Description

Book Synopsis

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.

    Table of Contents

    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

Big Data Big Analytics

    Product form

    £31.20

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    RRP £39.00 – you save £7.80 (20%)

    Order before 4pm today for delivery by Fri 19 Jun 2026.

    A Hardback by Michael Minelli, Michele Chambers, Ambiga Dhiraj


      View other formats and editions of Big Data Big Analytics by Michael Minelli

      Publisher: John Wiley & Sons Inc
      Publication Date: 19/02/2013
      ISBN13: 9781118147603, 978-1118147603
      ISBN10: 111814760X

      Description

      Book Synopsis

      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.

        Table of Contents

        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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