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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.
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.
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: 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 Officer 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
| Erscheinungsjahr: | 2015 |
|---|---|
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| ISBN-13: | 9781119181118 |
| ISBN-10: | 1119181119 |
| Sprache: | Englisch |
| Herstellernummer: | 1W119181110 |
| Einband: | Kartoniert / Broschiert |
| Autor: | Schmarzo, Bill |
| Hersteller: |
Wiley
John Wiley & Sons |
| Maße: | 235 x 191 x 17 mm |
| Von/Mit: | Bill Schmarzo |
| Erscheinungsdatum: | 21.12.2015 |
| Gewicht: | 0,6 kg |
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: 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 Officer 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
| Erscheinungsjahr: | 2015 |
|---|---|
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| ISBN-13: | 9781119181118 |
| ISBN-10: | 1119181119 |
| Sprache: | Englisch |
| Herstellernummer: | 1W119181110 |
| Einband: | Kartoniert / Broschiert |
| Autor: | Schmarzo, Bill |
| Hersteller: |
Wiley
John Wiley & Sons |
| Maße: | 235 x 191 x 17 mm |
| Von/Mit: | Bill Schmarzo |
| Erscheinungsdatum: | 21.12.2015 |
| Gewicht: | 0,6 kg |
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