{"product_id":"big-data-mba-isbn-9781119181118","title":"Big Data MBA","description":"\u003cp\u003e\u003cb\u003eIntegrate big data into business to drive competitive advantage and sustainable success\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eBig Data MBA\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003eBusiness 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.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eUnderstand \u003ci\u003ewhere\u003c\/i\u003e and \u003ci\u003ehow\u003c\/i\u003e to leverage big data\u003c\/li\u003e \u003cli\u003eIntegrate analytics into everyday operations\u003c\/li\u003e \u003cli\u003eStructure your organization to drive analytic insights\u003c\/li\u003e \u003cli\u003eOptimize processes, uncover opportunities, and stand out from the rest\u003c\/li\u003e \u003cli\u003eHelp business stakeholders to “think like a data scientist”\u003c\/li\u003e \u003cli\u003eUnderstand appropriate business application of different analytic techniques\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eIf you want data to transform your business, you need to know how to put it to use. \u003ci\u003eBig Data MBA\u003c\/i\u003e shows you how to implement big data and analytics to make better decisions.\u003c\/p\u003e \u003cp\u003eIntroduction xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Business Potential of Big Data Chapter 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 The Big Data Business Mandate 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBig Data MBA Introduction 4\u003c\/p\u003e \u003cp\u003eFocus Big Data on Driving Competitive Differentiation 6\u003c\/p\u003e \u003cp\u003eLeveraging Technology to Power Competitive Differentiation 7\u003c\/p\u003e \u003cp\u003eHistory Lesson on Economic-Driven Business Transformation 7\u003c\/p\u003e \u003cp\u003eCritical Importance of “Thinking Differently” 10\u003c\/p\u003e \u003cp\u003eDon’t Think Big Data Technology, Think Business Transformation 10\u003c\/p\u003e \u003cp\u003eDon’t Think Business Intelligence, Think Data Science 11\u003c\/p\u003e \u003cp\u003eDon’t Think Data Warehouse, Think Data Lake 11\u003c\/p\u003e \u003cp\u003eDon’t Think “What Happened,” Think “What Will Happen” 12\u003c\/p\u003e \u003cp\u003eDon’t Think HIPPO, Think Collaboration 14\u003c\/p\u003e \u003cp\u003eSummary 14\u003c\/p\u003e \u003cp\u003eHomework Assignment 15\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Big Data Business Model Maturity Index 17\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroducing the Big Data Business Model Maturity Index 18\u003c\/p\u003e \u003cp\u003ePhase 1: Business Monitoring 20\u003c\/p\u003e \u003cp\u003ePhase 2: Business Insights 21\u003c\/p\u003e \u003cp\u003ePhase 3: Business Optimization 25\u003c\/p\u003e \u003cp\u003ePhase 4: Data Monetization 27\u003c\/p\u003e \u003cp\u003ePhase 5: Business Metamorphosis 28\u003c\/p\u003e \u003cp\u003eBig Data Business Model Maturity Index Lessons Learned 30\u003c\/p\u003e \u003cp\u003eLesson 1: Focus Initial Big Data Efforts Internally 30\u003c\/p\u003e \u003cp\u003eLesson 2: Leverage Insights to Create New Monetization Opportunities 31\u003c\/p\u003e \u003cp\u003eLesson 3: Preparing for Organizational Transformation 32\u003c\/p\u003e \u003cp\u003eSummary 33\u003c\/p\u003e \u003cp\u003eHomework Assignment 34\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 The Big Data Strategy Document 35\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEstablishing Common Business Terminology 37\u003c\/p\u003e \u003cp\u003eIntroducing the Big Data Strategy Document 37\u003c\/p\u003e \u003cp\u003eIdentifying the Organization’s Key Business Initiatives 39\u003c\/p\u003e \u003cp\u003eWhat’s Important to Chipotle? 40\u003c\/p\u003e \u003cp\u003eIdentify Key Business Entities and Key Decisions 41\u003c\/p\u003e \u003cp\u003eIdentify Financial Drivers (Use Cases) 45\u003c\/p\u003e \u003cp\u003eIdentify and Prioritize Data Sources 48\u003c\/p\u003e \u003cp\u003eIntroducing the Prioritization Matrix 51\u003c\/p\u003e \u003cp\u003eUsing the Big Data Strategy Document to Win the World Series 52\u003c\/p\u003e \u003cp\u003eSummary 57\u003c\/p\u003e \u003cp\u003eHomework Assignment 58\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 The Importance of the User Experience 61\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Unintelligent User Experience 62\u003c\/p\u003e \u003cp\u003eCapture the Key Decisions 63\u003c\/p\u003e \u003cp\u003eSupport the User Decisions 63\u003c\/p\u003e \u003cp\u003eConsumer Case Study: Improve Customer Engagement 64\u003c\/p\u003e \u003cp\u003eBusiness Case Study: Enable Frontline Employees 66\u003c\/p\u003e \u003cp\u003eStore Manager Dashboard 67\u003c\/p\u003e \u003cp\u003eSample Use Case: Competitive Analysis 69\u003c\/p\u003e \u003cp\u003eAdditional Use Cases 70\u003c\/p\u003e \u003cp\u003eB2B Case Study: Make the Channel More Effective 71\u003c\/p\u003e \u003cp\u003eThe Advisors Are Your Partners—Make Them Successful 72\u003c\/p\u003e \u003cp\u003eFinancial Advisor Case Study 72\u003c\/p\u003e \u003cp\u003eInformational Sections of Financial Advisor Dashboard 74\u003c\/p\u003e \u003cp\u003eRecommendations Section of Financial Advisor Dashboard 77\u003c\/p\u003e \u003cp\u003eSummary 80\u003c\/p\u003e \u003cp\u003eHomework Assignment 81\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Data Science 83\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Differences Between Business Intelligence and Data Science 85\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat Is Data Science? 86\u003c\/p\u003e \u003cp\u003eBI Versus Data Science: The Questions Are Different 87\u003c\/p\u003e \u003cp\u003eBI Questions 88\u003c\/p\u003e \u003cp\u003eData Science Questions 88\u003c\/p\u003e \u003cp\u003eThe Analyst Characteristics Are Different 89\u003c\/p\u003e \u003cp\u003eThe Analytic Approaches Are Different 91\u003c\/p\u003e \u003cp\u003eBusiness Intelligence Analyst Engagement Process 91\u003c\/p\u003e \u003cp\u003eThe Data Scientist Engagement Process 93\u003c\/p\u003e \u003cp\u003eThe Data Models Are Different 96\u003c\/p\u003e \u003cp\u003eData Modeling for BI 96\u003c\/p\u003e \u003cp\u003eData Modeling for Data Science 98\u003c\/p\u003e \u003cp\u003eThe View of the Business Is Different 100\u003c\/p\u003e \u003cp\u003eSummary 104\u003c\/p\u003e \u003cp\u003eHomework Assignment 104\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Data Science 101 107\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData Science Case Study Setup 107\u003c\/p\u003e \u003cp\u003eFundamental Exploratory Analytics 110\u003c\/p\u003e \u003cp\u003eTrend Analysis 110\u003c\/p\u003e \u003cp\u003eBoxplots 112\u003c\/p\u003e \u003cp\u003eGeographical (Spatial) Analysis 113\u003c\/p\u003e \u003cp\u003ePairs Plot 114\u003c\/p\u003e \u003cp\u003eTime Series Decomposition 115\u003c\/p\u003e \u003cp\u003eAnalytic Algorithms and Models 116\u003c\/p\u003e \u003cp\u003eCluster Analysis 116\u003c\/p\u003e \u003cp\u003eNormal Curve Equivalent (NCE) Analysis 117\u003c\/p\u003e \u003cp\u003eAssociation Analysis 119\u003c\/p\u003e \u003cp\u003eGraph Analysis 121\u003c\/p\u003e \u003cp\u003eText Mining 122\u003c\/p\u003e \u003cp\u003eSentiment Analysis 123\u003c\/p\u003e \u003cp\u003eTraverse Pattern Analysis 124\u003c\/p\u003e \u003cp\u003eDecision Tree Classifier Analysis 125\u003c\/p\u003e \u003cp\u003eCohorts Analysis 126\u003c\/p\u003e \u003cp\u003eSummary 128\u003c\/p\u003e \u003cp\u003eHomework Assignment 131\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 The Data Lake 133\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction to the Data Lake 134\u003c\/p\u003e \u003cp\u003eCharacteristics of a Business-Ready Data Lake 136\u003c\/p\u003e \u003cp\u003eUsing the Data Lake to Cross the Analytics Chasm 137\u003c\/p\u003e \u003cp\u003eModernize Your Data and Analytics Environment 140\u003c\/p\u003e \u003cp\u003eAction #1: Create a Hadoop-Based Data Lake 140\u003c\/p\u003e \u003cp\u003eAction #2: Introduce the Analytics Sandbox 141\u003c\/p\u003e \u003cp\u003eAction #3: Off-Load ETL Processes from Data Warehouses 142\u003c\/p\u003e \u003cp\u003eAnalytics Hub and Spoke Analytics Architecture 143\u003c\/p\u003e \u003cp\u003eEarly Learnings 145\u003c\/p\u003e \u003cp\u003eLesson #1: The Name Is Not Important 145\u003c\/p\u003e \u003cp\u003eLesson #2: It’s Data Lake, Not Data Lakes 146\u003c\/p\u003e \u003cp\u003eLesson #3: Data Governance Is a Life Cycle, Not a Project 147\u003c\/p\u003e \u003cp\u003eLesson #4: Data Lake Sits Before Your Data Warehouse, Not After It 148\u003c\/p\u003e \u003cp\u003eWhat Does the Future Hold? 149\u003c\/p\u003e \u003cp\u003eSummary 150\u003c\/p\u003e \u003cp\u003eHomework Assignment 151\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Data Science for Business Stakeholders 153\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Thinking Like a Data Scientist 155\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Process of Thinking Like a Data Scientist 157\u003c\/p\u003e \u003cp\u003eStep 1: Identify Key Business Initiative 157\u003c\/p\u003e \u003cp\u003eStep 2: Develop Business Stakeholder Personas 158\u003c\/p\u003e \u003cp\u003eStep 3: Identify Strategic Nouns 160\u003c\/p\u003e \u003cp\u003eStep 4: Capture Business Decisions 161\u003c\/p\u003e \u003cp\u003eStep 5: Brainstorm Business Questions 162\u003c\/p\u003e \u003cp\u003eStep 8: Putting Analytics into Action 166\u003c\/p\u003e \u003cp\u003eSummary 168\u003c\/p\u003e \u003cp\u003eHomework Assignment 169\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 “By” Analysis Technique 171\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e“By” Analysis Introduction 172\u003c\/p\u003e \u003cp\u003e“By” Analysis Exercise 174\u003c\/p\u003e \u003cp\u003eFoot Locker Use Case “By” Analysis 178\u003c\/p\u003e \u003cp\u003eSummary 181\u003c\/p\u003e \u003cp\u003eHomework Assignment 182\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Score Development Technique 183\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDefinition of a Score 184\u003c\/p\u003e \u003cp\u003eFICO Score Example 185\u003c\/p\u003e \u003cp\u003eOther Industry Score Examples 188\u003c\/p\u003e \u003cp\u003eLeBron James Exercise Continued 189\u003c\/p\u003e \u003cp\u003eFoot Locker Example Continued 193\u003c\/p\u003e \u003cp\u003eSummary 197\u003c\/p\u003e \u003cp\u003eHomework Assignment 197\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 Monetization Exercise 199\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFitness Tracker Monetization Example 200\u003c\/p\u003e \u003cp\u003eStep 1: Understand Product Usage 200\u003c\/p\u003e \u003cp\u003eStep 2: Develop Stakeholder Personas 201\u003c\/p\u003e \u003cp\u003eStep 3: Brainstorm Potential Recommendations 203\u003c\/p\u003e \u003cp\u003eStep 4: Identify Supporting Data Sources 204\u003c\/p\u003e \u003cp\u003eStep 5: Prioritize Monetization Opportunities 206\u003c\/p\u003e \u003cp\u003eStep 6: Develop Monetization Plan 208\u003c\/p\u003e \u003cp\u003eSummary 209\u003c\/p\u003e \u003cp\u003eHomework Assignment 210\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 Metamorphosis Exercise 211\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBusiness Metamorphosis Review 212\u003c\/p\u003e \u003cp\u003eBusiness Metamorphosis Exercise 213\u003c\/p\u003e \u003cp\u003eArticulate the Business Metamorphosis Vision 214\u003c\/p\u003e \u003cp\u003eUnderstand Your Customers 215\u003c\/p\u003e \u003cp\u003eArticulate Value Propositions 215\u003c\/p\u003e \u003cp\u003eDefine Data and Analytic Requirements 216\u003c\/p\u003e \u003cp\u003eBusiness Metamorphosis in Health Care 223\u003c\/p\u003e \u003cp\u003eSummary 226\u003c\/p\u003e \u003cp\u003eHomework Assignment 227\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV Building Cross-organizational Support 229\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13 Power of Envisioning 231\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEnvisioning: Fueling Creative Thinking 232\u003c\/p\u003e \u003cp\u003eBig Data Vision Workshop Process 232\u003c\/p\u003e \u003cp\u003ePre-engagement Research 233\u003c\/p\u003e \u003cp\u003eBusiness Stakeholder Interviews 234\u003c\/p\u003e \u003cp\u003eExplore with Data Science 235\u003c\/p\u003e \u003cp\u003eWorkshop 236\u003c\/p\u003e \u003cp\u003eSetting Up the Workshop 239\u003c\/p\u003e \u003cp\u003eThe Prioritization Matrix 241\u003c\/p\u003e \u003cp\u003eSummary 243\u003c\/p\u003e \u003cp\u003eHomework Assignment 244\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14 Organizational Ramifications 245\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChief Data Monetization Officer 245\u003c\/p\u003e \u003cp\u003eCDMO Responsibilities 246\u003c\/p\u003e \u003cp\u003eCDMO Organization 246\u003c\/p\u003e \u003cp\u003eAnalytics Center of Excellence 247\u003c\/p\u003e \u003cp\u003eCDMO Leadership 248\u003c\/p\u003e \u003cp\u003ePrivacy, Trust, and Decision Governance 248\u003c\/p\u003e \u003cp\u003ePrivacy Issues = Trust Issues 249\u003c\/p\u003e \u003cp\u003eDecision Governance 250\u003c\/p\u003e \u003cp\u003eUnleashing Organizational Creativity 251\u003c\/p\u003e \u003cp\u003eSummary 253\u003c\/p\u003e \u003cp\u003eHomework Assignment 254\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 15 Stories 255\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCustomer and Employee Analytics 257\u003c\/p\u003e \u003cp\u003eProduct and Device Analytics 261\u003c\/p\u003e \u003cp\u003eNetwork and Operational Analytics 263\u003c\/p\u003e \u003cp\u003eCharacteristics of a Good Business Story 265\u003c\/p\u003e \u003cp\u003eSummary 266\u003c\/p\u003e \u003cp\u003eHomework Assignment 267\u003c\/p\u003e \u003cp\u003eIndex 269\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eBILL SCHMARZO\u003c\/b\u003e 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 \u003ci\u003eBig Data: Understanding How Data Powers Big Business\u003c\/i\u003e from Wiley.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003ePraise for Bill Schmarzo and \u003ci\u003eBig Data MBA\u003c\/i\u003e\u003c\/b\u003e  \u003c\/p\u003e\u003cp\u003e\"Practical information from Bill Schmarzo on leveraging big data for a competitive advantage is priceless. His extensive experience in the field of information management and his ability to provide real world examples of how to position your business for success uniquely qualifies him as an expert. Bill's first book, \u003ci\u003eBig Data: Understanding How Data Powers Big Business\u003c\/i\u003e, is insightful and serves as required reading for the MBA\/MSIS course on \u003ci\u003eBusiness Intelligence and Data Warehousing\u003c\/i\u003e that I am teaching.\"\u003cbr\u003e \u003cb\u003e—Jonathan Wu,\u003c\/b\u003e Chairman and Co-Founder, BASE Consulting Group  \u003c\/p\u003e\u003cp\u003e\"Based on over three decades of experience, I firmly believe business stakeholders need to be in the drivers' seats, while collaborating with their IT counterparts, to ensure a successful analytic initiative. Bill's …  latest book allows you to tap into his decades of expertise and incredibly valuable insights in this space.\"\u003cbr\u003e \u003cb\u003e—Margy Ross,\u003c\/b\u003e President at Kimball Group  \u003c\/p\u003e\u003cp\u003e\"Bill Schmarzo is a sound Big Data leader and a gift to the academic profession. Bill is passionate about sharing his knowledge and he can simplify the most complex topic and make it fun and exciting.\"\u003cbr\u003e \u003cb\u003e—Mouwafac Sidaoui,\u003c\/b\u003e Professor and Chairman of Business Analytics and Information Systems at the School of Management at the University of San Francisco  \u003c\/p\u003e\u003cp\u003e\"The market has been lacking a book that addresses the most important source of data value: how to use data and analytics as a business manager. Avoiding platitudes and vague hand-waving advice, this book provides a guide for applying data to solve business problems. Most books lead with technical advice, the wrong place to begin. This book delivers useful guidance on where and how to start, what's important and what one needs to know as a nontechnical manager.\"\u003cbr\u003e \u003cb\u003e—Mark Madsen,\u003c\/b\u003e President, Third Nature, Inc.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eVisit the companion website at\u003c\/b\u003e www.wiley.com\/go\/bigdatamba\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988810645733,"sku":"NP9781119181118","price":42.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119181118.jpg?v=1761781680","url":"https:\/\/k12savings.com\/products\/big-data-mba-isbn-9781119181118","provider":"K12savings","version":"1.0","type":"link"}