Unique prospective on the big data analytics phenomenon for both business and IT professionals The availability of Big Data, low-cost commodity hardware and new information management and analytics software has produced a unique moment in the history of business. The convergence of these trends means that we have the capabilities required to analyze astonishing data sets quickly and cost-effectively for the first time in history. These capabilities are neither theoretical nor trivial. They represent a genuine leap forward and a clear opportunity to realize enormous gains in terms of…mehr
Unique prospective on the big data analytics phenomenon for both business and IT professionals
The availability of Big Data, low-cost commodity hardware and new information management and analytics software has produced a unique moment in the history of business. The convergence of these trends means that we have the capabilities required to analyze astonishing data sets quickly and cost-effectively for the first time in history. These capabilities are neither theoretical nor trivial. They represent a genuine leap forward and a clear opportunity to realize enormous gains in terms of efficiency, productivity, revenue and profitability.
The Age of Big Data is here, and these are truly revolutionary times. This timely book looks at cutting-edge companies supporting an exciting new generation of business analytics. Learn more about the trends in big data and how they are impacting the business world (Risk, Marketing, Healthcare, Financial Services, etc.) Explains this new technology and how companies can use them effectively to gather the data that they need and glean critical insights Explores relevant topics such as data privacy, data visualization, unstructured data, crowd sourcing data scientists, cloud computing for big data, and much more.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Considered one of the top sales and marketing executives in the business analytics space, MICHAEL MINELLI is Vice President, Information Services, for MasterCard Advisors. The majority of his sixteen years of analytics industry experience was at SAS, where he spent over eleven years helping clients with large-scale analytic projects related to marketing, risk, supply chain, and finance. MICHELE CHAMBERS is currently in the Big Data Analytics startup world and was formerly the General Manager & Vice President of Big Data Analytics at IBM, where her team was responsible for working with customers to fully exploit the IBM Big Data Platform. AMBIGA DHIRAJ is the Head of Client Delivery for Mu Sigma, where she leads their delivery teams to solve high-impact business problems in the areas of marketing, supply chain, and risk analytics for market-leading companies across multiple verticals.
Inhaltsangabe
Foreword xiii Preface xix Acknowledgments xxi Chapter 1 What is Big Data and Why is It Important? 1 A Flood of Mythic "Start-Up" Proportions 4 Big Data is More Than Merely Big 5 Why Now? 6 A Convergence of Key Trends 7 Relatively Speaking . . . 9 A Wider Variety of Data 10 The Expanding Universe of Unstructured Data 11 Setting the Tone at the Top 15 Notes 18 Chapter 2 Industry Examples of Big Data 19 Digital Marketing and the Non-line World 19 Don't Abdicate Relationships 22 Is IT Losing Control of Web Analytics? 23 Database Marketers, Pioneers of Big Data 24 Big Data and the New School of Marketing 27 Consumers Have Changed. So Must Marketers. 28 The Right Approach: Cross-Channel Lifecycle Marketing 28 Social and Affiliate Marketing 30 Empowering Marketing with Social Intelligence 31 Fraud and Big Data 34 Risk and Big Data 37 Credit Risk Management 38 Big Data and Algorithmic Trading 40 Crunching Through Complex Interrelated Data 41 Intraday Risk Analytics, a Constant Flow of Big Data 42 Calculating Risk in Marketing 43 Other Industries Benefit from Financial Services' Risk Experience 43 Big Data and Advances in Health Care 44 "Disruptive Analytics" 46 A Holistic Value Proposition 47 BI is Not Data Science 49 Pioneering New Frontiers in Medicine 50 Advertising and Big Data: From Papyrus to Seeing Somebody 51 Big Data Feeds the Modern-Day Donald Draper 52 Reach, Resonance, and Reaction 53 The Need to Act Quickly (Real-Time When Possible) 54 Measurement Can Be Tricky 55 Content Delivery Matters Too 56 Optimization and Marketing Mixed Modeling 56 Beard's Take on the Three Big Data Vs in Advertising 57 Using Consumer Products as a Doorway 58 Notes 59 Chapter 3 Big Data Technology 61 The Elephant in the Room: Hadoop's Parallel World 61 Old vs. New Approaches 64 Data Discovery: Work the Way People's Minds Work 65 Open-Source Technology for Big Data Analytics 67 The Cloud and Big Data 69 Predictive Analytics Moves into the Limelight 70 Software as a Service BI 72 Mobile Business Intelligence is Going Mainstream 73 Ease of Mobile Application Deployment 75 Crowdsourcing Analytics 76 Inter- and Trans-Firewall Analytics 77 R&D Approach Helps Adopt New Technology 80 Adding Big Data Technology into the Mix 81 Big Data Technology Terms 83 Data Size 101 86 Notes 88 Chapter 4 Information Management 89 The Big Data Foundation 89 Big Data Computing Platforms (or Computing Platforms That Handle the Big Data Analytics Tsunami) 92 Big Data Computation 93 More on Big Data Storage 96 Big Data Computational Limitations 96 Big Data Emerging Technologies 97 Chapter 5 Business Analytics 99 The Last Mile in Data Analysis 101 Geospatial Intelligence Will Make Your Life Better 103 Listening: Is It Signal or Noise? 106 Consumption of Analytics 108 From Creation to Consumption 110 Visualizing: How to Make It Consumable? 110 Organizations are Using Data Visualization as a Way to Take Immediate Action 116 Moving from Sampling to Using All the Data 121 Thinking Outside the Box 122 360° Modeling 122 Need for Speed 122 Let's Get Scrappy 123 What Technology is Available? 124 Moving from Beyond the Tools to Analytic Applications 125 Notes 125 Chapter 6 The People Part of the Equation 127 Rise of the Data Scientist 128 Learning over Knowing 130 Agility 131 Scale and Convergence 131 Multidisciplinary Talent 131 Innovation 132 Cost Effectiveness 132 Using Deep Math, Science, and Computer Science 133 The 90/10 Rule and Critical Thinking 136 Analytic Talent and Executive Buy-in 137 Developing Decision Sciences Talent 139 Holistic View of Analytics 140 Creating Talent for Decision Sciences 142 Creating a Culture That Nurtures Decision Sciences Talent 144 Setting Up the Right Organizational Structure for Institutionalizing Analytics 146 Chapter 7 Data Privacy and Ethics 151 The Privacy Landscape 152 The Great Data Grab isn't New 152 Preferences, Personalization, and Relationships 153 Rights and Responsibility 154 Playing in a Global Sandbox 159 Conscientious and Conscious Responsibility 161 Privacy May Be the Wrong Focus 162 Can Data Be Anonymized? 164 Balancing for Counterintelligence 165 Now What? 165 Notes 167 Conclusion 169 Recommended Resources 175 About the Authors 177 Index 179
Foreword xiii Preface xix Acknowledgments xxi Chapter 1 What is Big Data and Why is It Important? 1 A Flood of Mythic "Start-Up" Proportions 4 Big Data is More Than Merely Big 5 Why Now? 6 A Convergence of Key Trends 7 Relatively Speaking . . . 9 A Wider Variety of Data 10 The Expanding Universe of Unstructured Data 11 Setting the Tone at the Top 15 Notes 18 Chapter 2 Industry Examples of Big Data 19 Digital Marketing and the Non-line World 19 Don't Abdicate Relationships 22 Is IT Losing Control of Web Analytics? 23 Database Marketers, Pioneers of Big Data 24 Big Data and the New School of Marketing 27 Consumers Have Changed. So Must Marketers. 28 The Right Approach: Cross-Channel Lifecycle Marketing 28 Social and Affiliate Marketing 30 Empowering Marketing with Social Intelligence 31 Fraud and Big Data 34 Risk and Big Data 37 Credit Risk Management 38 Big Data and Algorithmic Trading 40 Crunching Through Complex Interrelated Data 41 Intraday Risk Analytics, a Constant Flow of Big Data 42 Calculating Risk in Marketing 43 Other Industries Benefit from Financial Services' Risk Experience 43 Big Data and Advances in Health Care 44 "Disruptive Analytics" 46 A Holistic Value Proposition 47 BI is Not Data Science 49 Pioneering New Frontiers in Medicine 50 Advertising and Big Data: From Papyrus to Seeing Somebody 51 Big Data Feeds the Modern-Day Donald Draper 52 Reach, Resonance, and Reaction 53 The Need to Act Quickly (Real-Time When Possible) 54 Measurement Can Be Tricky 55 Content Delivery Matters Too 56 Optimization and Marketing Mixed Modeling 56 Beard's Take on the Three Big Data Vs in Advertising 57 Using Consumer Products as a Doorway 58 Notes 59 Chapter 3 Big Data Technology 61 The Elephant in the Room: Hadoop's Parallel World 61 Old vs. New Approaches 64 Data Discovery: Work the Way People's Minds Work 65 Open-Source Technology for Big Data Analytics 67 The Cloud and Big Data 69 Predictive Analytics Moves into the Limelight 70 Software as a Service BI 72 Mobile Business Intelligence is Going Mainstream 73 Ease of Mobile Application Deployment 75 Crowdsourcing Analytics 76 Inter- and Trans-Firewall Analytics 77 R&D Approach Helps Adopt New Technology 80 Adding Big Data Technology into the Mix 81 Big Data Technology Terms 83 Data Size 101 86 Notes 88 Chapter 4 Information Management 89 The Big Data Foundation 89 Big Data Computing Platforms (or Computing Platforms That Handle the Big Data Analytics Tsunami) 92 Big Data Computation 93 More on Big Data Storage 96 Big Data Computational Limitations 96 Big Data Emerging Technologies 97 Chapter 5 Business Analytics 99 The Last Mile in Data Analysis 101 Geospatial Intelligence Will Make Your Life Better 103 Listening: Is It Signal or Noise? 106 Consumption of Analytics 108 From Creation to Consumption 110 Visualizing: How to Make It Consumable? 110 Organizations are Using Data Visualization as a Way to Take Immediate Action 116 Moving from Sampling to Using All the Data 121 Thinking Outside the Box 122 360° Modeling 122 Need for Speed 122 Let's Get Scrappy 123 What Technology is Available? 124 Moving from Beyond the Tools to Analytic Applications 125 Notes 125 Chapter 6 The People Part of the Equation 127 Rise of the Data Scientist 128 Learning over Knowing 130 Agility 131 Scale and Convergence 131 Multidisciplinary Talent 131 Innovation 132 Cost Effectiveness 132 Using Deep Math, Science, and Computer Science 133 The 90/10 Rule and Critical Thinking 136 Analytic Talent and Executive Buy-in 137 Developing Decision Sciences Talent 139 Holistic View of Analytics 140 Creating Talent for Decision Sciences 142 Creating a Culture That Nurtures Decision Sciences Talent 144 Setting Up the Right Organizational Structure for Institutionalizing Analytics 146 Chapter 7 Data Privacy and Ethics 151 The Privacy Landscape 152 The Great Data Grab isn't New 152 Preferences, Personalization, and Relationships 153 Rights and Responsibility 154 Playing in a Global Sandbox 159 Conscientious and Conscious Responsibility 161 Privacy May Be the Wrong Focus 162 Can Data Be Anonymized? 164 Balancing for Counterintelligence 165 Now What? 165 Notes 167 Conclusion 169 Recommended Resources 175 About the Authors 177 Index 179
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