The visual organization : data visualization, big data, and the quest for better decisions

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.

Author Notes

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.

#visualorg
www.philsimon.com
@philsimon

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

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