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From The Visual Organization Full book available for purchase here. List of Figures and Tables xvii,Preface xix,Acknowledgments xxv. How to Help This Book xxvii,Part I Book Overview and Background 1. Introduction 3,Adventures in Twitter Data Discovery 4. Contemporary Dataviz 101 9,Primary Objective 9,Benefits 11. More Important Than Ever 13, Revenge of the Laggards The Current State of Dataviz 15.
Book Overview 18,Defining the Visual Organization 19. Central Thesis of Book 19,Cui Bono 20,Methodology Story Matters Here 21. The Quest for Knowledge and Case Studies 24,Differentiation A Note on Other Dataviz Texts 25. Plan of Attack 26, Chapter 1 The Ascent of the Visual Organization 29. The Rise of Big Data 30,Open Data 30,The Burgeoning Data Ecosystem 33.
The New Web Visual Semantic and API Driven 34,The Arrival of the Visual Web 34. Linked Data and a More Semantic Web 35,The Relative Ease of Accessing Data 36. Greater Efficiency via Clouds and Data Centers 37,Better Data Tools 38. Greater Organizational Transparency 40,The Copycat Economy Monkey See Monkey Do 41. Data Journalism and the Nate Silver Effect 41,Digital Man 44.
The Arrival of the Visual Citizen 44,Mobility 47,ftoc xiii February 3 2014 3 02 PM. xiv C o n t e n t s, The Visual Employee A More Tech and Data Savvy Workforce 47. Navigating Our Data Driven World 48, Chapter 2 Transforming Data into Insights The Tools 51. Dataviz Part of an Intelligent and Holistic Strategy 52. The Tyranny of Terminology Dataviz BI Reporting Analytics and. Do Visual Organizations Eschew All Tried and True Reporting. Drawing Some Distinctions 56,The Dataviz Fab Five 57. Applications from Large Enterprise Software Vendors 57. LESVs The Case For 58,LESVs The Case Against 59,Best of Breed Applications 61.
Ease of Use and Employee Training 62,Integration and the Big Data World 63. Popular Open Source Tools 64,Design Firms 66,Startups Web Services and Additional Resources 70. The Final Word One Size Doesn t Fit All 72,Part II Introducing the Visual Organization 75. Chapter 3 The Quintessential Visual Organization 77. Netflix 1 0 Upsetting the Applecart 77,Netflix 2 0 Self Cannibalization 78. Dataviz Part of a Holistic Big Data Strategy 80,Dataviz Imbued in the Netflix Culture 81.
Customer Insights 82,Better Technical and Network Diagnostics 84. Embracing the Community 88,Lessons 89,Chapter 4 Dataviz in the DNA 93. The Beginnings 94,UX Is Paramount 95,The Plumbing 97. Embracing Free and Open Source Tools 98,Extensive Use of APIs 101. Lessons 101,Chapter 5 Transparency in Texas 103,Background 104.
Early Dataviz Efforts 105,Embracing Traditional BI 106. Data Discovery 107,Better Visibility into Student Life 108. Expansion Spreading Dataviz Throughout the System 110. ftoc xiv February 3 2014 3 02 PM,C o n t e n t s xv. Results 111,Lessons 113, Part III Getting Started Becoming a Visual Organization 115. Chapter 6 The Four Level Visual Organization Framework 117. Big Disclaimers 118,A Simple Model 119,Limits and Clarifications 120.
Progression 122,Is Progression Always Linear 123, Can a Small Organization Best Position Itself to Reach Levels 3 and. 4 If So How 123, Can an Organization Start at Level 3 or 4 and Build from the Top. Is Intralevel Progression Possible 123, Are Intralevel and Interlevel Progression Inevitable 123. Can Different Parts of the Organization Exist on Different. Levels 124, Should an Organization Struggling with Levels 1 and 2 Attempt to. Move to Level 3 or 4 124,Regression Reversion to Lower Levels 124.
Complements Not Substitutes 125,Accumulated Advantage 125. The Limits of Lower Levels 125,Relativity and Sublevels 125. Should Every Organization Aspire to Level 4 126,Chapter 7 WWVOD 127. Visualizing the Impact of a Reorg 128,Visualizing Employee Movement 129. Starting Down the Dataviz Path 129,Results and Lessons 133.
Future 135,A Marketing Example 136,Chapter 8 Building the Visual Organization 139. Data Tips and Best Practices 139,Data The Primordial Soup 139. Walk Before You Run At Least for Now 140,A Dataviz Is Often Just the Starting Point 140. Visualize Both Small and Big Data 141,Don t Forget the Metadata 141. Look Outside of the Enterprise 143,The Beginnings All Data Is Not Required 143.
Visualize Good and Bad Data 144,Enable Drill Down 144. Design Tips and Best Practices 148,Begin with the End in Mind Sort of 148. Subtract When Possible 150, UX Participation and Experimentation Are Paramount 150. Encourage Interactivity 151,Use Motion and Animation Carefully 151. Use Relative Not Absolute Figures 151,Technology Tips and Best Practices 152.
Where Possible Consider Using APIs 152,ftoc xv February 3 2014 3 02 PM. xvi C o n t e n t s,Embrace New Tools 152,Know the Limitations of Dataviz Tools 153. Be Open 153,Management Tips and Best Practices 154. Encourage Self Service Exploration and Data Democracy 154. Exhibit a Healthy Skepticism 154,Trust the Process Not the Result 155. Avoid the Perils of Silos and Specialization 156,If Possible Visualize 156.
Seek Hybrids When Hiring 157,Think Direction First Precision Later 157. Chapter 9 The Inhibitors Mistakes Myths and Challenges 159. Mistakes 160,Falling into the Traditional ROI Trap 160. Always and Blindly Trusting a Dataviz 161,Ignoring the Audience 162. Developing in a Cathedral 162,Set It and Forget It 162. Bad Dataviz 163,Using Tiny Graphics 163, Data visualizations Guarantee Certainty and Success 165.
Data Visualization Is Easy 165,Data Visualizations Are Projects 166. There Is One Right Visualization 166,Excel Is Sufficient 167. Challenges 167,The Quarterly Visualization Mentality 167. Data Defiance 168, Unlearning History Overcoming the Disappointments of Prior. Part IV Conclusion and the Future of Dataviz 171,Coda We re Just Getting Started 173.
Four Critical Data Centric Trends 175,Wearable Technology and the Quantified Self 175. Machine Learning and the Internet of Things 176,Multidimensional Data 177. The Forthcoming Battle Over Data Portability and Ownership 179. Final Thoughts Nothing Stops This Train 181,Afterword My Life in Data 183. Appendix Supplemental Dataviz Resources 187,Selected Bibliography 191. About the Author 193, From The Visual Organization Data Visualization Big Data and the Quest for Better Decisions by Phil Simon Copyright 2014.
SAS Institute Inc Cary North Carolina USA ALL RIGHTS RESERVED. ftoc xvi February 3 2014 3 02 PM, From The Visual Organization Full book available for purchase here. C h a p t e r 1,The Ascent of the,Visual Organization. Where is the knowledge we have lost in information. hy are so many organizations starting to embrace data visualization. What are the trends driving this movement In other words why are. organizations becoming more visual, Let me be crystal clear data visualization is by no means a recent advent. Cavemen drew primitive paintings as a means of communication We have. been arranging data into tables columns and rows at least since the sec. ond century C E However the idea of representing quantitative information. graphically didn t arise until the seventeenth century So writes Stephen Few. in his paper Data Visualization for Human Perception. In 1644 Dutch astronomer and cartographer Michael Florent van Langren. created the first known graph of statistical data Van Langren displayed a wide. range of estimates of the distance in longitude between Toledo Spain and. Rome Italy A century and a half later Scottish engineer and political econo. mist William Playfair invented staples like the line graph bar chart pie chart. and circle graph, Van Langren Playfair and others discovered what we now take for granted. compared to looking at individual records in a spreadsheet or database table. it s easier to understand data and observe trends with simple graphs and charts. To read the entire paper go to http tinyurl com few perception. For more on the history of dataviz see http tinyurl com dv hist. c01 29 February 3 2014 4 19 PM,30 B o o k Overview and Background.
The neurological reasons behind this are beyond the scope of this book Suffice. it to say here that the human brain can more quickly and easily make sense. of certain types of information when they are represented in a visual format. This chapter explores some of the social technological data and business. trends driving the visual organization We will see that employees and. organizations are willingly representing or in some cases being forced to. represent their data in more visual ways,Let s start with the elephant in the room. The Rise of Big Data, We are without question living in an era of Big Data and whether most peo. ple or organizations realize this is immaterial As such compared to even five. years ago today there is a greater need to visualize data The reason is simple. there s just so much more of it The infographic in Figure 1 1 represents some. of the many statistics cited about the enormity of Big Data And the amount of. available data keeps exploding Just look at how much content people gener. ate in one minute on the Internet as shown in Figure 1 2. Figures 1 1 and 1 2 manifest that Big Data is well big and this means. many things For one new tools are needed to help people and organizations. make sense of this In Too Big to Ignore I discussed at length how relational. databases could not begin to store much less analyze petabytes of unstruc. tured data Yes data storage and retrieval are important but organizations ulti. mately should seek to use this information to make better business decisions. Over the past few years we ve begun to hear more about another game. changing movement open data Perhaps the seminal moment occurred when. Sir Tim Berners Lee gave a 2010 TED talk on the subject Put simply open. data represents the idea that certain data should be. While critical the arrival of freely available to everyone to use and republish as. Big Data is far from the only they wish without restrictions from copyright pat. data related trend to take root ents or other mechanisms of control. Think of open data as the liberation of valuable,over the past decade The. information that fosters innovation transparency,arrival of Big Data is one of. citizen participation policy measurement and bet,the key factors explaining.
ter more efficient government Examples of open or,the rise of the Visual. public datasets include music metadata site Music, Organization Brainz and geolocation site OpenStreetMap But it. To watch the talk go to http tinyurl com tim open data. c01 30 February 3 2014 4 19 PM, T h e A s c e n t o f t h e V i s u a l O r g a n i z a t i o n 31. Figure 1 1 What Is Big Data,Source Asigra, doesn t stop there Anyone today can access a wide trove of scientific eco. nomic health census and government data Data sources and types are being. released every day And as Chapter 2 will show there s no dearth of powerful. and user friendly tools designed specifically to visualize all this data. To access some very large public datasets see http aws amazon com publicdatasets. For some of them see http opendata tools org en visualization. c01 31 February 3 2014 4 19 PM,32 B o o k Overview and Background.
Figure 1 2 The Internet in One Minute,Source Image courtesy of Domo www domo com. c01 32 February 3 2014 4 19 PM, T h e A s c e n t o f t h e V i s u a l O r g a n i z a t i o n 33. ampton Central,Music Doap,Audio space Flickr,Scrobbler QDOS exporter SIOC. BBC BBC Magna SW,Later John Onto,Jamendo tune Conference. TOTP Peel FOAF world,names Revyu,DBpedia RDF Book,Census Mashup.
Data NEW Fact DBLP,book lingvoj,riese Berlin,Euro Explorer. Wiki Open wrappr,Track company Cyc,W3C Project,WordNet Guten. Figure 1 3 Examples of Mainstream Open Datasets as of 2008. Source Richard Cyganiak licensed under Creative Commons. Figure 1 3 represents a mere fraction of the open datasets currently avail. able to anyone with an Internet connection and a desire to explore. Of course the benefits of open data are not absolute Unfortunately and. not surprisingly many people use open data for malevolent purposes For. instance consider Jigsaw a marketplace that pays people to hand over other. people s contact information I won t dignify Jigsaw with a link here As of. this writing anyone can download this type of data on more than 7 million. professionals Beyond annoying phone calls from marketers and incessant. spam it s not hard to imagine terrorist organizations accessing open data for. nefarious purposes Still the pros of open data far exceed their cons. The Burgeoning Data Ecosystem, In the Introduction I discussed how anyone could easily visualize their Face. book Twitter and LinkedIn data I used Vizify to create an interesting visual. profile of my professional life but Vizify and its ilk are really just the tip of the. For a larger visual of what s out there see http tinyurl com open data book. c01 33 February 3 2014 4 19 PM,34 B o o k Overview and Background. iceberg Through open APIs scores of third parties can currently access. xiii ftoc xiii February 3 2014 3 02 PM Contents List of Figures and Tables xvii Preface xix Acknowledgments xxv How to Help This Book xxvii Part I Book Overview and Background 1

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