The Visual Organization – Data Visualization, Big Data, and the Quest for Better Decisions

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