun, it’s competitive, well look around all the time, where’s the next amazing thing going to come from?”
Data Visualization in Journalism
Map of the Market (1998)
Timeflow, an analytical timeline for journalists (2010)
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Enlightenment?
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Journalism/Communications
Humanities
ComputerScience/
HCI
Natural Sciences/Engineering
Social Sciences
Collaborations
Bill Lane CenterFor the American West
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“People come up to me all the time and ask, what’s with this data visualization thing?”
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• Follow these three blogs (via RSS, email, Flipboard etc), you’ll catch virtually everything interesting that comes along
• Cover visualization for news, science, marketing, art
Cross-Disciplinary CommunicationVis Blogs: The Virtual Water Cooler
Infosthetics.com Visual Complexity Flowing Data
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Excitement
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Visualization as a Medium?
“I think that there's a sense that data is something more like a medium, and something that can be used to tell stories, and to do all of the things that a medium can do, to delight and inspire...”
“I think that there's a sense that data is something more like a medium, and something that can be used to tell stories, and to do all of the things that a medium can do, to delight and inspire...”
Visualization as a Communication Medium?
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Communication Medium?
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How do we tell stories with data?
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How do we approach data in an enlightened way?
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Data StorytellingFrom Discovery to Communication
Discovery/Acquisition Analysis Visualization
Traditional Approach
Enterprise DBSpreadsheetExternal Source
Office Productivity AppSPSS...
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Data StorytellingFrom Discovery to Communication
Discovery/Acquisition
Cleaning/“Munging”
Analysis/ExploratoryVisualization
Iteration Publication
“Enlightened” Approach
Enterprise DBSpreadsheetExternal Source
Google RefineData Wrangler
TableauSpotfireMS OfficeGephiCognosFusion TablesR
Static Image orInteractive app:FlashProcessingHTML5
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Data StorytellingFrom Discovery to Communication
Discovery/Acquisition
Cleaning/“Munging”
Analysis/ExploratoryVisualization
Iteration Publication
“Enlightened” Approach
Enterprise DBSpreadsheetExternal Source
Google RefineData Wrangler
TableauSpotfireMS OfficeGephiCognosFusion TablesR
Static Image orInteractive app:FlashProcessingHTML5
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Data StorytellingFrom Discovery to Communication
Discovery/Acquisition
Cleaning/“Munging”
Analysis/ExploratoryVisualization
Iteration Publication
“Enlightened” Approach
Enterprise DBSpreadsheetExternal Source
Google RefineData Wrangler
TableauSpotfireMS OfficeGephiCognosFusion TablesR
Static Image orInteractive app:FlashProcessingHTML5
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Data StorytellingFrom Discovery to Communication
Discovery/Acquisition
Cleaning/“Munging”
Analysis/ExploratoryVisualization
Iteration Publication
“Enlightened” Approach
Enterprise DBSpreadsheetExternal Source
Google RefineData Wrangler
TableauSpotfireMS OfficeGephiCognosFusion TablesR
Static Image orInteractive app:FlashProcessingHTML5
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Data StorytellingFrom Discovery to Communication
Cleaning/“Munging”
Analysis/ExploratoryVisualization
Iteration Publication
“Enlightened” Approach
TableauSpotfireMS OfficeGephiCognosFusion TablesR
Static Image orInteractive app:FlashProcessingHTML5
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Data StorytellingFrom Discovery to Communication
Cleaning/“Munging”
Analysis/ExploratoryVisualization
Iteration Publication Feedback CollectiveExploration
“Enlightened” Approach
TableauSpotfireMS OfficeGephiCognosFusion TablesR
Static Image orInteractive app:FlashProcessingHTML5
User Testing Comment Threads
Save state/BookmarkIn-Context CommentsSharing
![Page 75: Enlightenment 2.0: A New Era of Collaboration and Storytelling with Data](https://reader033.vdocuments.net/reader033/viewer/2022061200/54771d51b4af9f33218b45a3/html5/thumbnails/75.jpg)
Data StorytellingFrom Discovery to Communication
Cleaning/“Munging”
Analysis/ExploratoryVisualization
Iteration Publication Feedback CollectiveExploration
TableauSpotfireMS OfficeGephiCognosFusion TablesR
Static Image orInteractive app:FlashProcessingHTML5
User Testing Comment Threads
Save state/BookmarkIn-Context CommentsSharing
![Page 76: Enlightenment 2.0: A New Era of Collaboration and Storytelling with Data](https://reader033.vdocuments.net/reader033/viewer/2022061200/54771d51b4af9f33218b45a3/html5/thumbnails/76.jpg)
Data StorytellingFrom Discovery to Communication
Cleaning/“Munging”
Analysis/ExploratoryVisualization
Iteration Publication Feedback CollectiveExploration
TableauSpotfireMS OfficeGephiCognosFusion TablesR
Static Image orInteractive app:FlashProcessingHTML5
User Testing Comment Threads
Save state/BookmarkIn-Context CommentsShare
Use of proven methods of user engagement and storytelling
![Page 77: Enlightenment 2.0: A New Era of Collaboration and Storytelling with Data](https://reader033.vdocuments.net/reader033/viewer/2022061200/54771d51b4af9f33218b45a3/html5/thumbnails/77.jpg)
Data StorytellingFrom Discovery to Communication
The Traditional ApproachHand-waving and Powerpoint
![Page 78: Enlightenment 2.0: A New Era of Collaboration and Storytelling with Data](https://reader033.vdocuments.net/reader033/viewer/2022061200/54771d51b4af9f33218b45a3/html5/thumbnails/78.jpg)
The Enlightened Approach
Data StorytellingFrom Discovery to Communication
Takes advantage of proven models of narrative storytelling and user engagement.
The Traditional ApproachHand-waving and Powerpoint
![Page 79: Enlightenment 2.0: A New Era of Collaboration and Storytelling with Data](https://reader033.vdocuments.net/reader033/viewer/2022061200/54771d51b4af9f33218b45a3/html5/thumbnails/79.jpg)
“We are very interested in how visualizations are being taken up in mass media, particularly in journalism, so we simply collected as many examples of that as we could.”
Data StorytellingStory Formats: Learning BestPractices
![Page 80: Enlightenment 2.0: A New Era of Collaboration and Storytelling with Data](https://reader033.vdocuments.net/reader033/viewer/2022061200/54771d51b4af9f33218b45a3/html5/thumbnails/80.jpg)
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G',*" E E E E E E V V E E E V V V E E V V E V V V E E E V E E E V V E VB3'()*.1+(.@()*?@@ )*.1+(.@()*?@@(51.J';* E E E E E E V V V E V E V V V V E V E E E V V E E V V V V V V<%1#%&(-:'1%;"(-%12,"&'()"@'*+(<%/'. <%1#%&(-:'1%;" E E E E E E V E E E E V V V V V V V V V V E V E E V E E E V V E E
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Fig. 7. Design space analysis of narrative visualization. Columns indicate recurring design elements and selected regions highlight patterns in the data. Region (1) shows clusters of orderingstrategies that correspond to distinct genres of visual narration. Region (2) highlights the consistency of interactive designs used by visualizations. Region (3) shows the under-utilization of strategiesto engage the user in the interactive functionality. Region (4) shows the under-utilization of common storytelling techniques across narrative visualizations.
Segal and Heer, 2010
Data StorytellingAnalysis of Visin Media
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Data Storytelling
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Fig. 7. Design space analysis of narrative visualization. Columns indicate recurring design elements and selected regions highlight patterns in the data. Region (1) shows clusters of orderingstrategies that correspond to distinct genres of visual narration. Region (2) highlights the consistency of interactive designs used by visualizations. Region (3) shows the under-utilization of strategiesto engage the user in the interactive functionality. Region (4) shows the under-utilization of common storytelling techniques across narrative visualizations.
Data StorytellingAnalysis of Visin Media
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email protected]:" 7%&"&;%",(B%:'6 E V E E E E E E V V E E E E V E E E E E E E V E V E EG'"/,+(I@@'&6%P'\(B",%>"&(-**";D6(%&(5"D%6*"& 7%&"&;%",(B%:'6 E V E E E E E E V V E E V E E E E V E E E E E E E V E V V V E V E V V E V E V E0G5(!.P'6(>+()';*.1 7%&"&;%",(B%:'6 E V E E E E E E V E E E V E E E E E E E E E E E V EF](=;.&.:%;(G"*" 7%&"&;%",(B%:'6 E V E E E E E E V E E E V E E E E E E E E E E E V E V V E V V E E V E E E E V EA?/#'*(^_`_\(M'";*%.&(@1.:("1.?&/(*3'(F] 0?"1/%"& E V E E E E E E V E E E V E E E E V E E E E E E V E V V E V E E E V V E V E E E7.1:?,"(I&'(^_`_\(G1%P'1Z6(M"&D% 0?"1/%"& E V E E E E E E V E E E V E E E E V E E E E E E V E V V E V V E E V E E E E E EN%#3*%&#(F2(O"/1%"&Z6(X",, 0?"1/%"& E V E E E E E V E E E E V E E E E E E E E E E E V E E V E V V E E V V E E E V E!"22%&#(O+/1.2.8'1(O.*62.*6(";1.66(*3'(F] 0?"1/%"& E V E E E E E E V E E E V E E E E E E E E E V E V E V V E V V E E V V E V E E E!.6;.8(!'*1.(A.:>6\(%&*'1";*%P'(:"2 0?"1/%"& E V E E E E E E V E E E V E E E E E E E E E E E V E E V E V V E E V V E V E E EB3'(X.1,/(=;.&.:+(B?1&6(*3'(9.1&'1 0?"1/%"& E V E E E E E E V E E E E E E E E E E E E E E E V E V V E V E E E V E E E E E E!%&&'6.*"(=:2,.+:'&*(=S2,.1'1 !%&&'6.*"(5?>,%;(M"/%. E V E E E E E E E E E V V E V E E E E E E V V E V E V V E V V E E V V V E E V E-(!"2(.@(I,+:2%;(!'/",6 Q'8(Y.1D(B%:'6 E V E E E E E E V V E E E E E E E E E E E V V E V E V V E-,,(.@(R&@,"*%.&Z6(N%**,'(5"1*6 Q'8(Y.1D(B%:'6 E V E E E E E V V E E E V E E E V E E E E V V E V E V V E V E E E V V E E E V E5"*36(*.(*3'(B.2(.@(*3'(O.:'(M?&(93"1*6 Q'8(Y.1D(B%:'6 E V E E E E E E V E E E V E V E E E E E E E E E V E V V E V V E V V V E E E E EB3'(=>>("&/(7,.8(.@(!.P%'6\(A.S(I@@%;'(M';'%2*6(`abc(d( Q'8(Y.1D(B%:'6 E V E E E E E E V V E E V E E E E E E V E E E E V E V V E V V E E V V E E E E EB3'(T.>,'66(M"*'(@.1(5'.2,'(N%D'(Y.? Q'8(Y.1D(B%:'6 E V E E E E E E V E V E V E E E E E E E E V V E V E V V E V V E E V E E E E E E-/P'1*%6':'&*\(A?6 F&%*'/(B';3&.,.#+ E V E E E E E-/P'1*%6':'&*\(O',%;.2*'1 F&%*'/(B';3&.,.#+ E V E E E E E-&",+$%&#(I>":"Z6();3'/?,' X"63%&#*.&(5.6* E V E E E E E E E V E E V E E E E V E E E E E E V E V V V E V E E E E E E E V EI6;"16(^_`_\(B3'(A'6*(5%;*?1'(Q.:%&''6 0?"1/%"& E E V E E E E V V E V E V E E E E E E E E E E E V E V V E V E E E V V E V E E EB3'(9.&6?:'1("&/(M'*"%,(51%;'(R&/%;'6(6%&;'(^__c 0?"1/%"& E E V E E E E V V V E E V E V E E E E E E V E E V E V V E V V E EF](<.*%&#(R&*'&*%.&6 0?"1/%"& E E V E E E E V V V E E V E V E E E E E E V E E V E9.:2"1%6.&(.@(A'"1(!"1D'*6 Q'8(Y.1D(B%:'6 E E V E E E E E V V E E V E V E E E E V E V V E V E V V E V V E V V V E E E E E7";'6(.@(*3'(G'"/ Q'8(Y.1D(B%:'6 E E V E E E E E V E E E V E E E E E E E E E E E V E
Segal and Heer, 2010
vis.stanford.edu/papers/narrative
Data StorytellingAnalysis of Visin Media
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Sources of Inspiration:Comic Books
MoviesVideo Games
The Enlightened OrganizationTakes advantage of proven models of narrative storytelling and user engagement.
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The “Science Fair Poster”Using Space to Lay Out Regions; Directional Reading
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The “Science Fair Poster”Using Space to Lay Out Regions; Directional Reading
“Stimulating Default View”
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The “Martini Glass” Linear Narrative Followed by Individual Exploration
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The “Martini Glass” Linear Narrative Followed by Individual Exploration
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The “Martini Glass” Linear Narrative Followed by Individual Exploration
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The “Martini Glass”Linear Narrative Followed by Individual Exploration
First read: narrative
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The “Martini Glass”Linear Narrative Followed by Individual Exploration
First read: narrativeSecond read: reference
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The “Data Slide Show”Linear Narrative With Interaction Within Each Frame
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The “Data Slide Show”Or just a series of annotated, sequenced charts
Search All NYTimes.com
Public workers make more …Surveys by the Bureau of EconomicAnalysis show that public workers’annual compensation — salary plusbenefits — is higher on average thanprivate sector workers, and they suggestthat the gap is growing.
… especially when figured per hourof work.Public workers also put in significantlyfewer hours per week. According to theBureau of Labor Statistics, theircompensation per hour is muchhigher.
Most of the advantage is in benefits.They cost state and local governments$14 an hour on average, about 70percent more than private employerspay for their workers.
But comparisons are tricky …Economists point out that governmentwork tends to be in highly skilledcategories, so it is misleading tocompare the groups as a whole.
Published: March 6, 2011
Are State and Local Government Employees Paid Too Much?It’s not an easy question to answer for a number of reasons. Here’s a primer on the issue.
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Search All NYTimes.com
Public workers make more …Surveys by the Bureau of EconomicAnalysis show that public workers’annual compensation — salary plusbenefits — is higher on average thanprivate sector workers, and they suggestthat the gap is growing.
… especially when figured per hourof work.Public workers also put in significantlyfewer hours per week. According to theBureau of Labor Statistics, theircompensation per hour is muchhigher.
Most of the advantage is in benefits.They cost state and local governments$14 an hour on average, about 70percent more than private employerspay for their workers.
But comparisons are tricky …Economists point out that governmentwork tends to be in highly skilledcategories, so it is misleading tocompare the groups as a whole.
Published: March 6, 2011
Are State and Local Government Employees Paid Too Much?It’s not an easy question to answer for a number of reasons. Here’s a primer on the issue.
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The “Data Slide Show”Or just a series of annotated, sequenced charts
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Wide-Angle View; Presents Links to Selected Closer Views
The “Drill-Down Story” Wide-Angle View; Presents Links to Selected Closer Views
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Wide-Angle View; Presents Links to Selected Closer Views
The “Drill-Down Story” Wide-Angle View; Presents Links to Selected Closer Views
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Wide-Angle View; Presents Links to Selected Closer Views
The “Drill-Down Story” Wide-Angle View; Presents Links to Selected Closer Views
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The “Drill-Down Story” Wide-Angle View; Presents Links to Selected Closer Views
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Find yourself in the data
The “Drill-Down Story” Wide-Angle View; Presents Links to Selected Closer Views
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The “Drill-Down Story” Wide-Angle View; Presents Links to Selected Closer Views
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Linear Narratives Using Motion Graphics, Voiceover“Videographics”
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Could be as simple as a screencast“Videographics”
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Data StorytellingEngaging the Audience
The Traditional ApproachViews interactive media as a one-way avenueto present its message
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The Enlightened Approach
Data StorytellingEngaging the Audience
Adds a social component to allow users to return feedback and participate in discovery
The Traditional ApproachViews interactive media as a one-way avenueto present its message
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Data StorytellingGoing Social
Cleaning/“Munging”
Analysis/ExploratoryVisualization
Iteration Publication Feedback CollectiveExploration
TableauSpotfireMS OfficeGephiCognosFusion TablesR
Static Image orInteractive app:FlashProcessingHTML5
User Testing Comment Threads
Save state/BookmarkIn-Context CommentsSharing
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Data StorytellingGoing Social
Cleaning/“Munging”
Analysis/ExploratoryVisualization
Iteration Publication Feedback CollectiveExploration
TableauSpotfireMS OfficeGephiCognosFusion TablesR
Static Image orInteractive app:FlashProcessingHTML5
User Testing Comment Threads
Save state/BookmarkIn-Context CommentsSharing
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Data StorytellingGoing Social: Linked Comment Thread
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Data StorytellingGoing Social: Linked Comment Thread
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Data StorytellingGoing Social: Linked Comment Thread
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Data StorytellingGoing Social: Twitter/Facebook Integration with Saved State
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Data StorytellingGoing Social: Twitter/Facebook Integration with Saved State
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Data StorytellingGoing Social: Annotation
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Data StorytellingGoing Social: Annotation
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Data StorytellingGoing Social: Prediction
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Public Posture in a Two-Way World?
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• Publish reports as static charts and tables
• Lock down internal metrics, reports
• Limit distribution within company
Traditional ApproachAggressively protect proprietary data
Data and the Public Sphere
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• Create public spaces for conversations about data related to your industry, even vis tools
• Promote data vis “competitions” around issues important to your customers
• Release some company information, reports as structured data, or through API
• Rigorously scrub public data to anonymize,protect sensitive information
Enlightened ApproachJudicious Sharing and Publication
Data and the Public Sphere
ManyEyes by IBM/Cognos
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http://visualizing.org
Data and the Public Sphere: Creating Community
“Visualizing.org” by General Electric and Seed Media Group
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http://visualizing.org
“Visualizing.org” by General Electric and Seed Media Group
Data and the Public Sphere: Competitions
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http://www.wnyc.org/shows/bl/census/
“Map Your Moves” by WNYC Public Radio
Data and the Public Sphere: Competitions
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http://www.wnyc.org/shows/bl/census/
“Map Your Moves” by WNYC Public Radio
Data and the Public Sphere: Competitions
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http://www.nytimes.com/interactive/2010/01/10/nyregion/20100110-netflix-map.html
Data and the Public Sphere: Peeks/Leaks
“A Peek into Netflix Queues,” The New York Times (2010)
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The Alternative?
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• Need to follow Twitter, Facebook, other social media to know how people are reacting to your products and services
• Growing list of companies doing real-time analysis with sophisticated natural language processing tools/ artificial intelligence: topic modeling, sentiment analysis...
Customer Mood Watch
Data and the Public SphereKeeping an Ear to the Ground
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http://clara.is
Data and the Public Sphere
Clara, Social Media Monitoring for Video Game Communities
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Data StorytellingEngaging the Audience
The Traditional ApproachTake existing presentation materials andhire a design firm to add “wow” factor
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The Enlightened Approach
Data StorytellingEngaging the Audience
Create data visualizations to explain performance, provide industry context, or engage customers in how they use their product.
Use best visual practices that leverage research on effectiveness of visual language through clarity and simplicity
The Traditional ApproachTake existing presentation materials andhire a design firm to add “wow” factor
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• Bertin’s rules for visual encodings: Length/height, position, color, area, symbol etc, in descending order of effectiveness
• Clean layout and design based in gestalt language of grouping, symbology
• Especially when introducing unfamiliar data
• Prominent key/legend on all graphics!
Importance of Good Fundamentals
Data StorytellingEngaging the Audience
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Data StorytellingSeek Out the Experts
Edward Tufte Stephen Few Connie Malamed
Best Guides for Visual Communication
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Data StorytellingBut Have a Little Fun, Too
Article by Martin Wattenberg and Fernanda Viégas
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Data StorytellingBut Have a Little Fun, Too
Article by Martin Wattenberg and Fernanda Viégas
“Graphs have become easier to read, though their minimalist uniformity sometimes feels like a library where all the books were written by Hemingway.”
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Data StorytellingJoin the Conversation
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Bring on the Debates!