Big Data MBA Driving Business Strategies with Data Science

Integrate big data into business to drive competitive advantage and sustainable success

Big Data MBA brings insight and expertise to leveraging big data in business so you can harness the power of analytics and gain a true business advantage. Based on a practical framework with supporting methodology and hands-on exercises, this book helps identify where and how big data can help you transform your business. You'll learn how to exploit new sources of customer, product, and operational data, coupled with advanced analytics and data science, to optimize key processes, uncover monetization opportunities, and create new sources of competitive differentiation. The discussion includes guidelines for operationalizing analytics, optimal organizational structure, and using analytic insights throughout your organization's user experience to customers and front-end employees alike. You'll learn to “think like a data scientist” as you build upon the decisions your business is trying to make, the hypotheses you need to test, and the predictions you need to produce.

Business stakeholders no longer need to relinquish control of data and analytics to IT. In fact, they must champion the organization's data collection and analysis efforts. This book is a primer on the business approach to analytics, providing the practical understanding you need to convert data into opportunity.

• Understand where and how to leverage big data
• Integrate analytics into everyday operations
• Structure your organization to drive analytic insights
• Optimize processes, uncover opportunities, and stand out from the rest
• Help business stakeholders to “think like a data scientist”
• Understand appropriate business application of different analytic techniques

If you want data to transform your business, you need to know how to put it to use. Big Data MBA shows you how to implement big data and analytics to make better decisions.

BILL SCHMARZO is the chief technology officer of the Big Data Practice of EMC Global Services. He is responsible for setting the strategy and defining the big data service offerings and capabilities for EMC Global Services. He also works directly with organizations to help them identify where and how to start their big data journeys. In addition, Schmarzo is the author of Big Data: Understanding How Data Powers Big Business from Wiley.

Introduction xxiii

Part I Business Potential of Big Data CHAPTER 1

Chapter 1 The Big Data Business Mandate 3

Big Data MBA Introduction 4

Focus Big Data on Driving Competitive Differentiation 6

Leveraging Technology to Power Competitive Differentiation 7

History Lesson on Economic-Driven Business Transformation 7

Critical Importance of “Thinking Differently” 10

Don’t Think Big Data Technology, Think Business Transformation 10

Don’t Think Business Intelligence, Think Data Science 11

Don’t Think Data Warehouse, Think Data Lake 11

Don’t Think “What Happened,” Think “What Will Happen” 12

Don’t Think HIPPO, Think Collaboration 14

Summary 14

Homework Assignment 15

Chapter 2 Big Data Business Model Maturity Index 17

Introducing the Big Data Business Model Maturity Index 18

Phase 1: Business Monitoring 20

Phase 2: Business Insights 21

Phase 3: Business Optimization 25

Phase 4: Data Monetization 27

Phase 5: Business Metamorphosis 28

Big Data Business Model Maturity Index Lessons Learned 30

Lesson 1: Focus Initial Big Data Efforts Internally 30

Lesson 2: Leverage Insights to Create New Monetization Opportunities 31

Lesson 3: Preparing for Organizational Transformation 32

Summary 33

Homework Assignment 34

Chapter 3 The Big Data Strategy Document 35

Establishing Common Business Terminology 37

Introducing the Big Data Strategy Document 37

Identifying the Organization’s Key Business Initiatives 39

What’s Important to Chipotle? 40

Identify Key Business Entities and Key Decisions 41

Identify Financial Drivers (Use Cases) 45

Identify and Prioritize Data Sources 48

Introducing the Prioritization Matrix 51

Using the Big Data Strategy Document to Win the World Series 52

Summary 57

Homework Assignment 58

Chapter 4 The Importance of the User Experience 61

The Unintelligent User Experience 62

Capture the Key Decisions 63

Support the User Decisions 63

Consumer Case Study: Improve Customer Engagement 64

Business Case Study: Enable Frontline Employees 66

Store Manager Dashboard 67

Sample Use Case: Competitive Analysis 69

Additional Use Cases 70

B2B Case Study: Make the Channel More Effective 71

The Advisors Are Your Partners—Make Them Successful 72

Financial Advisor Case Study 72

Informational Sections of Financial Advisor Dashboard 74

Recommendations Section of Financial Advisor Dashboard 77

Summary 80

Homework Assignment 81

Part II Data Science 83

Chapter 5 Differences Between Business Intelligence and Data Science 85

What Is Data Science? 86

BI Versus Data Science: V The Questions Are Different 87

BI Questions 88

Data Science Questions 88

The Analyst Characteristics Are Different 89

The Analytic Approaches Are Different 91

Business Intelligence Analyst Engagement Process 91

The Data Scientist Engagement Process 93

The Data Models Are Different 96

Data Modeling for BI 96

Data Modeling for Data Science 98

The View of the Business Is Different 100

Summary 104

Homework Assignment 104

Chapter 6 Data Science 101 107

Data Science Case Study Setup 107

Fundamental Exploratory Analytics 110

Trend Analysis 110

Boxplots 112

Geographical (Spatial) Analysis 113

Pairs Plot 114

Time Series Decomposition 115

Analytic Algorithms and Models 116

Cluster Analysis 116

Normal Curve Equivalent (NCE) Analysis 117

Association Analysis 119

Graph Analysis 121

Text Mining 122

Sentiment Analysis 123

Traverse Pattern Analysis 124

Decision Tree Classifier Analysis 125

Cohorts Analysis 126

Summary 128

Homework Assignment 131

Chapter 7 The Data Lake 133

Introduction to the Data Lake 134

Characteristics of a Business-Ready Data Lake 136

Using the Data Lake to Cross the Analytics Chasm 137

Modernize Your Data and Analytics Environment 140

Action #1: Create a Hadoop-Based Data Lake 140

Action #2: Introduce the Analytics Sandbox 141

Action #3: Off-Load ETL Processes from Data Warehouses 142

Analytics Hub and Spoke Analytics Architecture 143

Early Learnings 145

Lesson #1: The Name Is Not Important 145

Lesson #2: It’s Data Lake, Not Data Lakes 146

Lesson #3: Data Governance Is a Life Cycle, Not a Project 147

Lesson #4: Data Lake Sits Before Your Data Warehouse, Not After It 148

What Does the Future Hold? 149

Summary 150

Homework Assignment 151

Part III Data Science for Business Stakeholders 153

Chapter 8 Thinking Like a Data Scientist 155

The Process of Thinking Like a Data Scientist 157

Step 1: Identify Key Business Initiative 157

Step 2: Develop Business Stakeholder Personas 158

Step 3: Identify Strategic Nouns 160

Step 4: Capture Business Decisions 161

Step 5: Brainstorm Business Questions 162

Step 8: Putting Analytics into Action 166

Summary 168

Homework Assignment 169

Chapter 9 “By” Analysis Technique 171

“By” Analysis Introduction 172

“By” Analysis Exercise 174

Foot Locker Use Case “By” Analysis 178

Summary 181

Homework Assignment 182

Chapter 10 Score Development Technique 183

Definition of a Score 184

FICO Score Example 185

Other Industry Score Examples 188

LeBron James Exercise Continued 189

Foot Locker Example Continued 193

Summary 197

Homework Assignment 197

Chapter 11 Monetization Exercise 199

Fitness Tracker Monetization Example 200

Step 1: Understand Product Usage 200

Step 2: Develop Stakeholder Personas 201

Step 3: Brainstorm Potential Recommendations 203

Step 4: Identify Supporting Data Sources 204

Step 5: Prioritize Monetization Opportunities 206

Step 6: Develop Monetization Plan 208

Summary 209

Homework Assignment 210

Chapter 12 Metamorphosis Exercise 211

Business Metamorphosis Review 212

Business Metamorphosis Exercise 213

Articulate the Business Metamorphosis Vision 214

Understand Your Customers 215

Articulate Value Propositions 215

Define Data and Analytic Requirements 216

Business Metamorphosis in Health Care 223

Summary 226

Homework Assignment 227

Part IV Building Cross-Organizational Support 229

Chapter 13 Power of Envisioning 231

Envisioning: Fueling Creative Thinking 232

Big Data Vision Workshop Process 232

Pre-engagement Research 233

Business Stakeholder Interviews 234

Explore with Data Science 235

Workshop 236

Setting Up the Workshop 239

The Prioritization Matrix 241

Summary 243

Homework Assignment 244

Chapter 14 Organizational Ramifications 245

Chief Data Monetization Offi cer 245

CDMO Responsibilities 246

CDMO Organization 246

Analytics Center of Excellence 247

CDMO Leadership 248

Privacy, Trust, and Decision Governance 248

Privacy Issues = Trust Issues 249

Decision Governance 250

Unleashing Organizational Creativity 251

Summary 253

Homework Assignment 254

Chapter 15 Stories 255

Customer and Employee Analytics 257

Product and Device Analytics 261

Network and Operational Analytics 263

Characteristics of a Good Business Story 265

Summary 266

Homework Assignment 267

Index 269

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