The Visual Imperative: Creating a Visual Culture of Data Discovery

Description

Data is powerful. It separates leaders from laggards and it drives business disruption, transformation, and reinvention. Today’s most progressive companies are using the power of data to propel their industries into new areas of innovation, specialization, and optimization. The horsepower of new tools and technologies have provided more opportunities than ever to harness, integrate, and interact with massive amounts of disparate data for business insights and value – something that will only continue in the era of the Internet of Things. And, as a new breed of tech-savvy and digitally native knowledge workers rise to the ranks of data scientist and visual analyst, the needs and demands of the people working with data are changing, too. The world of data is changing fast. And, it’s becoming more visual. Visual insights are becoming increasingly dominant in information management, and with the reinvigorated role of data visualization, this imperative is a driving force to creating a visual culture of data discovery. The traditional standards of data visualizations are making way for richer, more robust and more advanced visualizations and new ways of seeing and interacting with data. However, while data visualization is a critical tool to exploring and understanding bigger and more diverse and dynamic data, by understanding and embracing our human hardwiring for visual communication and storytelling and properly incorporating key design principles and evolving best practices, we take the next step forward to transform data visualizations from tools into unique visual information assets. Discusses several years of in-depth industry research and presents vendor tools, approaches, and methodologies in discovery, visualization, and visual analyticsProvides practicable and use case-based experience from advisory work with Fortune 100 and 500 companies across multiple verticalsPresents the next-generation of visual discovery, data storytelling, and the Five Steps to Data Storytelling with VisualizationExplains the Convergence of Visual Analytics and Visual discovery, including how to use tools such as R in statistical and analytic modelingCovers emerging technologies such as streaming visualization in the IOT (Internet of Things) and streaming animation 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