{"product_id":"big-data-revolution-isbn-9781118943717","title":"Big Data Revolution","description":"\u003cb\u003eExploit the power and potential of Big Data to revolutionize business outcomes\u003cbr\u003e \u003cbr\u003e \u003c\/b\u003e  \u003cp\u003eBig Data Revolution is a guide to improving performance, making better decisions, and transforming business through the effective use of Big Data. In this collaborative work by an IBM Vice President of Big Data Products and an Oxford Research Fellow, this book presents inside stories that demonstrate the power and potential of Big Data within the business realm. Readers are guided through tried-and-true methodologies for getting more out of data, and using it to the utmost advantage. This book describes the major trends emerging in the field, the pitfalls and triumphs being experienced, and the many considerations surrounding Big Data, all while guiding readers toward better decision making from the perspective of a data scientist.\u003c\/p\u003e \u003cp\u003eCompanies are generating data faster than ever before, and managing that data has become a major challenge. With the right strategy, Big Data can be a powerful tool for creating effective business solutions – but deep understanding is key when applying it to individual business needs. Big Data Revolution provides the insight executives need to incorporate Big Data into a better business strategy, improving outcomes with innovation and efficient use of technology.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eExamine the major emerging patterns in Big Data\u003c\/li\u003e \u003cli\u003eConsider the debate surrounding the ethical use of data\u003c\/li\u003e \u003cli\u003eRecognize patterns and improve personal and organizational performance\u003c\/li\u003e \u003cli\u003eMake more informed decisions with quantifiable results\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eIn an information society, it is becoming increasingly important to make sense of data in an economically viable way. It can drive new revenue streams and give companies a competitive advantage, providing a way forward for businesses navigating an increasingly complex marketplace. Big Data Revolution provides expert insight on the tool that can revolutionize industries.\u003c\/p\u003e \u003cp\u003ePrologue 1\u003c\/p\u003e \u003cp\u003eBerkeley, 1930s 1\u003c\/p\u003e \u003cp\u003ePattern Recognition 2\u003c\/p\u003e \u003cp\u003eNelson Peltz 3\u003c\/p\u003e \u003cp\u003eCommitting to One Percent 5\u003c\/p\u003e \u003cp\u003eThe Big Data Revolution 6\u003c\/p\u003e \u003cp\u003eIntroduction 7\u003c\/p\u003e \u003cp\u003eStorytelling 7\u003c\/p\u003e \u003cp\u003eObjective 7\u003c\/p\u003e \u003cp\u003eOutline 8\u003c\/p\u003e \u003cp\u003ePart I “The Revolution Starts Now: 9 Industries Transforming with Data” 8\u003c\/p\u003e \u003cp\u003ePart II “Learning from Patterns in Big Data” 11\u003c\/p\u003e \u003cp\u003ePart III “Leading the Revolution” 11\u003c\/p\u003e \u003cp\u003eStorytelling (Continued) 13\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I: the Revolution Starts Now:\u003c\/b\u003e \u003cb\u003e9 Industries Transforming With Data 15\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1: Transforming Farms with Data 17\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCalifornia, 2013 17\u003c\/p\u003e \u003cp\u003eBrief History of Farming 18\u003c\/p\u003e \u003cp\u003eThe Data Era 19\u003c\/p\u003e \u003cp\u003ePotato Farming 20\u003c\/p\u003e \u003cp\u003ePrecision Farming 21\u003c\/p\u003e \u003cp\u003eCapturing Farm Data 22\u003c\/p\u003e \u003cp\u003eDeere \u0026amp; Company Versus Monsanto 24\u003c\/p\u003e \u003cp\u003eIntegrated Farming Systems 25\u003c\/p\u003e \u003cp\u003eData Prevails 26\u003c\/p\u003e \u003cp\u003eThe Climate Corporation 26\u003c\/p\u003e \u003cp\u003eGrowsafe Systems 27\u003c\/p\u003e \u003cp\u003eFarm of the Future 27\u003c\/p\u003e \u003cp\u003eCalifornia, 2013 (Continued) 29\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2: Why Doctors Will Have Math Degrees 31\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnited States, 2014 31\u003c\/p\u003e \u003cp\u003eThe History of Medical Education 32\u003c\/p\u003e \u003cp\u003eScientific Method 32\u003c\/p\u003e \u003cp\u003eRise of Specialists 33\u003c\/p\u003e \u003cp\u003eWe Have a Problem 34\u003c\/p\u003e \u003cp\u003eBen Goldacre 35\u003c\/p\u003e \u003cp\u003eVinod Khosla 35\u003c\/p\u003e \u003cp\u003eThe Data Era 36\u003c\/p\u003e \u003cp\u003eCollecting Data 36\u003c\/p\u003e \u003cp\u003eTelemedicine 38\u003c\/p\u003e \u003cp\u003eInnovating with Data 40\u003c\/p\u003e \u003cp\u003eImplications of a Data-Driven Medical World 42\u003c\/p\u003e \u003cp\u003eThe Future of Medical School 42\u003c\/p\u003e \u003cp\u003eA Typical Medical School 42\u003c\/p\u003e \u003cp\u003eA Medical School for the Data Era 43\u003c\/p\u003e \u003cp\u003eUnited States, 2030 44\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3: Revolutionizing Insurance: Why Actuaries Will Become Data Scientists 45\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMiddle of Somewhere, 2012 45\u003c\/p\u003e \u003cp\u003eShort History of Property \u0026amp; Casualty Insurance and Underwriting 46\u003c\/p\u003e \u003cp\u003eActuarial Science In Insurance 47\u003c\/p\u003e \u003cp\u003ePensions, Insurance, Leases 49\u003c\/p\u003e \u003cp\u003eCompound Interest 50\u003c\/p\u003e \u003cp\u003eProbability 50\u003c\/p\u003e \u003cp\u003eMortality Data 50\u003c\/p\u003e \u003cp\u003eModern-Day Insurance 51\u003c\/p\u003e \u003cp\u003eEight Weeks to Eight Days 51\u003c\/p\u003e \u003cp\u003eOnline Policies 52\u003c\/p\u003e \u003cp\u003eThe Data Era 52\u003c\/p\u003e \u003cp\u003eDynamic Risk Management 52\u003c\/p\u003e \u003cp\u003eCatastrophe Risk 54\u003c\/p\u003e \u003cp\u003eOpen Access Modeling 55\u003c\/p\u003e \u003cp\u003eOpportunities 56\u003c\/p\u003e \u003cp\u003eMiddle of Somewhere, 2012 (Continued) 58\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4: Personalizing Retail and Fashion 59\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eKarolina 59\u003c\/p\u003e \u003cp\u003eA Brief History of Retail 60\u003c\/p\u003e \u003cp\u003eRetail Eras 60\u003c\/p\u003e \u003cp\u003eAristide Boucicaut 61\u003c\/p\u003e \u003cp\u003eThe Shift 62\u003c\/p\u003e \u003cp\u003eThe Data Era 63\u003c\/p\u003e \u003cp\u003eStitch Fix 63\u003c\/p\u003e \u003cp\u003eKeaton Row 65\u003c\/p\u003e \u003cp\u003eZara 66\u003c\/p\u003e \u003cp\u003eKarolina (Continued) 67\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5: Transforming Customer Relationships with Data 69\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBuying a House 69\u003c\/p\u003e \u003cp\u003eBrief History of Customer Service 70\u003c\/p\u003e \u003cp\u003eCustomer Service Over Time 70\u003c\/p\u003e \u003cp\u003eBoeing 72\u003c\/p\u003e \u003cp\u003eFinancial Services 74\u003c\/p\u003e \u003cp\u003eThe Data Era 75\u003c\/p\u003e \u003cp\u003eAn Automobile Manufacturer 76\u003c\/p\u003e \u003cp\u003eZendesk 76\u003c\/p\u003e \u003cp\u003eBuying a House (Continued) 77\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6: Intelligent Machines 79\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDenmark 79\u003c\/p\u003e \u003cp\u003eIntelligent Machines 80\u003c\/p\u003e \u003cp\u003eMachine Data 81\u003c\/p\u003e \u003cp\u003eThe Data Era 82\u003c\/p\u003e \u003cp\u003eGeneral Electric 82\u003c\/p\u003e \u003cp\u003eDrones 84\u003c\/p\u003e \u003cp\u003eTesla 86\u003c\/p\u003e \u003cp\u003eNetworks of Data 87\u003c\/p\u003e \u003cp\u003eDenmark (Continued) 88\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7: Government and Society 89\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEgypt, 2011 89\u003c\/p\u003e \u003cp\u003eSocial Media 90\u003c\/p\u003e \u003cp\u003eIntelligence 90\u003c\/p\u003e \u003cp\u003eSnowden Effect 91\u003c\/p\u003e \u003cp\u003ePrivacy Risk Versus Reward 91\u003c\/p\u003e \u003cp\u003eObservation or Surveillance 93\u003c\/p\u003e \u003cp\u003eDevelopment Targets 93\u003c\/p\u003e \u003cp\u003eOpen Data 95\u003c\/p\u003e \u003cp\u003eHackathons 95\u003c\/p\u003e \u003cp\u003eOpen Access 95\u003c\/p\u003e \u003cp\u003eEnsuring Personal Protection 96\u003c\/p\u003e \u003cp\u003ePrivate Clouds 97\u003c\/p\u003e \u003cp\u003eSanitizing Data 97\u003c\/p\u003e \u003cp\u003eEvidence-Based Policy 97\u003c\/p\u003e \u003cp\u003ePublic-Private Partnerships 98\u003c\/p\u003e \u003cp\u003eImpact Bonds 101\u003c\/p\u003e \u003cp\u003eSocial Impact Bond 102\u003c\/p\u003e \u003cp\u003eDevelopment Impact Bonds 103\u003c\/p\u003e \u003cp\u003eThe Role of Big Data 104\u003c\/p\u003e \u003cp\u003eEgypt, 2011 (Continued) 105\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8: Corporate Sustainability 107\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCity of London 107\u003c\/p\u003e \u003cp\u003eGlobal Megaforces 109\u003c\/p\u003e \u003cp\u003ePopulation 109\u003c\/p\u003e \u003cp\u003eCarbon Footprint 110\u003c\/p\u003e \u003cp\u003eWater Scarcity 110\u003c\/p\u003e \u003cp\u003eEnvironmental Risk 111\u003c\/p\u003e \u003cp\u003eBP and Exxon Mobile 111\u003c\/p\u003e \u003cp\u003eEarly Warning Systems 112\u003c\/p\u003e \u003cp\u003eSocial Media 113\u003c\/p\u003e \u003cp\u003eRisk and Resilience 114\u003c\/p\u003e \u003cp\u003eMeasuring Sustainability 115\u003c\/p\u003e \u003cp\u003eLong-Term Decision Making 116\u003c\/p\u003e \u003cp\u003eStranded Assets 117\u003c\/p\u003e \u003cp\u003eCity of London (Continued) 118\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9: Weather and Energy 119\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIndia, 2012 119\u003c\/p\u003e \u003cp\u003eThe Weather 120\u003c\/p\u003e \u003cp\u003eForecasting the Weather 120\u003c\/p\u003e \u003cp\u003eWhen are Weather Forecasts Wrong? 121\u003c\/p\u003e \u003cp\u003eChaos 122\u003c\/p\u003e \u003cp\u003eEnsemble Forecasts 122\u003c\/p\u003e \u003cp\u003eCommunication 123\u003c\/p\u003e \u003cp\u003eRenewable Energy 124\u003c\/p\u003e \u003cp\u003eSolar, Hydro, and Wind Power 124\u003c\/p\u003e \u003cp\u003eVolatile or Intermittent Supply 125\u003c\/p\u003e \u003cp\u003eEnergy Consumption 126\u003c\/p\u003e \u003cp\u003eSmart Meters 127\u003c\/p\u003e \u003cp\u003eIntelligent Demand-Side Management 128\u003c\/p\u003e \u003cp\u003eIndia, 2012 (Continued) 129\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II: Learning From Patterns in Big Data 131\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10: Pattern Recognition 133\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eElements of Success Rhyme 133\u003c\/p\u003e \u003cp\u003ePattern Recognition: A Gift or Trap? 134\u003c\/p\u003e \u003cp\u003eWhat Fish Teach Us About Pattern Recognition 135\u003c\/p\u003e \u003cp\u003eBayes’ Theorem 135\u003c\/p\u003e \u003cp\u003eTsukiji Market 135\u003c\/p\u003e \u003cp\u003ePattern Recognition 137\u003c\/p\u003e \u003cp\u003eRochester Institute of Technology 137\u003c\/p\u003e \u003cp\u003eA Method for Recognizing Patterns 137\u003c\/p\u003e \u003cp\u003eElements of Success Rhyme (Continued) 140\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11: Why Patterns in Big Data Have Emerged 141\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMeatpacking District 141\u003c\/p\u003e \u003cp\u003eBusiness Models in the Data Era 142\u003c\/p\u003e \u003cp\u003eData as a Competitive Advantage 143\u003c\/p\u003e \u003cp\u003eData Improves Existing Products or Services 145\u003c\/p\u003e \u003cp\u003eData as the Product 145\u003c\/p\u003e \u003cp\u003eDun \u0026amp; Bradstreet 146\u003c\/p\u003e \u003cp\u003eCoStar 148\u003c\/p\u003e \u003cp\u003eIhs 149\u003c\/p\u003e \u003cp\u003eMeatpacking District (Continued) 151\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12: Patterns in Big Data 153\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Data Factor 154\u003c\/p\u003e \u003cp\u003eSummary of Big Data Patterns 155\u003c\/p\u003e \u003cp\u003eRedefining a Skilled Worker 155\u003c\/p\u003e \u003cp\u003eCreating and Utilizing New Sources of Data 156\u003c\/p\u003e \u003cp\u003eBuilding New Data Applications 157\u003c\/p\u003e \u003cp\u003eTransforming and Creating New Business Processes 157\u003c\/p\u003e \u003cp\u003eData Collection for Competitive Advantage 158\u003c\/p\u003e \u003cp\u003eExposing Opinion-Based Biases 159\u003c\/p\u003e \u003cp\u003eReal-Time Monitoring and Decision Making 159\u003c\/p\u003e \u003cp\u003eSocial Networks Leveraging and Creating Data 160\u003c\/p\u003e \u003cp\u003eDeconstructing the Value Chain 161\u003c\/p\u003e \u003cp\u003eNew Product Offerings 161\u003c\/p\u003e \u003cp\u003eBuilding for Customers Instead of Markets 162\u003c\/p\u003e \u003cp\u003eTradeoff Between Privacy and Insight 163\u003c\/p\u003e \u003cp\u003eChanging the Definition of a Product 163\u003c\/p\u003e \u003cp\u003eInverting the Search Paradigm for Data Discovery 164\u003c\/p\u003e \u003cp\u003eData Security 165\u003c\/p\u003e \u003cp\u003eNew Partnerships Founded on Data 165\u003c\/p\u003e \u003cp\u003eShortening the Innovation Lifecycle 166\u003c\/p\u003e \u003cp\u003eDefining New Channels to Market 166\u003c\/p\u003e \u003cp\u003eNew Economic Models 167\u003c\/p\u003e \u003cp\u003eForecasting and Predicting Future Events 168\u003c\/p\u003e \u003cp\u003eChanging Incentives 168\u003c\/p\u003e \u003cp\u003eNew Partnerships (Public\/Private) 169\u003c\/p\u003e \u003cp\u003eReal-Time Monitoring and Decision Making (Early Warning Systems) 169\u003c\/p\u003e \u003cp\u003eA Framework for Big Data Patterns 170\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III: Leading the Revolution 171\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13: The Data Opportunity 173\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat Oil Teaches Us About Data 173\u003c\/p\u003e \u003cp\u003eBain Study 175\u003c\/p\u003e \u003cp\u003eSeizing the Opportunity 176\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14: Porsche 177\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRome 177\u003c\/p\u003e \u003cp\u003eFerdinand Porsche 178\u003c\/p\u003e \u003cp\u003eThe Birth of Porsche 178\u003c\/p\u003e \u003cp\u003eThe Porsche Sports Car 179\u003c\/p\u003e \u003cp\u003ePorsche Today 180\u003c\/p\u003e \u003cp\u003eRome (Continued) 180\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 15: Puma 181\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eHerzogenaurach 181\u003c\/p\u003e \u003cp\u003eAdvertising Wars 182\u003c\/p\u003e \u003cp\u003eJochen Zeitz 182\u003c\/p\u003e \u003cp\u003eEnvironmental Profit and Loss 183\u003c\/p\u003e \u003cp\u003eHerzogenaurach (Continued) 184\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 16: A Methodology for Applying Big Data Patterns 185\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 185\u003c\/p\u003e \u003cp\u003eThe Method 186\u003c\/p\u003e \u003cp\u003eStep 1: Understand Data Assets 187\u003c\/p\u003e \u003cp\u003eThe Patterns 188\u003c\/p\u003e \u003cp\u003eStep 2: Explore Data 191\u003c\/p\u003e \u003cp\u003eChallenges 192\u003c\/p\u003e \u003cp\u003eQuestions 192\u003c\/p\u003e \u003cp\u003eHypotheses 193\u003c\/p\u003e \u003cp\u003eData 193\u003c\/p\u003e \u003cp\u003eModels 193\u003c\/p\u003e \u003cp\u003eStatistical Significance 194\u003c\/p\u003e \u003cp\u003eStep 3: Design the Future 194\u003c\/p\u003e \u003cp\u003eThe Patterns 195\u003c\/p\u003e \u003cp\u003eStep 4: Design a Data-Driven Business Model 197\u003c\/p\u003e \u003cp\u003eThe Patterns 197\u003c\/p\u003e \u003cp\u003eStep 5: Transform Business Processes for the Data Era 199\u003c\/p\u003e \u003cp\u003eThe Patterns 199\u003c\/p\u003e \u003cp\u003eStep 6: Design for Governance and Security 201\u003c\/p\u003e \u003cp\u003eThe Patterns 201\u003c\/p\u003e \u003cp\u003eStep 7: Share Metrics and Incentives 202\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 17: Big Data Architecture 205\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 205\u003c\/p\u003e \u003cp\u003eArchitect for the Future 206\u003c\/p\u003e \u003cp\u003eLessons from Stuttgart 207\u003c\/p\u003e \u003cp\u003eBig Data Reference Architectures 207\u003c\/p\u003e \u003cp\u003eLeveraging Investments in Architecture 208\u003c\/p\u003e \u003cp\u003eBig Data Reference Architectures 211\u003c\/p\u003e \u003cp\u003eBusiness View 212\u003c\/p\u003e \u003cp\u003eLogical View 213\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 18: Business View Reference Architecture 215\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 215\u003c\/p\u003e \u003cp\u003eMen’s Trunk: A Retailer in the Data Era 216\u003c\/p\u003e \u003cp\u003eThe Business View Reference Architecture 217\u003c\/p\u003e \u003cp\u003eAnswer Fabric 218\u003c\/p\u003e \u003cp\u003eData Virtualization 219\u003c\/p\u003e \u003cp\u003eData Engines 220\u003c\/p\u003e \u003cp\u003eManagement 221\u003c\/p\u003e \u003cp\u003eData Governance 221\u003c\/p\u003e \u003cp\u003eUser Interface, Applications, and Business Processes 222\u003c\/p\u003e \u003cp\u003eSummary 222\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 19: Logical View Reference Architecture 223\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 223\u003c\/p\u003e \u003cp\u003eMen’s Trunk: A Retailer in the Data Era (Continued) 224\u003c\/p\u003e \u003cp\u003eThe Logical View Reference Architecture 226\u003c\/p\u003e \u003cp\u003eData Ingest 227\u003c\/p\u003e \u003cp\u003eAnalytics 227\u003c\/p\u003e \u003cp\u003eDiscovery 228\u003c\/p\u003e \u003cp\u003eLanding 228\u003c\/p\u003e \u003cp\u003eOperational Warehouse 229\u003c\/p\u003e \u003cp\u003eInformation Insight 230\u003c\/p\u003e \u003cp\u003eOperational Data 231\u003c\/p\u003e \u003cp\u003eGovernance 231\u003c\/p\u003e \u003cp\u003eMen’s Trunk: A Retailer in the Data Era (Continued) 232\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 20: The Architecture of the Future 233\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMen’s Trunk: A Retailer in the Data Era (Continued) 233\u003c\/p\u003e \u003cp\u003eMen’s Trunk: Applying the Methodology 235\u003c\/p\u003e \u003cp\u003eStep 1: Understand Data Assets 235\u003c\/p\u003e \u003cp\u003eStep 2: Explore the Data 236\u003c\/p\u003e \u003cp\u003eStep 3: Design the Future 237\u003c\/p\u003e \u003cp\u003eStep 4: Design a Data-Driven Business Model 237\u003c\/p\u003e \u003cp\u003eStep 5: Transform Business Processes for the Data Era 237\u003c\/p\u003e \u003cp\u003eStep 6: Design for Governance and Security 237\u003c\/p\u003e \u003cp\u003eStep 7: Share Metrics and Incentives 238\u003c\/p\u003e \u003cp\u003eMen’s Trunk: The Business View Reference Architecture 239\u003c\/p\u003e \u003cp\u003eAnswer Fabric 240\u003c\/p\u003e \u003cp\u003eData Virtualization 241\u003c\/p\u003e \u003cp\u003eData Engines 241\u003c\/p\u003e \u003cp\u003eManagement 242\u003c\/p\u003e \u003cp\u003eData Governance 242\u003c\/p\u003e \u003cp\u003eUser Interface, Applications, and Business Processes 243\u003c\/p\u003e \u003cp\u003eMen’s Trunk: The Logical View Reference Architecture 244\u003c\/p\u003e \u003cp\u003eApproach 244\u003c\/p\u003e \u003cp\u003eMen’s Trunk: A Retailer in the Data Era (Continued) 248\u003c\/p\u003e \u003cp\u003eEpilogue 249\u003c\/p\u003e \u003cp\u003eThe Time is Now 249\u003c\/p\u003e \u003cp\u003eTaking Action 250\u003c\/p\u003e \u003cp\u003eFear not Usual Competitors 251\u003c\/p\u003e \u003cp\u003eThe Future 252\u003c\/p\u003e \u003cp\u003eIndex 255\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eRob Thomas\u003c\/b\u003e is Vice President of Product Development for Big Data and Information Management in IBM Software Group. Previously, he had responsibility for global sales and mergers \u0026amp; acquisitions. \u003cb\u003ePatrick McSharry\u003c\/b\u003e is a Senior Research Fellow at the Smith School of Enterprise and the Environment, Faculty Member of the Oxford Man Institute of Quantitative Finance at Oxford University and Visiting Professor at the Department of Electrical and Computer Engineering, Carnegie Mellon University.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eREVOLUTIONIZE BUSINESS WITH THE POWER OF BIG DATA\u003c\/b\u003e  \u003c\/p\u003e\u003cp\u003eBig Data can be a powerful tool for creating effective business solutions, but formulating and executing the right strategy requires a deep understanding of an increasingly complex subject. \u003ci\u003eBig Data Revolution\u003c\/i\u003e highlights the power, potential, and pitfalls of Big Data, providing the insight you need to improve business outcomes with innovation and the efficient use of technology. Companies are generating data faster than ever before, and that data can be leveraged to transform industries.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003e\u003ci\u003eDrive better business by exploiting Big Data capabilities.\u003c\/i\u003e\u003c\/b\u003e  \u003c\/p\u003e\u003cul\u003e \u003cli\u003eExamine major Big Data patterns and recognize future patterns as they emerge\u003c\/li\u003e \u003cli\u003eDevelop a governance and security strategy for the ethical use of data\u003c\/li\u003e \u003cli\u003eImprove personal and organizational performance with tested methodologies\u003c\/li\u003e \u003cli\u003eMake better, more informed decisions with quantifiable results\u003c\/li\u003e \u003cli\u003eDefine new business processes based on current and future data assets\u003c\/li\u003e \u003c\/ul\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988810744037,"sku":"NP9781118943717","price":19.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118943717.jpg?v=1761781680","url":"https:\/\/k12savings.com\/products\/big-data-revolution-isbn-9781118943717","provider":"K12savings","version":"1.0","type":"link"}