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The Intelligent Enterprise in the Era of Big Data
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Table of Contents

Preface xiii

Acknowledgments xix

Part I Challenges of the Digital Age 1 The Crisis Has Not Gone Away: Opportunity Beckons 3

1.1 Introduction 3

1.2 Challenges with Current Technology Paradigms: Chronic Issues of Time to Market and Flexibility 9

1.3 The Emergence of Packaged Applications 11

1.4 The New Front: Information; Big Data is Not New; What is New is Unstructured Information 12

1.5 Enterprise Architecture: Current State and Implications 14

1.6 The Intelligent Enterprise of Tomorrow 15

References 15

Part II An Architecture for the Intelligent Enterprise 2 Efficiency and Agility 19

2.1 Introduction 19

2.2 The Process-Oriented Enterprise 19

2.2.1 Becoming Process Oriented 23

2.2.2 Why Must We Choose? 24

2.2.3 Design and Execution 25

2.3 Role of Outsourcing in Creating Efficiency and Agility 26

2.4 Role of Technology in Efficiency and Agility 29

2.4.1 Current Challenges with Technology 30

2.4.2 BPM Software 30

2.4.3 Role of Methodology 32

2.4.4 Agile Not Equal to Agility 33

2.5 A New Technology Paradigm for Efficiency and Agility 35

2.5.1 Technology and the Process-Oriented Architecture 35

2.5.2 RAGE AITM 38

2.5.3 RAGE Abstract Components 39

2.5.4 RIMTM - An Actionable, Dynamic Methodology 40

2.5.5 Real Time Software Development 43

2.6 Summary 44

References 46

3 Insight and Intelligence 51

3.1 Introduction 51

3.2 The Excitement Around Big Data 52

3.3 Information Overload, Asymmetry, and Decision Making 54

3.3.1 Information Overload 54

3.3.2 Information Asymmetry 56

3.4 Artificial Intelligence to the Rescue 59

3.4.1 A Taxonomy of AI Problem Types and Methods 60

3.4.2 AI Solution Outcomes 61

3.4.3 AI Solution Methods 66

3.5 Machine Learning Using Computational Statistics 68

3.5.1 Decision Trees 69

3.5.2 Artificial Neural Networks (ANNs) 71

Kernel Machines 74

3.5.3 Deep Learning Architectures 76

3.6 Machine Learning with Natural Language 78

3.6.1 The "Bag-of-Words" Representation 78

3.6.2 Sentiment Analysis 80

3.6.3 Knowledge Acquisition and Representation 82

3.7 A Deep Learning Framework for Learning and Inference 83

3.7.1 Conceptual Semantic Network 89

3.7.2 Knowledge Discoverer 91

3.7.3 Computational Linguistics Engine 92

3.7.4 Impact Analysis 95

3.7.5 Formulation of the Impact Analysis Problem 96

3.8 Summary 96

References 99

4 The Intelligent Enterprise of Tomorrow 109

4.1 The Road to an Intelligent Enterprise 109

4.2 Enterprise Architecture Evolution 113

4.2.1 Technology Evolution 113

4.2.2 Flexible, Near Real Time Software Development 121

4.2.3 Machine Intelligence 122

4.2.4 E4.0 Architecture 123

4.3 Humans versus Machines 126

4.4 Summary 130

Appendix: A Five-Step Approach to an Intelligent Enterprise 130

References 131

Part III Real World Case Studies 5 Active Advising with Intelligent Agents 135

5.1 Introduction 135

5.2 The Investment Advisory Market 135

5.3 What Do Investors Really Need and Want 137

5.4 Challenges with High-Touch Advisory Services 137

5.4.1 Questions of Value and Interest 137

5.4.2 The Massive "Wealth Transfer" Phenomenon 138

5.4.3 The Rise of Robo-Advisors 139

5.4.4 Technology for HNWI's Unique Needs 140

5.5 Active Advising - A Framework Based on Machine Intelligence 140

5.6 A Holistic View of the Client's Needs 142

5.7 Summary 149

Appendix: The RAGE Business Process Automation and Cognitive Intelligence Platform 150

References 151

6 Finding Alpha in Markets 153

6.1 Introduction 153

6.2 Information Asymmetry and Financial Markets 154

6.3 Machine Intelligence and Alpha 157

6.4 How Well Does it Work? 162

6.4.1 Data 162

6.4.2 Measuring Lead-Lag Relationship 162

6.4.3 Back-Testing Results 164

6.5 Summary 167

Appendix: Snapshot of the Operating Model at a Sector Level for the Oil and Gas Industry 168

References 168

7 Will Financial Auditors Become Extinct? 171

7.1 Introduction 171

7.2 The External Financial Audit 173

7.2.1 Client Engagement 173

7.2.2 Audit Planning 173

7.2.3 Fieldwork 174

7.2.4 Review and Draft 176

7.3 An Intelligent Audit Machine 176

7.3.1 Client Engagement 179

7.3.2 Audit Planning 180

7.3.3 Fieldwork 181

7.3.4 Existence Tests 181

7.3.5 Rights and Obligations 182

7.3.6 Substantive Analytical Procedures 182

7.3.7 Closing Balance Tests 182

7.3.8 Analyze and Issue Financials 183

7.3.9 Audit Standards 183

7.3.10 Workflow/Configuration 183

7.4 Summary 184

References 184

Index 187

About the Author

Venkat Srinivasan, PhD, is Chairman and Chief Executive Officer of RAGE Frameworks, Inc., which supports the creation of intelligent business process automation solutions and cognitive intelligence solutions for global corporations. He is an entrepreneur and holds several patents in the area of knowledge-based technology architectures. He is the author of two edited volumes and over 30 peer-reviewed publications. He has served as an associate professor in the College of Business Administration at Northeastern University.

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