Phil Simon is a frequent keynote speaker and recognized technology expert. He is the awardwinning author of six management books He consults with organizations on matters related to strategy, data, and technology His contributions have been featured on The Harvard Business Review, CNN, NBC, CNBC, Inc. Magazine, BusinessWeek, The Huffington Post, Fast Company, The New York Times, ReadWriteWeb, and many other sites.#visualorgwww.philsimon.com@philsimon

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• List of Figures and Tables xviiPreface xixAcknowledgments xxvHow to Help This Book xxviiPart I Book Overview and Background 1Introduction 3Adventures in Twitter Data Discovery 4Contemporary Dataviz 101 9Primary Objective 9Benefits 11More Important Than Ever 13Revenge of the Laggards: The Current State of Dataviz 15Book Overview 18Defining the Visual Organization 19Central Thesis of Book 19Cui Bono? 20Methodology: Story Matters Here 21The Quest for Knowledge and Case Studies 24Differentiation: A Note on Other Dataviz Texts 25Plan of Attack 26Next 27Notes 27Chapter 1 The Ascent of the Visual Organization 29The Rise of Big Data 30Open Data 30The Burgeoning Data Ecosystem 33The New Web: Visual, Semantic, and API-Driven 34The Arrival of the Visual Web 34Linked Data and a More Semantic Web 35The Relative Ease of Accessing Data 36Greater Efficiency via Clouds and Data Centers 37Better Data Tools 38Greater Organizational Transparency 40The Copycat Economy: Monkey See, Monkey Do 41Data Journalism and the Nate Silver Effect 41Digital Man 44The Arrival of the Visual Citizen 44Mobility 47The Visual Employee: A More Tech- and Data-Savvy Workforce 47Navigating Our Data-Driven World 48Next 49Notes 49Chapter 2 Transforming Data into Insights: The Tools 51Dataviz: Part of an Intelligent and Holistic Strategy 52The Tyranny of Terminology: Dataviz, BI, Reporting, Analytics, and KPIs 53Do Visual Organizations Eschew All Tried-and-True Reporting Tools? 55Drawing Some Distinctions 56The Dataviz Fab Five 57Applications from Large Enterprise Software Vendors 57LESVs: The Case For 58LESVs: The Case Against 59Best-of-Breed Applications 61Cost 62Ease of Use and Employee Training 62Integration and the Big Data World 63Popular Open-Source Tools 64D3.js 64R 65Others 66Design Firms 66Startups, Web Services, and Additional Resources 70The Final Word: One Size Doesn’t Fit All 72Next 73Notes 73Part II Introducing the Visual Organization 75Chapter 3 The Quintessential Visual Organization 77Netflix 1.0: Upsetting the Applecart 77Netflix 2.0: Self-Cannibalization 78Dataviz: Part of a Holistic Big Data Strategy 80Dataviz: Imbued in the Netflix Culture 81Customer Insights 82Better Technical and Network Diagnostics 84Embracing the Community 88Lessons 89Next 90Notes 90Chapter 4 Dataviz in the DNA 93The Beginnings 94UX Is Paramount 95The Plumbing 97Embracing Free and Open-Source Tools 98Extensive Use of APIs 101Lessons 101Next 102Note 102Chapter 5 Transparency in Texas 103Background 104Early Dataviz Efforts 105Embracing Traditional BI 106Data Discovery 107Better Visibility into Student Life 108Expansion: Spreading Dataviz Throughout the System 110Results 111Lessons 113Next 113Notes 114Part III Getting Started: Becoming a Visual Organization 115Chapter 6 The Four-Level Visual Organization Framework 117Big Disclaimers 118A Simple Model 119Limits and Clarifications 120Progression 122Is Progression Always Linear? 123Can a Small Organization Best Position Itself to Reach Levels 3 and 4? If So, How? 123Can an Organization Start at Level 3 or 4 and Build from the Top Down? 123Is Intralevel Progression Possible? 123Are Intralevel and Interlevel Progression Inevitable? 123Can Different Parts of the Organization Exist on Different Levels? 124Should an Organization Struggling with Levels 1 and 2 Attempt to Move to Level 3 or 4? 124Regression: Reversion to Lower Levels 124Complements, Not Substitutes 125Accumulated Advantage 125The Limits of Lower Levels 125Relativity and Sublevels 125Should Every Organization Aspire to Level 4? 126Next 126Chapter 7 WWVOD? 127Visualizing the Impact of a Reorg 128Visualizing Employee Movement 129Starting Down the Dataviz Path 129Results and Lessons 133Future 135A Marketing Example 136Next 137Notes 137Chapter 8 Building the Visual Organization 139Data Tips and Best Practices 139Data: The Primordial Soup 139Walk Before You Run . . . At Least for Now 140A Dataviz Is Often Just the Starting Point 140Visualize Both Small and Big Data 141Don’t Forget the Metadata 141Look Outside of the Enterprise 143The Beginnings: All Data Is Not Required 143Visualize Good and Bad Data 144Enable Drill-Down 144Design Tips and Best Practices 148Begin with the End in Mind (Sort of) 148Subtract When Possible 150UX: Participation and Experimentation Are Paramount 150Encourage Interactivity 151Use Motion and Animation Carefully 151Use Relative—Not Absolute—Figures 151Technology Tips and Best Practices 152Where Possible, Consider Using APIs 152Embrace New Tools 152Know the Limitations of Dataviz Tools 153Be Open 153Management Tips and Best Practices 154Encourage Self-Service, Exploration, and Data Democracy 154Exhibit a Healthy Skepticism 154Trust the Process, Not the Result 155Avoid the Perils of Silos and Specialization 156If Possible, Visualize 156Seek Hybrids When Hiring 157Think Direction First, Precision Later 157Next 158Notes 158Chapter 9 The Inhibitors: Mistakes, Myths, and Challenges 159Mistakes 160Falling into the Traditional ROI Trap 160Always—and Blindly—Trusting a Dataviz 161Ignoring the Audience 162Developing in a Cathedral 162Set It and Forget It 162Bad Dataviz 163TMI 163Using Tiny Graphics 163Myths 165Data-visualizations Guarantee Certainty and Success 165Data Visualization Is Easy 165Data Visualizations Are Projects 166There Is One “Right” Visualization 166Excel Is Sufficient 167Challenges 167The Quarterly Visualization Mentality 167Data Defiance 168Unlearning History: Overcoming the Disappointments of Prior Tools 168Next 169Notes 169Part IV Conclusion and the Future of Dataviz 171Coda: We’re Just Getting Started 173Four Critical Data-Centric Trends 175Wearable Technology and the Quantified Self 175Machine Learning and the Internet of Things 176Multidimensional Data 177The Forthcoming Battle Over Data Portability and Ownership 179Final Thoughts: Nothing Stops This Train 181Notes 182Afterword: My Life in Data 183Appendix: Supplemental Dataviz Resources 187Selected Bibliography 191About the Author 193Index 195