The Visual Imperative: Creating a Visual Culture of Data Discovery

Cover......Page 1
Title Page......Page 4
Copyright Page......Page 5
Dedication......Page 6
Contents......Page 8
Foreword......Page 16
Introduction......Page 20
About the Author......Page 24
Acknowledgments......Page 26
Part I - How data is driving business disruption, transformation, and reinvention......Page 28
Chapter 1 - Separating leaders from laggards......Page 30
1.1 - An era of disruption, transformation, and reinvention......Page 31
1.1.1 - Understand disruption......Page 33
1.1.2 - Build capabilities by design......Page 34
1.2 - Characteristics of data-centric companies......Page 37
1.2.1 - They are data harvesters......Page 38
1.2.2 - They live an analytic culture......Page 39
1.2.3 - They think long term......Page 40
1.2.5 - And, they stay connected......Page 41
1.3.1 - Netflix......Page 42
1.3.2 - Facebook......Page 43
1.3.3 - Starbucks......Page 46
1.3.4 - Amazon......Page 47
1.4 - From the data to the dashboard......Page 48
1.5 - Navigating this book......Page 50
References......Page 51
2.1 - The discovery imperative......Page 54
2.2 - Business intelligence versus data discovery......Page 55
2.3 - The business impact of the discovery culture......Page 58
2.3.1 - Fostering a discovery culture......Page 60
2.3.2 - Discovery culture challenges......Page 61
2.4 - The role of friction in discovery......Page 62
2.5.1.1 - Spreadsheets......Page 64
2.5.2.1 - Multifaceted, search mode......Page 65
2.6 - SQL: The language of discovery......Page 66
2.6.1 - SQL capability and compatibility......Page 68
2.6.4 - Thinking long-term......Page 69
Reference......Page 70
Chapter 3 - From self-service to self-sufficiency......Page 72
3.1 - From self-service to self-sufficiency......Page 73
3.1.1 - A lesson from frozen yogurt......Page 76
3.2 - Discovery capabilities for self-sufficiency......Page 78
3.2.2 - Discovery environment......Page 79
3.2.3 - Discovery access......Page 80
3.3 - (Information) enablement technology......Page 81
3.4 - The data scientist: the sexiest of them all......Page 82
3.4.1 - The blended aptitudes of data scientist, PhD......Page 83
3.5 - Moving forward......Page 86
References......Page 87
4.1 - Introduction......Page 88
4.2 - Aftershocks of the Big Data Revolution......Page 89
4.3 - Big data’s personal impact......Page 91
4.3.1 - Social graphs......Page 92
4.3.2 - Data ownership and data memory......Page 97
4.3.3 - Passive data collection......Page 98
4.4 - Exercise: ethical data scenarios......Page 99
4.4.2 - Scenario two......Page 100
4.5.1 - Facebook......Page 101
4.5.2 - Target......Page 103
4.5.3 - Ashley Madison......Page 104
4.6 - The data visualization hippocratic oath......Page 105
4.7 - Ethics requires Leadership......Page 106
4.7.2 - Cultural competence and diversity in data......Page 107
4.7.4 - Finally, critical thinking competence......Page 108
4.8 - Closing thoughts......Page 109
References......Page 110
5.2 - The new data science student......Page 112
5.2.2 - Continuous learning......Page 114
5.2.4.1 - A snapshot of integrated education in technology......Page 115
5.2.5 - New university programs and vendor participation......Page 116
5.3 - The new digital native......Page 118
5.3.1 - Leadership qualities of the millennial leader......Page 121
5.3.2 - Humility......Page 122
5.4 - Addressing the gender gap—women in IT......Page 123
5.5 - New roles for new leaders......Page 126
5.5.1 - The data visualization analyst......Page 127
5.5.2 - The visualization curator/designer......Page 129
5.5.4 - The chief storytelling officer......Page 130
5.6 - The steps beyond......Page 131
References......Page 132
Part II - Communicating data visually......Page 134
Chapter 6 - Visual communication and literacy......Page 136
6.1 - The history of visual communication......Page 137
6.2 - Visual memorability and retention......Page 143
6.2.1 - Imagery analysis......Page 146
6.3 - Extending visual literacy to visual data literacy......Page 148
6.3.1 - Visual learning key to data literacy......Page 149
6.3.2 - Incorporating data into visual learning......Page 151
6.3.4 - Encode......Page 152
6.3.5 - Special considerations for visual data literacy educators......Page 153
6.4 - From communication to conversation......Page 154
References......Page 156
Chapter 7 - Visual storytelling with data......Page 158
7.1 - The storytelling paradigm: defining the data story......Page 159
7.2 - A brief bit of story psychology......Page 160
7.2.1.1 - Survival of the fittest......Page 161
7.2.2 - A cognitive perspective......Page 162
7.3 - The data narrative framework......Page 163
7.3.1 - Inverted journalism pyramid......Page 165
7.4.1 - First, find data that supports your story......Page 166
7.4.2 - Then, layer information for understanding......Page 167
7.4.4 - …But Beware the False Reveal......Page 168
7.4.5 - Tell It fast – and make it mobile......Page 171
7.5.1 - The storytelling paradox......Page 172
7.6 - Storytelling’s secret ingredient: the audience......Page 176
References......Page 177
Chapter 8 - The importance of visual design......Page 180
8.1 - Data visualization is a beautiful science......Page 181
8.2 - Key cognitive elements in visualization......Page 182
8.2.1 - Patterns and organization......Page 183
8.2.2 - Color use......Page 185
8.2.3 - Counting and numerosity......Page 186
8.3 - Color 101......Page 187
8.3.1 - Color contingencies......Page 188
8.3.1.1 - Color blindness......Page 189
8.3.1.2.1 - Color symbolism......Page 190
8.3.1.2.2 - His and hers colors......Page 191
8.4.1 - Lines......Page 192
8.4.2 - Shapes......Page 194
8.4.3 - Typography......Page 195
8.5 - Visualization constraints......Page 198
8.5.1 - The eye (candy) exam......Page 199
References......Page 201
Chapter 9 - The data visualization continuum......Page 204
9.1 - Data visualization definition......Page 205
9.2 - The power of viz......Page 207
9.3.1 - Explanatory......Page 210
9.3.2 - Exploratory......Page 211
9.3.3 - The hybrid model......Page 213
9.4 - The rise of the infographic......Page 214
9.5 - Choosing the chart: a quick diagnostic......Page 215
9.6.1 - Rule #1: visualize with goals in mind......Page 220
9.6.2 - Rule #2: know your data......Page 221
9.6.5 - Rule #5: choose the right chart......Page 222
9.6.6 - Rule #6: practice responsible charting......Page 223
9.6.7 - Rule #7: use labels wisely......Page 224
9.6.8 - Rule #8: design with care......Page 225
9.6.10 - Rule #10: edit and revise before you share......Page 226
References......Page 227
Part III - Building the foundation for data visualization......Page 228
Chapter 10 - Architecting for discovery......Page 230
10.1.1 - The integration challenge......Page 231
10.1.2 - The management challenge......Page 232
10.1.3 - The discovery challenge......Page 233
10.2.1 - Choosing data abstraction for unification......Page 234
10.2.1.2 - Putting abstraction in context......Page 235
10.2.2 - Centralizing context in the cloud......Page 236
10.2.3 - Getting visual with self-service......Page 237
10.3 - Designing for frictionless......Page 239
10.4 - Enabling governed data discovery......Page 240
10.4.1.1 - Checkpoint #1: to enable faster data exploration......Page 241
10.4.1.2 - Checkpoint #2: at the time of insight......Page 243
10.4.1.3 - Checkpoint #3: for institutionalizing and ongoing sharing......Page 244
10.6 - Anatomy of a visual discovery application......Page 245
10.7 - The convergence of visual analytics and visual discovery......Page 246
References......Page 247
Chapter 11 - Data visualization as a core competency......Page 248
11.1.1 - Benefits......Page 249
11.2 - Introducing the Data Visualization Competency Center......Page 250
11.2.1 - Decision Design Standards......Page 252
11.2.2 - Establish internal data visualization best practices......Page 253
11.2.3 - Develop a collaborative, collective culture......Page 254
11.2.4 - A brief word on mobile......Page 255
11.3 - Tools for the DVCC......Page 256
11.4 - Socializing the DVCC......Page 258
11.4.1 - Crafting a vision statement......Page 259
11.4.2 - Building the core team......Page 260
11.5 - The implementation roadmap......Page 261
11.5.1 - Aligning business outcomes to drivers and efficiencies......Page 262
11.5.2 - Establishing success criteria for each phase......Page 263
11.5.4 - Architecting an all-inclusive communication plan......Page 264
11.6 - Four tips to DVCC success......Page 265
11.6.2 - Establish a common business vocabulary......Page 266
11.6.3 - Know who your data stewards are......Page 267
11.6.4 - Respect the limitations of self-service visualization......Page 268
References......Page 269
Chapter 12 - Visual discovery by design......Page 270
12.1 - Data discovery by design......Page 271
12.1.1 - Power of disruption (in the tool landscape)......Page 272
12.1.2 - The value of speed......Page 274
12.1.3 - Discovery by design......Page 276
12.1.3.2 - Designed for collaboration......Page 277
12.1.3.4 - Designed for mobility......Page 278
12.1.4 - Tying it together......Page 279
12.2 - Leveraging visualization for assessable, meaningful, actionable......Page 281
Reference......Page 282
Chapter 13 - The need for visualization in the Internet of Things......Page 284
13.1 - Understanding “data in motion”......Page 285
13.2 - The Internet of Things primer......Page 286
13.3 - The art of animation......Page 288
13.3.1 - Beyond animation to streaming data visualization......Page 291
13.3.1.1 - Visual sedimentation......Page 292
13.4 - Human centered design in mobile(First) strategies......Page 294
13.4.1 - Wearable devices......Page 295
13.4.1.2 - Apple watch......Page 296
13.4.1.3 - FitBit......Page 297
13.4.2.2 - Wearables for him......Page 298
13.5 - Interaction for playful data visualization......Page 299
13.5.2 - Interactivity......Page 302
References......Page 305
Appendix - Recommended Reading......Page 308
Subject Index......Page 310
Back Cover......Page 321