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<p>Foreword xix</p> <p>Introduction xxi</p> <p><b>Chapter 1 The Last Mile Problem 1</b></p> <p>The Information Age: Driving the Need for Data Fluency 2</p> <p>Data Fluency: Unlock the Potential Energy of Data in Your Organization 4</p> <p>Big Data and Data Metaphors 5</p> <p>Our Data Fluency Framework 7</p> <p>Case Studies: A Window into the Framework for Data Fluency 8</p> <p>Data Consumers: Fantasy Football 8</p> <p>Producers of Data Products: U.S. News 11</p> <p>Organizational-Level Consumers: School District Woes 13</p> <p>Organizational-Level Producers: Insurance Company Bottom Lines 15</p> <p><b>Chapter 2 The Data Fluency Framework 19</b></p> <p>The Data Fluency Framework 21</p> <p>Individuals and the Organization 22</p> <p>Using Data versus Presenting Data 23</p> <p>Element 1: Data Literate Consumers 23</p> <p>Element 2: Data Fluent Producers 24</p> <p>Element 3: The Data Fluent Culture 26</p> <p>Element 4: The Data Product Ecosystem 27</p> <p>Connective Tissue 28</p> <p>Resources for More Depth 28</p> <p>Benefits of the Data Fluent Organization 29</p> <p>How to Use This Framework 30</p> <p>How Organizations Struggle 31</p> <p>Summary 32</p> <p><b>Chapter 3 How Organizations Struggle with Data Fluency 33</b></p> <p>Pitfalls on the Path to Data Fluency 35</p> <p>Report Proliferation 35</p> <p>Balkanized Data 36</p> <p>Data Elitism 38</p> <p>The Supermodel 39</p> <p>Searching for Understanding 40</p> <p>Data Care 42</p> <p>Metric Fixation 43</p> <p>Finding Balance 45</p> <p><b>Chapter 4 A Consumer’s Guide to Understanding Data 47</b></p> <p>Data Products 49</p> <p>Everyday Data Products 51</p> <p>Barriers to Using Data Products 55</p> <p>Jargon 55</p> <p>Not Knowing Where to Start or What to Focus On 57</p> <p>Inconsistency 58</p> <p>Learning the Language of Data 60</p> <p>Atomic Data 60</p> <p>Summarized Data 61</p> <p>Exploring Data Tables 63</p> <p>Rows Tell Stories 63</p> <p>Columns Give the Bigger Picture 64</p> <p>Understanding Charts and Visualizations 64</p> <p>Comprehensibility 65</p> <p>Dissecting Data Products 66</p> <p>Where Does It Come From? 67</p> <p>What Can I Learn from It? 68</p> <p>What Can You Do with It? 74</p> <p>Wrapping Up 77</p> <p><b>Chapter 5 Data Authors: Skilled Designers of Data Presentations 79</b></p> <p>A Rare Skillset 80</p> <p>What You’ll Learn 81</p> <p>Guided Conversations 82</p> <p>Finding Your Purpose and Message 84</p> <p>Let the Data Speak 85</p> <p>Your Objectives 85</p> <p>Your Audience 86</p> <p>Information Discrimination 88</p> <p>Defining Meaningful and Actionable Metrics 92</p> <p>Creating Structure and Flow to Your Data Products 95</p> <p>A Guided Path: Structure and Flow 95</p> <p>Why Structure Matters? 97</p> <p>Structure Options 97</p> <p>Designing Attractive, Easy-to-Understand Data Products 103</p> <p>Form 103</p> <p>Visualizing Your Data 104</p> <p>Color 107</p> <p>Typography 109</p> <p>Wrapping Data in Context 111</p> <p>Language 112</p> <p>Creating Dialogue with Your Data Products 114</p> <p>Your Audience’s Audience 114</p> <p>Data Leading to Dialogue 114</p> <p>Design Principles 115</p> <p>Visualizations 115</p> <p>Design Principles for Data Products 121</p> <p>Compactness and Modularity 121</p> <p>Gradual Reveal 121</p> <p>Guide Attention 122</p> <p>Support Casual Use 122</p> <p>Lead to Action 123</p> <p>Customizable 124</p> <p>Explanation before Information 124</p> <p>Viva the Authors of Data Products 125</p> <p><b>Chapter 6 The Data Fluent Culture 127</b></p> <p>Leadership, Culture, and Communicating Priorities 129</p> <p>Set and Communicate Expectations 130</p> <p>Articulate Specific, Measureable Indicators 131</p> <p>Celebrate Effective Data Use and Products 132</p> <p>Use Data to Inform Decisions and Actions 133</p> <p>Establishing Key Metrics to Rally Around 134</p> <p>What Makes a Good Metric? 134</p> <p>Using Metrics to Drive Organizational Improvement 137</p> <p>Choose a Few Key Metrics at Any Given Level 138</p> <p>Select Key Metrics That Align with the Mission and Vision 138</p> <p>Show Employees That Their Contributions Are Essential 138</p> <p>Reference Key Metrics and Data Analysis When Communicating Goals 139</p> <p>Avoiding Metrics Pitfalls 139</p> <p>Shared Understandings 141</p> <p>Common Vocabulary and Terminology Relating to Organization-Specific Data 143</p> <p>Clear Definitions of Measures 143</p> <p>Standard Forms for Collecting Data 143</p> <p>Understanding and Appreciating Credible, Reliable Data Sources 145</p> <p>Understand the Strengths and Weaknesses of Data Sources 145</p> <p>Provide Transparency into How Data Is Manipulated and Modeled 147</p> <p>Define a Shared Set of Key Metrics 148</p> <p>Understanding the Purpose and Motivation for Data Products 149</p> <p>Everyday Activities 151</p> <p>Data Consumers 152</p> <p>Help Individuals Evaluate Data without Distraction 152</p> <p>Focus on the Message 153</p> <p>Data Products 153</p> <p>Establish Clear Guidelines for Quality Data Products 153</p> <p>Develop a Feedback Mechanism for Data Products to Help Evolve and Improve Content. 155</p> <p>Celebrate Examples of Quality Data Products 156</p> <p>Data Usage 156</p> <p>Encourage Data-Driven Decision-Making 157</p> <p>Evaluating Effective Data Use within the Organization 157</p> <p>Evolution of Data Fluent Cultures 158</p> <p><b>Chapter 7 The Data Product Ecosystem 161</b></p> <p>Data Products for Information Delivery 162</p> <p>Necessary Conditions 163</p> <p>Learning from the App Store 165</p> <p>Demand 167</p> <p>Top-Down Demand Map 167</p> <p>Grassroots Needs 169</p> <p>Where to Begin 169</p> <p>Design 170</p> <p>Objective 170</p> <p>Start with a Style Guide 172</p> <p>Develop 172</p> <p>“It’s a Poor Craftsman Who Blames His Tools” 174</p> <p>Discover 175</p> <p>Objective 176</p> <p>Where to Begin: A Centralized Inventory of Data Products 176</p> <p>Discuss 177</p> <p>Objective 177</p> <p>Where to Begin: Create a Place to Capture Insights 178</p> <p>Distill 179</p> <p>Learning from Wikipedia 179</p> <p>Objective 180</p> <p>What Can You Do Without? 180</p> <p>“Only Connect” 181</p> <p><b>Chapter 8 The Journey to Data Fluency 183</b></p> <p>Why Data Fluency? 185</p> <p>Data Consumers: Creating a Sophisticated Audience 187</p> <p>Data Product Producers: The Skills to Enable Effective Data Communication 188</p> <p>Data Fluent Culture: Building a Shared Understanding of Data 189</p> <p>Data Product Ecosystem: Tools and Processes to Facilitate the Fluid Exchange of Information 189</p> <p>Begin the Journey 190</p> <p>Feature The Data Fluency Inventory 193</p> <p>The Data Fluency Inventory Survey Questions 194</p> <p>Component 1: Data Consumer Literacy 196</p> <p>Use of Data 197</p> <p>Data Skills 198</p> <p>Value Placed on Data 199</p> <p>Component 2: Data Product Author Skills 200</p> <p>Tools 201</p> <p>Skills 202</p> <p>Perceptions and Attitudes 203</p> <p>Component 3: Data Fluent Culture 204</p> <p>Leadership 205</p> <p>Key Metrics 205</p> <p>Shared Understanding and Everyday Data Use 206</p> <p>Component 4: Data Product Ecosystem 207</p> <p>Demand and Design 208</p> <p>Develop 208</p> <p>Discover 209</p> <p>Discuss and Distill 209</p> <p>Summary of the DFI 210</p> <p>DFI Scoring Guide (for Organizations) 210</p> <p>Question Types and Point Values 211</p> <p>Organization Scores 211</p> <p>Component 1: Data Consumer Literacy 211</p> <p>Thoughts for an Organizational Leader 212</p> <p>Component 2: Data Product Authors 212</p> <p>Thoughts for an Organizational Leader 213</p> <p>Component 3: Data Fluent Culture 213</p> <p>Thoughts for an Organizational Leader 214</p> <p>Component 4: Data Product Ecosystem 214</p> <p>Thoughts for an Organizational Leader 214</p> <p>Organizational Scoring Summary 215</p> <p>Scoring Guide (for Individuals) 215</p> <p>What Can You Measure as an Individual? 216</p> <p>Component 1: Data Consumer (Individual Only) 216</p> <p>Component 2: Data Authors (Individual Only) 217</p> <p>Individual Scoring Guide Summary 217</p> <p>DFI Supporting Materials 218</p> <p>Survey Introduction E-mail 218</p> <p>Data Literacy Quiz 219</p> <p><b>Appendix A Designing Data Products 223</b></p> <p>A Checklist for Creating Data Products 224</p> <p>Think Like a Designer 226</p> <p>Designed to Be Used 228</p> <p>Breaking Free of the One-Page Dashboard Rule 229</p> <p>Dashboard Alerts Checklist 231</p> <p>Context: Users Need to Understand How an Alert Is Defined and How It Fits into the Larger Picture 232</p> <p>Cogency: An Alerting System Needs to Avoid Causing Unnecessary Alarm While Delivering Easy-to-Understand Information That Can Be Acted Upon 232</p> <p>Communication: Alerts Must Be Designed to Effectively Capture Attention and Inform 233</p> <p>Control: Advanced Alert System Should Give Users the Ability to Customize and Manage Alerts 233</p> <p>8 Features of Successful Real-time Dashboards 234</p> <p><b>Appendix B Style Guide 237</b></p> <p>Style Guide Sample 1: Fonts 239</p> <p>Style Guide Sample 2: Colors 240</p> <p>Style Guide Sample 3: Date/Number Formatting 241</p> <p>Style Guide Sample 4: Bar Charts 242</p> <p>Style Guide Sample 5: Trend Charts 243</p> <p>Style Guide Sample 6: Tables 244</p> <p>Index 245</p>

<p><b>Zach Gemignani</b> is co-founder and CEO of Juice Analytics. He works with companies with companies to build analytics-savvy organizations and develop analytical tools that focus on getting the human elements right. <b>Chris Gemignani</b> is also co-founder of Juice Analytics and previously Chris was a VP analyst at Citibank and PNC bank. <b>Dr. Richard Galentino</b> is CEO of Stratable, Inc., a strategic planning and organizational development consulting firm. <b>Dr. Patrick Schuermann</b> is a Harvard International Education Policy Fellow. <b>Nathan Yau</b> (Consulting Editor) has a PhD in statistics and is a statistical consultant helping clients make use of their data through visualization. Nathan is the author of the best-selling visualization books, <i>Visualize This</i> and <i>Data Points</i>.

<p><b>POWERFUL DATA THAT GETS RESULTS</b> <p>There is a colossal disconnect between the volumes of data collected by organizations and the people who might make use of it. This gap can be bridged by creating focused and thoughtful data presentation tools, including reports, dashboards, visualizations, or key metrics. Together, these tools and capabilities lead to data fluency, the ability to use the language of data to exchange and explore ideas within your organization. <ul> <li><b>Conquer the different challenges organizations face with data reporting and communication</b></li> <li><b>Find a fit between audience needs and the right data presentations</b></li> <li><b>Design effective data presentations and tools, including choices in visualizations, layout, and styling</b></li> <li><b>Shape the culture of your organization to encourage the use of data in daily activities</b></li> <li><b>Build an ecosystem where quality data presentation solutions can reach their audiences with geometry, traditional charts, maps, color, art, and even humor</b></li> </ul> <p><b>Learn more by visiting the companion website at www.juiceanalytics.com/data-fluency</b>

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