Zach Gemignani 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. Chris Gemignani is also co-founder of Juice Analytics and previously Chris was a VP analyst at Citibank and PNC bank. Dr. Richard Galentino is CEO of Stratable, Inc., a strategic planning and organizational development consulting firm. Dr. Patrick Schuermann is a Harvard International Education Policy Fellow. Nathan Yau (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, Visualize This and Data Points.

Foreword xix

Introduction xxi

Chapter 1 The Last Mile Problem 1

The Information Age: Driving the Need for Data Fluency 2

Data Fluency: Unlock the Potential Energy of Data in Your Organization 4

Big Data and Data Metaphors 5

Our Data Fluency Framework 7

Case Studies: A Window into the Framework for Data Fluency 8

Data Consumers: Fantasy Football 8

Producers of Data Products: U S News 11

Organizational-Level Consumers: School District Woes 13

Organizational-Level Producers: Insurance Company Bottom Lines 15

Chapter 2 The Data Fluency Framework 19

The Data Fluency Framework 21

Individuals and the Organization 22

Using Data versus Presenting Data 23

Element 1: Data Literate Consumers 23

Element 2: Data Fluent Producers 24

Element 3: The Data Fluent Culture 26

Element 4: The Data Product Ecosystem 27

Connective Tissue 28

Resources for More Depth 28

Benefits of the Data Fluent Organization 29

How to Use This Framework 30

How Organizations Struggle 31

Summary 32

Chapter 3 How Organizations Struggle with Data Fluency 33

Pitfalls on the Path to Data Fluency 35

Report Proliferation 35

Balkanized Data 36

Data Elitism 38

The Supermodel 39

Searching for Understanding 40

Data Care 42

Metric Fixation 43

Finding Balance 45

Chapter 4 A Consumer's Guide to Understanding Data 47

Data Products 49

Everyday Data Products 51

Barriers to Using Data Products 55

Jargon 55

Not Knowing Where to Start or What to Focus On 57

Inconsistency 58

Learning the Language of Data 60

Atomic Data 60

Summarized Data 61

Exploring Data Tables 63

Rows Tell Stories 63

Columns Give the Bigger Picture 64

Understanding Charts and Visualizations 64

Comprehensibility 65

Dissecting Data Products 66

Where Does It Come From? 67

What Can I Learn from It? 68

What Can You Do with It? 74

Wrapping Up 77

Chapter 5 Data Authors: Skilled Designers of Data Presentations 79

A Rare Skillset 80

What You'll Learn 81

Guided Conversations 82

Finding Your Purpose and Message 84

Let the Data Speak 85

Your Objectives 85

Your Audience 86

Information Discrimination 88

Defining Meaningful and Actionable Metrics 92

Creating Structure and Flow to Your Data Products 95

A Guided Path: Structure and Flow 95

Why Structure Matters? 97

Structure Options 97

Designing Attractive, Easy-to-Understand Data Products 103

Form 103

Visualizing Your Data 104

Color 107

Typography 109

Wrapping Data in Context 111

Language 112

Creating Dialogue with Your Data Products 114

Your Audience's Audience 114

Data Leading to Dialogue 114

Design Principles 115

Visualizations 115

Design Principles for Data Products 121

Compactness and Modularity 121

Gradual Reveal 121

Guide Attention 122

Support Casual Use 122

Lead to Action 123

Customizable 124

Explanation before Information 124

Viva the Authors of Data Products 125

Chapter 6 The Data Fluent Culture 127

Leadership, Culture, and Communicating Priorities 129

Set and Communicate Expectations 130

Articulate Specific, Measureable Indicators 131

Celebrate Effective Data Use and Products 132

Use Data to Inform Decisions and Actions 133

Establishing Key Metrics to Rally Around 134

What Makes a Good Metric? 134

Using Metrics to Drive Organizational Improvement 137

Choose a Few Key Metrics at Any Given Level 138

Select Key Metrics That Align with the Mission and Vision 138

Show Employees That Their Contributions Are Essential 138

Reference Key Metrics and Data Analysis When Communicating Goals 139

Avoiding Metrics Pitfalls 139

Shared Understandings 141

Common Vocabulary and Terminology Relating to Organization-Specific Data 143

Clear Definitions of Measures 143

Standard Forms for Collecting Data 143

Understanding and Appreciating Credible, Reliable Data Sources 145

Understand the Strengths and Weaknesses of Data Sources 145

Provide Transparency into How Data Is Manipulated and Modeled 147

Define a Shared Set of Key Metrics 148

Understanding the Purpose and Motivation for Data Products 149

Everyday Activities 151

Data Consumers 152

Help Individuals Evaluate Data without Distraction 152

Focus on the Message 153

Data Products 153

Establish Clear Guidelines for Quality Data Products 153

Develop a Feedback Mechanism for Data Products to Help Evolve and Improve Content 155

Celebrate Examples of Quality Data Products 156

Data Usage 156

Encourage Data-Driven Decision-Making 157

Evaluating Effective Data Use within the Organization 157

Evolution of Data Fluent Cultures 158

Chapter 7 The Data Product Ecosystem 161

Data Products for Information Delivery 162

Necessary Conditions 163

Learning from the App Store 165

Demand 167

Top-Down Demand Map 167

Grassroots Needs 169

Where to Begin 169

Design 170

Objective 170

Start with a Style Guide 172

Develop 172

"It's a Poor Craftsman Who Blames His Tools" 174

Discover 175

Objective 176

Where to Begin: A Centralized Inventory of Data Products 176

Discuss 177

Objective 177

Where to Begin: Create a Place to Capture Insights 178

Distill 179

Learning from Wikipedia 179

Objective 180

What Can You Do Without? 180

"Only Connect" 181

Chapter 8 The Journey to Data Fluency 183

Why Data Fluency? 185

Data Consumers: Creating a Sophisticated Audience 187

Data Product Producers: The Skills to Enable Effective Data Communication 188

Data Fluent Culture: Building a Shared Understanding of Data 189

Data Product Ecosystem: Tools and Processes to Facilitate the Fluid Exchange of Information 189

Begin the Journey 190

Feature The Data Fluency Inventory 193

The Data Fluency Inventory Survey Questions 194

Component 1: Data Consumer Literacy 196

Use of Data 197

Data Skills 198

Value Placed on Data 199

Component 2: Data Product Author Skills 200

Tools 201

Skills 202

Perceptions and Attitudes 203

Component 3: Data Fluent Culture 204

Leadership 205

Key Metrics 205

Shared Understanding and Everyday Data Use 206

Component 4: Data Product Ecosystem 207

Demand and Design 208

Develop 208

Discover 209

Discuss and Distill 209

Summary of the DFI 210

DFI Scoring Guide (for Organizations) 210

Question Types and Point Values 211

Organization Scores 211

Component 1: Data Consumer Literacy 211

Thoughts for an Organizational Leader 212

Component 2: Data Product Authors 212

Thoughts for an Organizational Leader 213

Component 3: Data Fluent Culture 213

Thoughts for an Organizational Leader 214

Component 4: Data Product Ecosystem 214

Thoughts for an Organizational Leader 214

Organizational Scoring Summary 215

Scoring Guide (for Individuals) 215

What Can You Measure as an Individual? 216

Component 1: Data Consumer (Individual Only) 216

Component 2: Data Authors (Individual Only) 217

Individual Scoring Guide Summary 217

DFI Supporting Materials 218

Survey Introduction E-mail 218

Data Literacy Quiz 219

Appendix A Designing Data Products 223

A Checklist for Creating Data Products 224

Think Like a Designer 226

Designed to Be Used 228

Breaking Free of the One-Page Dashboard Rule 229

Dashboard Alerts Checklist 231

Context: Users Need to Understand How an Alert Is Defined and How It Fits into the Larger Picture 232

Cogency: An Alerting System Needs to Avoid Causing Unnecessary Alarm While Delivering Easy-to- Understand Information That Can Be Acted Upon 232

Communication: Alerts Must Be Designed to Effectively Capture Attention and Inform 233

Control: Advanced Alert System Should Give Users the Ability to Customize and Manage Alerts 233

8 Features of Successful Real-time Dashboards 234

Appendix B Style Guide 237

Style Guide Sample 1: Fonts 239

Style Guide Sample 2: Colors 240

Style Guide Sample 3: Date/Number Formatting 241

Style Guide Sample 4: Bar Charts 242

Style Guide Sample 5: Trend Charts 243

Style Guide Sample 6: Tables 244

Index 245