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The Visual Organization

Data Visualization, Big Data, and the Quest for Better Decisions

Gebonden Engels 2014 9781118794388
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The 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.

Amidst all of the chaos, though, a new type of organization is emerging.

In The Visual Organization, 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.

Rife with real–world examples and case studies, The Visual Organization is a full–color tour–de–force.


Aantal pagina's:240
Uitgever:John Wiley & Sons


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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

Next 27

Notes 27

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

The Visual Employee: A More Tech– and Data–Savvy Workforce 47

Navigating Our Data–Driven World 48

Next 49

Notes 49

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 KPIs 53

Do Visual Organizations Eschew All Tried–and–True Reporting Tools? 55

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

Cost 62

Ease of Use and Employee Training 62

Integration and the Big Data World 63

Popular Open–Source Tools 64

D3.js 64

R 65

Others 66

Design Firms 66

Startups, Web Services, and Additional Resources 70

The Final Word: One Size Doesn t Fit All 72

Next 73

Notes 73

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

Next 90

Notes 90

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

Next 102

Note 102

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

Results 111

Lessons 113

Next 113

Notes 114

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 Down? 123

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

Next 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

Next 137

Notes 137

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

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

Next 158

Notes 158

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

TMI 163

Using Tiny Graphics 163

Myths 165

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 Tools 168

Next 169

Notes 169

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

Notes 182

Afterword: My Life in Data 183

Appendix: Supplemental Dataviz Resources 187

Selected Bibliography 191

About the Author 193

Index 195

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