Big Data MBA
Driving Business Strategies with Data Science
(Sprache: Englisch)
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...
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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.
Inhaltsverzeichnis zu „Big Data MBA “
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
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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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Autoren-Porträt von Bill Schmarzo
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.
Bibliographische Angaben
- Autor: Bill Schmarzo
- 2016, 1. Auflage, 320 Seiten, Maße: 19,1 x 23,5 cm, Kartoniert (TB), Englisch
- Verlag: Wiley & Sons
- ISBN-10: 1119181119
- ISBN-13: 9781119181118
- Erscheinungsdatum: 03.10.2017
Sprache:
Englisch
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