{"product_id":"the-visual-organization-isbn-9781118794388","title":"The Visual Organization","description":"\u003cp\u003eThe era of Big Data as arrived, and most organizations are woefully unprepared. Slowly, many are discovering that stalwarts like Excel spreadsheets, KPIs, standard reports, and even traditional business intelligence tools aren't sufficient. These old standbys can't begin to handle today's increasing streams, volumes, and types of data. \u003cbr\u003e\u003cbr\u003eAmidst all of the chaos, though, a new type of organization is emerging. \u003cbr\u003e\u003cbr\u003eIn\u003ci\u003e The Visual Organization\u003c\/i\u003e, award-winning author and technology expert Phil Simon looks at how an increasingly number of organizations are embracing new dataviz tools and, more important, a new mind-set based upon data discovery and exploration. Simon adroitly shows how Amazon, Apple, Facebook, Google, Twitter, and other tech heavyweights use powerful data visualization tools to garner fascinating insights into their businesses. But make no mistake: these companies are hardly alone. Organizations of all types, industries, sizes are representing their data in new and amazing ways. As a result, they are asking better questions and making better business decisions.\u003cbr\u003e\u003cbr\u003eRife with real-world examples and case studies, \u003ci\u003eThe Visual Organization \u003c\/i\u003eis a full-color tour-de-force.\u003c\/p\u003e  \u003cp\u003e\u003ci\u003eList of Figures and Tables xvii\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003ePreface xix\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eAcknowledgments xxv\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eHow to Help This Book xxvii\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Book Overview and Background 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 3\u003c\/p\u003e \u003cp\u003eAdventures in Twitter Data Discovery 4\u003c\/p\u003e \u003cp\u003eContemporary Dataviz 101 9\u003c\/p\u003e \u003cp\u003ePrimary Objective 9\u003c\/p\u003e \u003cp\u003eBenefits 11\u003c\/p\u003e \u003cp\u003eMore Important Than Ever 13\u003c\/p\u003e \u003cp\u003eRevenge of the Laggards: The Current State of Dataviz 15\u003c\/p\u003e \u003cp\u003eBook Overview 18\u003c\/p\u003e \u003cp\u003eDefining the Visual Organization 19\u003c\/p\u003e \u003cp\u003eCentral Thesis of Book 19\u003c\/p\u003e \u003cp\u003eCui Bono? 20\u003c\/p\u003e \u003cp\u003eMethodology: Story Matters Here 21\u003c\/p\u003e \u003cp\u003eThe Quest for Knowledge and Case Studies 24\u003c\/p\u003e \u003cp\u003eDifferentiation: A Note on Other Dataviz Texts 25\u003c\/p\u003e \u003cp\u003ePlan of Attack 26\u003c\/p\u003e \u003cp\u003eNext 27\u003c\/p\u003e \u003cp\u003eNotes 27\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 The Ascent of the Visual Organization 29\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Rise of Big Data 30\u003c\/p\u003e \u003cp\u003eOpen Data 30\u003c\/p\u003e \u003cp\u003eThe Burgeoning Data Ecosystem 33\u003c\/p\u003e \u003cp\u003eThe New Web: Visual, Semantic, and API-Driven 34\u003c\/p\u003e \u003cp\u003eThe Arrival of the Visual Web 34\u003c\/p\u003e \u003cp\u003eLinked Data and a More Semantic Web 35\u003c\/p\u003e \u003cp\u003eThe Relative Ease of Accessing Data 36\u003c\/p\u003e \u003cp\u003eGreater Efficiency via Clouds and Data Centers 37\u003c\/p\u003e \u003cp\u003eBetter Data Tools 38\u003c\/p\u003e \u003cp\u003eGreater Organizational Transparency 40\u003c\/p\u003e \u003cp\u003eThe Copycat Economy: Monkey See, Monkey Do 41\u003c\/p\u003e \u003cp\u003eData Journalism and the Nate Silver Effect 41\u003c\/p\u003e \u003cp\u003eDigital Man 44\u003c\/p\u003e \u003cp\u003eThe Arrival of the Visual Citizen 44\u003c\/p\u003e \u003cp\u003eMobility 47\u003c\/p\u003e \u003cp\u003eThe Visual Employee: A More Tech- and Data-Savvy Workforce 47\u003c\/p\u003e \u003cp\u003eNavigating Our Data-Driven World 48\u003c\/p\u003e \u003cp\u003eNext 49\u003c\/p\u003e \u003cp\u003eNotes 49\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Transforming Data into Insights: The Tools 51\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDataviz: Part of an Intelligent and Holistic Strategy 52\u003c\/p\u003e \u003cp\u003eThe Tyranny of Terminology: Dataviz, BI, Reporting, Analytics, and KPIs 53\u003c\/p\u003e \u003cp\u003eDo Visual Organizations Eschew All Tried-and-True Reporting Tools? 55\u003c\/p\u003e \u003cp\u003eDrawing Some Distinctions 56\u003c\/p\u003e \u003cp\u003eThe Dataviz Fab Five 57\u003c\/p\u003e \u003cp\u003eApplications from Large Enterprise Software Vendors 57\u003c\/p\u003e \u003cp\u003eLESVs: The Case For 58\u003c\/p\u003e \u003cp\u003eLESVs: The Case Against 59\u003c\/p\u003e \u003cp\u003eBest-of-Breed Applications 61\u003c\/p\u003e \u003cp\u003eCost 62\u003c\/p\u003e \u003cp\u003eEase of Use and Employee Training 62\u003c\/p\u003e \u003cp\u003eIntegration and the Big Data World 63\u003c\/p\u003e \u003cp\u003ePopular Open-Source Tools 64\u003c\/p\u003e \u003cp\u003eD3.js 64\u003c\/p\u003e \u003cp\u003eR 65\u003c\/p\u003e \u003cp\u003eOthers 66\u003c\/p\u003e \u003cp\u003eDesign Firms 66\u003c\/p\u003e \u003cp\u003eStartups, Web Services, and Additional Resources 70\u003c\/p\u003e \u003cp\u003eThe Final Word: One Size Doesn’t Fit All 72\u003c\/p\u003e \u003cp\u003eNext 73\u003c\/p\u003e \u003cp\u003eNotes 73\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Introducing the Visual Organization 75\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 The Quintessential Visual Organization 77\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eNetflix 1.0: Upsetting the Applecart 77\u003c\/p\u003e \u003cp\u003eNetflix 2.0: Self-Cannibalization 78\u003c\/p\u003e \u003cp\u003eDataviz: Part of a Holistic Big Data Strategy 80\u003c\/p\u003e \u003cp\u003eDataviz: Imbued in the Netflix Culture 81\u003c\/p\u003e \u003cp\u003eCustomer Insights 82\u003c\/p\u003e \u003cp\u003eBetter Technical and Network Diagnostics 84\u003c\/p\u003e \u003cp\u003eEmbracing the Community 88\u003c\/p\u003e \u003cp\u003eLessons 89\u003c\/p\u003e \u003cp\u003eNext 90\u003c\/p\u003e \u003cp\u003eNotes 90\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Dataviz in the DNA 93\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Beginnings 94\u003c\/p\u003e \u003cp\u003eUX Is Paramount 95\u003c\/p\u003e \u003cp\u003eThe Plumbing 97\u003c\/p\u003e \u003cp\u003eEmbracing Free and Open-Source Tools 98\u003c\/p\u003e \u003cp\u003eExtensive Use of APIs 101\u003c\/p\u003e \u003cp\u003eLessons 101\u003c\/p\u003e \u003cp\u003eNext 102\u003c\/p\u003e \u003cp\u003eNote 102\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Transparency in Texas 103\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBackground 104\u003c\/p\u003e \u003cp\u003eEarly Dataviz Efforts 105\u003c\/p\u003e \u003cp\u003eEmbracing Traditional BI 106\u003c\/p\u003e \u003cp\u003eData Discovery 107\u003c\/p\u003e \u003cp\u003eBetter Visibility into Student Life 108\u003c\/p\u003e \u003cp\u003eExpansion: Spreading Dataviz Throughout the System 110\u003c\/p\u003e \u003cp\u003eResults 111\u003c\/p\u003e \u003cp\u003eLessons 113\u003c\/p\u003e \u003cp\u003eNext 113\u003c\/p\u003e \u003cp\u003eNotes 114\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Getting Started: Becoming a Visual Organization 115\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 The Four-Level Visual Organization Framework 117\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBig Disclaimers 118\u003c\/p\u003e \u003cp\u003eA Simple Model 119\u003c\/p\u003e \u003cp\u003eLimits and Clarifications 120\u003c\/p\u003e \u003cp\u003eProgression 122\u003c\/p\u003e \u003cp\u003eIs Progression Always Linear? 123\u003c\/p\u003e \u003cp\u003eCan a Small Organization Best Position Itself to Reach Levels 3 and 4? If So, How? 123\u003c\/p\u003e \u003cp\u003eCan an Organization Start at Level 3 or 4 and Build from the Top Down? 123\u003c\/p\u003e \u003cp\u003eIs Intralevel Progression Possible? 123\u003c\/p\u003e \u003cp\u003eAre Intralevel and Interlevel Progression Inevitable? 123\u003c\/p\u003e \u003cp\u003eCan Different Parts of the Organization Exist on Different Levels? 124\u003c\/p\u003e \u003cp\u003eShould an Organization Struggling with Levels 1 and 2 Attempt to Move to Level 3 or 4? 124\u003c\/p\u003e \u003cp\u003eRegression: Reversion to Lower Levels 124\u003c\/p\u003e \u003cp\u003eComplements, Not Substitutes 125\u003c\/p\u003e \u003cp\u003eAccumulated Advantage 125\u003c\/p\u003e \u003cp\u003eThe Limits of Lower Levels 125\u003c\/p\u003e \u003cp\u003eRelativity and Sublevels 125\u003c\/p\u003e \u003cp\u003eShould Every Organization Aspire to Level 4? 126\u003c\/p\u003e \u003cp\u003eNext 126\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 WWVOD? 127\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eVisualizing the Impact of a Reorg 128\u003c\/p\u003e \u003cp\u003eVisualizing Employee Movement 129\u003c\/p\u003e \u003cp\u003eStarting Down the Dataviz Path 129\u003c\/p\u003e \u003cp\u003eResults and Lessons 133\u003c\/p\u003e \u003cp\u003eFuture 135\u003c\/p\u003e \u003cp\u003eA Marketing Example 136\u003c\/p\u003e \u003cp\u003eNext 137\u003c\/p\u003e \u003cp\u003eNotes 137\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Building the Visual Organization 139\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData Tips and Best Practices 139\u003c\/p\u003e \u003cp\u003eData: The Primordial Soup 139\u003c\/p\u003e \u003cp\u003eWalk Before You Run . . . At Least for Now 140\u003c\/p\u003e \u003cp\u003eA Dataviz Is Often Just the Starting Point 140\u003c\/p\u003e \u003cp\u003eVisualize Both Small and Big Data 141\u003c\/p\u003e \u003cp\u003eDon’t Forget the Metadata 141\u003c\/p\u003e \u003cp\u003eLook Outside of the Enterprise 143\u003c\/p\u003e \u003cp\u003eThe Beginnings: All Data Is Not Required 143\u003c\/p\u003e \u003cp\u003eVisualize Good and Bad Data 144\u003c\/p\u003e \u003cp\u003eEnable Drill-Down 144\u003c\/p\u003e \u003cp\u003eDesign Tips and Best Practices 148\u003c\/p\u003e \u003cp\u003eBegin with the End in Mind (Sort of) 148\u003c\/p\u003e \u003cp\u003eSubtract When Possible 150\u003c\/p\u003e \u003cp\u003eUX: Participation and Experimentation Are Paramount 150\u003c\/p\u003e \u003cp\u003eEncourage Interactivity 151\u003c\/p\u003e \u003cp\u003eUse Motion and Animation Carefully 151\u003c\/p\u003e \u003cp\u003eUse Relative—Not Absolute—Figures 151\u003c\/p\u003e \u003cp\u003eTechnology Tips and Best Practices 152\u003c\/p\u003e \u003cp\u003eWhere Possible, Consider Using APIs 152\u003c\/p\u003e \u003cp\u003eEmbrace New Tools 152\u003c\/p\u003e \u003cp\u003eKnow the Limitations of Dataviz Tools 153\u003c\/p\u003e \u003cp\u003eBe Open 153\u003c\/p\u003e \u003cp\u003eManagement Tips and Best Practices 154\u003c\/p\u003e \u003cp\u003eEncourage Self-Service, Exploration, and Data Democracy 154\u003c\/p\u003e \u003cp\u003eExhibit a Healthy Skepticism 154\u003c\/p\u003e \u003cp\u003eTrust the Process, Not the Result 155\u003c\/p\u003e \u003cp\u003eAvoid the Perils of Silos and Specialization 156\u003c\/p\u003e \u003cp\u003eIf Possible, Visualize 156\u003c\/p\u003e \u003cp\u003eSeek Hybrids When Hiring 157\u003c\/p\u003e \u003cp\u003eThink Direction First, Precision Later 157\u003c\/p\u003e \u003cp\u003eNext 158\u003c\/p\u003e \u003cp\u003eNotes 158\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 The Inhibitors: Mistakes, Myths, and Challenges 159\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMistakes 160\u003c\/p\u003e \u003cp\u003eFalling into the Traditional ROI Trap 160\u003c\/p\u003e \u003cp\u003eAlways—and Blindly—Trusting a Dataviz 161\u003c\/p\u003e \u003cp\u003eIgnoring the Audience 162\u003c\/p\u003e \u003cp\u003eDeveloping in a Cathedral 162\u003c\/p\u003e \u003cp\u003eSet It and Forget It 162\u003c\/p\u003e \u003cp\u003eBad Dataviz 163\u003c\/p\u003e \u003cp\u003eTMI 163\u003c\/p\u003e \u003cp\u003eUsing Tiny Graphics 163\u003c\/p\u003e \u003cp\u003eMyths 165\u003c\/p\u003e \u003cp\u003eData-visualizations Guarantee Certainty and Success 165\u003c\/p\u003e \u003cp\u003eData Visualization Is Easy 165\u003c\/p\u003e \u003cp\u003eData Visualizations Are Projects 166\u003c\/p\u003e \u003cp\u003eThere Is One “Right” Visualization 166\u003c\/p\u003e \u003cp\u003eExcel Is Sufficient 167\u003c\/p\u003e \u003cp\u003eChallenges 167\u003c\/p\u003e \u003cp\u003eThe Quarterly Visualization Mentality 167\u003c\/p\u003e \u003cp\u003eData Defiance 168\u003c\/p\u003e \u003cp\u003eUnlearning History: Overcoming the Disappointments of Prior Tools 168\u003c\/p\u003e \u003cp\u003eNext 169\u003c\/p\u003e \u003cp\u003eNotes 169\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV Conclusion and the Future of Dataviz 171\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCoda: We’re Just Getting Started 173\u003c\/p\u003e \u003cp\u003eFour Critical Data-Centric Trends 175\u003c\/p\u003e \u003cp\u003eWearable Technology and the Quantified Self 175\u003c\/p\u003e \u003cp\u003eMachine Learning and the Internet of Things 176\u003c\/p\u003e \u003cp\u003eMultidimensional Data 177\u003c\/p\u003e \u003cp\u003eThe Forthcoming Battle Over Data Portability and Ownership 179\u003c\/p\u003e \u003cp\u003eFinal Thoughts: Nothing Stops This Train 181\u003c\/p\u003e \u003cp\u003eNotes 182\u003c\/p\u003e \u003cp\u003eAfterword: My Life in Data 183\u003c\/p\u003e \u003cp\u003eAppendix: Supplemental Dataviz Resources 187\u003c\/p\u003e \u003cp\u003e\u003ci\u003eSelected Bibliography 191\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eAbout the Author 193\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eIndex 195\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePhil Simon\u003c\/b\u003e 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 \u003ci\u003eThe Harvard Business Review\u003c\/i\u003e, CNN, NBC, CNBC, \u003ci\u003eInc. Magazine, BusinessWeek\u003c\/i\u003e, The Huffington Post, \u003ci\u003eFast Company\u003c\/i\u003e, \u003ci\u003eThe New York Times,\u003c\/i\u003e ReadWriteWeb, and many other sites.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e#visualorg\u003cbr\u003e www.philsimon.com\u003cbr\u003e @philsimon\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eThe VISUAL ORGANIZATION\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData Visualization, Big Data, and the Quest for Better Decisions\u003c\/p\u003e \u003cp\u003eThoreau once said, “It’s not what you look at that matters; \u003ci\u003eit’s what you see.\u003c\/i\u003e”\u003c\/p\u003e \u003cp\u003eIn today’s business world, it’s not exactly easy to see what’s going on. Most people are overwhelmed by data.\u003c\/p\u003e \u003cp\u003eAmidst all of the hype and confusion surrounding Big Data, a new type of enterprise is emerging: \u003ci\u003eThe Visual Organization\u003c\/i\u003e. An increasing number of organizations have realized that today’s volume, variety, and velocity of data require new applications. More than technology, though, they have adopted a different mind-set—one based upon data discovery and exploration, not conventional enterprise “reporting.” These companies understand that interactive heat maps and tree maps lend themselves to data discovery more than Microsoft Excel, static graphs, pie charts, and dashboards.\u003c\/p\u003e \u003cp\u003eFor example, most people have heard of Netflix. Still, relatively few realize that behind the scenes, Netflix does some remarkable things to make the magic happen. Netflix uses fascinating dataviz tools to discover trends, diagnose technical issues, and unearth extraordinarily valuable insights about its customers.\u003c\/p\u003e \u003cp\u003eEmployees at Autodesk use a remarkable and interactive tool that visualizes current and historical employee movement. From this, they can identify potential management issues and see what a corporate reorg \u003ci\u003ereally\u003c\/i\u003e looks like.\u003c\/p\u003e \u003cp\u003eAnd then there’s eBay. Powerful data-discovery tools allow the company’s employees to effectively “see” what \u003ci\u003eebay.com\u003c\/i\u003e would look like....as a brick-and-mortar store.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e\u003ci\u003eIt’s time to get visual.\u003c\/i\u003e\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eThe Visual Organization\u003c\/i\u003e is the sixth book by award-winning author, keynote speaker, and recognized technology expert Phil Simon. Simon demonstrates how a new breed of progressive enterprises has turned traditional data visualization on its head In their place, they are embracing new, interactive, and more robust tools. And these tools help separate the signals from the noise that is Big Data. As a result, they are asking better questions and making better business decisions.\u003c\/p\u003e \u003cp\u003eRife with real-world examples and practical advice, \u003ci\u003eThe Visual Organization\u003c\/i\u003e is a full-color \u003ci\u003etour de force\u003c\/i\u003e. Simon deftly explains how organizations can do more than just \u003ci\u003esurvive\u003c\/i\u003e the data deluge; they can thrive in it. It is required reading for executives, professionals, and students interested in unleashing the power of data.\u003c\/p\u003e \u003cp\u003eStart to see your business differently.\u003c\/p\u003e  \u003cp\u003e\u003c\/p\u003e\u003cb\u003ePraise for \u003ci\u003e\u003c\/i\u003eThe Visual Organization\u003c\/b\u003e\u003ci\u003e\u003c\/i\u003e \u003cp\u003e\u003c\/p\u003e“The sheer amount of data coming at us these days is overwhelming companies both small and large. It’s no wonder that so many miss out on the opportunities right in front of them. \u003ci\u003eThe Visual Organization\u003c\/i\u003e manifests how a wide range of companies is leveraging new and powerful visual tools. Read it and turn your company into a more efficient, data-driven, decision-making machine.”\u003cbr\u003e\u003cb\u003eFrank Gruber, CEO \u0026amp; Co-Founder, Tech Cocktail; Author of \u003ci\u003eStartup Mixology\u003c\/i\u003e\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e“There is data everywhere, but how many of us actually use it to drive our work? People learn in different ways, and for many a visual approach is most powerful. Unfortunately, most organizations have overlooked the opportunity in the visual presentation of data. With \u003ci\u003eThe Visual Organization\u003c\/i\u003e, no longer is there an excuse for doing so. Simon offers a nuanced and refreshing view on contemporary data visualization through compelling storytelling, and yes, great visuals.”\u003cbr\u003e\u003cb\u003eTerri L. Griffith, Chair, Management Department, Santa Clara University; Author of \u003ci\u003eThe Plugged-In Manager\u003c\/i\u003e\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e“There’s good news in this dazzling book. Yes, Big Data is overwhelming, but progressive organizations have found a way to identify the signal in its noise. Through a mix of analysis and synthesis, Simon demonstrates how it’s possible to see not only the forest, but the trees.”\u003cbr\u003e\u003cb\u003eBrad Feld, Co-founder and Managing Director, Foundry Group; Co-author of \u003ci\u003eStartup Life\u003c\/i\u003e\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e“As we’ve come to expect from him, Phil doesn’t just write about a topic. Instead, he explores it deeply, connecting it to the business, technology, and societal world around us. Somehow he has the uncanny knack to offer sage advice for both IT and business professionals–this time about visual data expression–and bedeck it in a fantastic narrative.”\u003cbr\u003e\u003cb\u003eDouglas Laney, Research Vice President, Information Innovation, Gartner\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e“It’s now critical to display data in ways that leverage our human visual capabilities and empower us to discover predictive insights from data To that end, \u003ci\u003eThe Visual Organization\u003c\/i\u003e is essential reading.”\u003cbr\u003e\u003cb\u003eEric Siegel, Founder, Predictive Analytics World; Author of \u003ci\u003ePredictive Analytics\u003c\/i\u003e\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e“It’s clear that Big Data is transforming business. 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