The Intelligent Enterprise in the Era of Big Data (eBook)

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.

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 / 18