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Big Data MBA: Driving Business Strategies with Data Science

Big Data MBA: Driving Business Strategies with Data Science (Paperback)

Bill Schmarzo (지은이)
John Wiley & Sons Inc
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Big Data MBA: Driving Business Strategies with Data Science
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· 제목 : Big Data MBA: Driving Business Strategies with Data Science (Paperback) 
· 분류 : 외국도서 > 컴퓨터 > 데이터베이스 관리 > 데이터 마이닝
· ISBN : 9781119181118
· 쪽수 : 320쪽
· 출판일 : 2015-12-21

목차

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

저자소개

빌 슈마르조 (지은이)    정보 더보기
‘빅데이터 학장(Dean of Big Data)’으로 통하는 빌 슈마르조는 현재 델 테크놀로지스의 고객 데이터 혁신 책임자다. 여가 시간에는 아이오와 주립대학교와 코(Coe) 대학(아이오와주 시더래피즈)에 출강한다. 또 샌프란시스코 대학교 경영대학원 석좌 연구원, 아일랜드 골웨이 대학(National University of Ireland Galway) 경영경제학부 명예교수로 활동하고 있다. 골웨이 대학에서는 빅데이터 MBA와 ‘데이터 과학자처럼 생각하기’ 같은 과목을 가르치면서 학생들에게 멘토링도 하고 있다.『빅데이터(Big Data: Understanding How Data Powers Big Business)』,『빅데이터 MBA(Big Data MBA: Driving Business Strategies with Data Science)』,『데이터 과학자처럼 사고하는 기술(The Art of Thinking Like a Data Scientist)』, 『데이터, 분석, 디지털 변형의 경제학(The Economics ofData, Analytics, and Digital Transformation)』 등 4권의 책을 썼다. ‘데이터 사이언스 센트럴(Data Science Central)’에 300개 이상의 블로그 글을 썼으며, 팟캐스트에도 셀 수 없을 만큼 많이 출연했다. 수많은 기조 연설과 대학 강의를 했으며, 데이터 과학, 인공지능, 데이터 경제학, 디자인 사고, 팀 권한 부여를 주제로 활발한 소셜 미디어 활동을 하고 있다.
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