The Intelligent Enterprise in the Era of Big Data - Venkat Srinivasan - E-Book

The Intelligent Enterprise in the Era of Big Data E-Book

Venkat Srinivasan

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" ... the enterprise of today has changed ... wherever you sit in this new corporation ... Srinivasan gives us a practical and provocative guide for rethinking our business process ... calling us all to action around rapid development of our old, hierarchical structures into flexible customer centric competitive force .... A must read for today's business leader." Mark Nunnelly, Executive Director, MassIT, Commonwealth of Massachusetts and Managing Director, Bain Capital "'Efficiency,' 'agile,' and 'analytics' used to be the rage. Venkat Srinivasan explains in this provocative book why organizations can no longer afford to stop there. They need to move beyond - to be 'intelligent.' It isn't just theory. He's done it." Bharat Anand, Henry R. Byers Professor of Business Administration, Harvard Business School In the era of big data and automation, the book presents a cutting-edge approach to how enterprises should organize and function. Striking a practical balance between theory and practice, The Intelligent Enterprise in the Era of Big Data presents the enterprise architecture that identifies the power of the emerging technology environment. Beginning with an introduction to the key challenges that enterprises face, the book systematically outlines modern enterprise architecture through a detailed discussion of the inseparable elements of such architecture: efficiency, flexibility, and intelligence. This architecture enables rapid responses to market needs by sensing important developments in internal and external environments in real time. Illustrating all of these elements in an integrated fashion, The Intelligent Enterprise in the Era of Big Data also features: * A detailed discussion on issues of time-to-market and flexibility with respect to enterprise application technology * Novel analyses illustrated through extensive real-world case studies to help readers better understand the applicability of the architecture and concepts * Various applications of natural language processing to real-world business transactions * Practical approaches for designing and building intelligent enterprises The Intelligent Enterprise in the Era of Big Data is an appropriate reference for business executives, information technology professionals, data scientists, and management consultants. The book is also an excellent supplementary textbook for upper-undergraduate and graduate-level courses in business intelligence, data mining, big data, and business process automation. "a compelling vision of the next generation of organization--the intelligent enterprise--which will leverage not just big data but also unstructured text and artificial intelligence to optimize internal processes in real time ... a must-read book for CEOs and CTOs in all industries." Ravi Ramamurti, D"Amore-McKim Distinguished Professor of International Business and Strategy, and Director, Center for Emerging Markets, Northeastern University "It is about the brave new world that narrows the gap between technology and business .... The book has practical advice from a thoughtful practitioner. Intelligent automation will be a competitive strength in the future. Will your company be ready?" Victor J. Menezes, Retired Senior Vice Chairman, Citigroup 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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“Big data, machine intelligence, digital age are buzz words thrown around at many a daytime meeting or evening conversation. In this book, Venkat Srinivasan brilliantly and succinctly challenges the organizations of tomorrow to be nimble, intelligent and efficient. And lays out a roadmap for them to succeed. A must read for CEOs, CXOs, consultants and academics who embrace change and are true leaders.”

Dr. Sanjiv Chopra MD, MACP. Professor of Medicine, Harvard Medical School

“Dr. Srinivasan talks to us from a future that he has already seen and in many ways realized through practical applications he describes in his book. This is a breakthrough treatise on Artificial Intelligence in the virtual or non-physical world of Business Processes, de-mystifying, deconstructing and making the cold logic and magic of AI accessible to all. This is a must read for anyone curious about how work will get done in the future, so you can start making informed choices today.”

Joy Dasgupta, SVP, RAGE Frameworks

“For a business leader to constantly deliver superior business performance is daily fodder. The challenge lies in driving change in organizational behaviour. Dr. Srinivasan shifts the paradigm; he provides solutions for what may hitherto have been impossible or prohibitive!”

Sanjay Gupta is CEO, EnglishHelper, Inc. and formerly SVP, American Express

“This book is a must read for business and technology leaders focused on driving deep transformation of their businesses. Venkat has brilliantly outlined practical applications of intelligent machines across the enterprise. The best part, this eloquent narration is based on problems he has solved himself at RAGE Frameworks.”

Vikram Mahidhar, SVP, RAGE Frameworks

“An amazing book addressing the challenges faced by all businesses. Having gone through these challenges myself in my professional career with several global organizations I can totally relate to the book. Business needs are changing at a very fast pace and Dr. Srinivasan has offered very practical solutions. Process oriented solutions are flexible and allows business to adapt quickly to these fast changing requirements. Intelligent automation has the ability to dramatically transform organizations and provide a competitive edge. A must read for business leaders.”

Vivek Sharma, CEO, Piramal Pharma Solutions

“Technology is intended to make business more agile, more efficient. But time and again, this same technology becomes a straitjacket once implemented, and forces the business to adapt, instead of the other way around. The book provides a step-by-step deconstruction of what it takes to be agile, efficient and intelligent. Based on this deconstruction, Venkat develops an alternate architecture that leads to the truly agile, efficient and intelligent enterprise. This is not just theory and concept, but implemented and running at several leading global corporations today. Ignore at your own peril!”

Deepak Verma, Managing Director nv vogt and formerly, CEO, eCredit, Inc.

“Over the last 30 years I've helped a number of companies grow faster than their competitors in many industries. But, in so many ways, the enterprise of today has changed: it's global, its customers have many new expectations for service, it is facing new competition from new business models, and it has a new workforce with different skills and desires. Wherever you sit in this new corporation, Srinivasan gives us a practical and provocative guide for rethinking our business process…using data and user controlled access as a speedy weapon rather than a cumbersome control and calling us all to action around rapid redevelopment of our old, hierarchical structures into flexible customer centric competitive force. A must read for today's business leader.”

Mark Nunnelly, Executive Director, MassIT, Commonwealth of Massachusetts and Managing Director, Bain Capital

“ ‘Efficiency’, ‘agile,’ and ‘analytics’ used to be the rage. Venkat Srinivasan explains in this provocative book why organizations can no longer afford to stop there. They need to move beyond – to be ‘intelligent.’ It isn't just theory. He's done it.”

Bharat Anand, Henry R. Byers Professor of Business Administration, Harvard Business School

“Venkat Srinivasan is one of those rare individuals who combines the intellectual horsepower of an academic, the foresight of a visionary, and the creativity of an entrepreneur. In this book he offers a compelling vision of the next generation of organization—the intelligent enterprise—which will leverage not just big data but also unstructured text and artificial intelligence to optimize internal processes in real time. Say good-bye to software systems that don't talk to one another and cost a fortune to customize, and say hello to the solution that may become the new normal. If the intelligent enterprise seems utopian, read the chapters on how some companies have actually applied this concept with impressive results. Let Srinivasan give you a peep into the future. This is a must-read book for CEOs and CTOs in all industries.”

Ravi Ramamurti, D”Amore-McKim Distinguished Professor of International Business & Strategy, and Director, Center for Emerging Markets, Northeastern U.

“Dr. Venkat Srinivasan has written a book aimed at business professionals and technologists. This is not geek speak, not an academic treatise. Venkat writes with great clarity and precision based on his real-life experience of delivering solutions through the RAGE AI platform. It is about the brave new world that narrows the gap between technology and business. Most of us have labored with technology projects that took too long, cost too much and delivered less than expected. Process-oriented software and Artificial Intelligence can create solutions that are flexible, smart and efficient. The book has practical advice from a thoughtful practitioner. Intelligent automation will be a competitive strength in the future. Will your company be ready?”

Victor J. Menezes, Retired Senior Vice Chairman, Citigroup



Copyright © 2017 by John Wiley & Sons, Inc. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada.

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Library of Congress Cataloging-in-Publication Data applied for.

ISBN: 9781118834626

This book is dedicated to my family

To my wife Pratima from whom I have learned so much, for her unwavering support for my efforts, and to our daughters, Meghana and Nandini, with love and gratitude

To my parents, Srinivasan Varadarajan and Sundara Srinivasan, for braving significant challenges in their lives and insulating me from them so I could pursue my dreams






1.1 Introduction

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

1.3 The Emergence of Packaged Applications

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

1.5 Enterprise Architecture: Current State and Implications

1.6 The Intelligent Enterprise of Tomorrow




2.1 Introduction

2.2 The Process-Oriented Enterprise

2.3 Role of Outsourcing in Creating Efficiency and Agility

2.4 Role of Technology in Efficiency and Agility

2.5 A New Technology Paradigm for Efficiency and Agility

2.6 Summary




3.1 Introduction

3.2 The Excitement around Big Data

3.3 Information Overload, Asymmetry, and Decision Making

3.4 Artificial Intelligence to the Rescue

3.5 Machine Learning Using Computational Statistics

3.6 Machine Learning with Natural Language

3.7 A Deep Learning Framework for Learning and Inference

3.8 Summary



4.1 The Road to an Intelligent Enterprise

4.2 Enterprise Architecture Evolution

4.3 Humans versus Machines

4.4 Summary

Appendix: A Five-Step Approach to an Intelligent Enterprise




5.1 Introduction

5.2 The Investment Advisory Market

5.3 What Do Investors Really Need and Want

5.4 Challenges with High-Touch Advisory Services

5.5 Active Advising – A Framework Based on Machine Intelligence

5.6 A Holistic View of the Client's Needs

5.7 Summary

Appendix: The RAGE Business Process Automation and Cognitive Intelligence Platform



6.1 Introduction

6.2 Information Asymmetry and Financial Markets

6.3 Machine Intelligence and Alpha

6.4 How Well Does It Work?

6.5 Summary

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



7.1 Introduction

7.2 The External Financial Audit

7.3 An Intelligent Audit Machine

7.4 Summary




List of Illustrations

Chapter 1

Figure 1.1

Information overload problem

Figure 1.2

Enterprise environment

Chapter 2

Figure 2.1

Functional enterprise architecture

Figure 2.2

Process-centric enterprise architecture

Figure 2.3

Design versus execution in enterprise architecture

Figure 2.4

Typical BPM role in E2E loan processing application

Figure 2.5

Conventional methodology

Figure 2.6

Generic Agile methodology

Figure 2.7

Business process orientation and technology

Figure 2.8

Anatomy of an abstract component

Figure 2.9

RAGE AI™ solution architecture

Figure 2.10

RAGE AI™ engines

Figure 2.11

Machine intelligence role in E2E loan processing application

Figure 2.12

High-level task to component map

Figure 2.13

RAGE methodology: RIM™

Figure 2.14

RAGE AI™ - Real time software development lifecycle

Figure 2.15

RAGE AI™ real time software development lifecycle steps

Chapter 3

Figure 3.1

Information overload and decision accuracy

Figure 3.2

Access-driven information asymmetry

Figure 3.3

Information asymmetry caused by access to different information at the same time

Figure 3.4

Information asymmetry caused by differences in interpretation of information

Figure 3.5

Information asymmetry caused by intentional misinformation

Figure 3.6

Dimensions of artificial intelligence

Figure 3.7

Taxonomy of AI solutions

Figure 3.8

Clustering outcome type

Figure 3.9

Classification outcome type

Figure 3.10

Financial statement extraction from a PDF document

Figure 3.11

Extraction from a legal contract

Figure 3.12

Interpretation outcome type

Figure 3.13

Map of AI problem types and commonly used solution methods

Figure 3.14

An illustrative CART

Figure 3.15

Neurons and synapses in the human brain

Figure 3.16

Structure of a single hidden-layer feed forward ANN

Figure 3.17

Geometric motivation for classification

Figure 3.18

Illustrative SVM hyperplane separation

Figure 3.19

The human brain and multiple sources of input

Figure 3.20

RAGE AI™: A deep learning framework for natural language understanding

Figure 3.21

RAGE AI™ deep learning architecture

Figure 3.22

Automated knowledge discoverer architecture

Figure 3.23

Breaking a document into sub-graphs

Chapter 4

Figure 4.1

Enterprise environment

Figure 4.2

Functional enterprise architecture

Figure 4.3

Process-centric enterprise architecture

Figure 4.4

Design versus execution in enterprise architecture

Figure 4.5

Technology evolution

Figure 4.6

Enterprise 1.0

Figure 4.7

Enterprise 2.0

Figure 4.8

Enterprise 3.0

Figure 4.9

Intelligent enterprise of tomorrow

Figure 4.10

Intelligent enterprise of tomorrow

Figure 4.11

Rate of displacement versus rate of new skilled jobs

Figure 4A.1

Simple approach to an intelligent enterprise architecture

Chapter 5

Figure 5.1

Active advising with intelligent agents

Figure 5.2

Household Balance Sheet

Figure 5.3

Goals assessment agent

Figure 5.4

Client risk tolerance

Figure 5.5

Rebalancing agent

Figure 5.6

Rebalancing agent results view

Figure 5.7

Goals monitoring agent

Figure 5.8

Tax-loss harvesting agent

Figure 5.9

Sensing agent heat map

Figure 5A.1

Business process automation (BPA) and cognitive intelligence (CI) platform

Figure 5A.2

Intelligent agent framework

Chapter 6

Figure 6.1

Information asymmetry

Figure 6.2

An intelligent system to detect alpha in financial markets

Figure 6.3

Sample oil and gas portfolio

Figure 6.4

Impact analysis for Norfolk Southern

Figure 6.5

RTI trading returns versus benchmarks

Chapter 7

Figure 7.1

A generic external audit process


Figure 7.2

Intelligent audit machine – Functional architecture

Figure 7.3

Intelligent, adaptive automation

Figure 7.4

Intelligent, adaptive automation

Figure 7.5

Fundamental analysis

Figure 7.6

Continuous risk signals

Figure 7.7

Audit standards actionable intelligence



Table of Contents







































































































































































































Two centuries ago, Adam Smith laid down architectural principles that govern how enterprises organize themselves and function. In this book, we present an entirely new way to think about how enterprises should organize and function in the digital age. We present the enterprise architecture for the future: an architecture that recognizes the power of the emerging technology environment, enables enterprises to respond rapidly to market needs and innovation, and anticipates such needs by sensing important developments in internal and external environments in real time.

Enterprises continually strive toward becoming efficient and competitive through various means. Prompted by the TQM and radical re-engineering movements of the 1980s and 1990s, many enterprises have attempted to embrace process orientation as the key to efficiency and competitive differentiation. However, most have had only limited success in becoming process efficient. This may be largely because in today's dynamic business environment, the static and unresponsive nature of most technology paradigms has stifled any significant progress. In recent years the flood of digital information, called big data, has compounded this challenge and opened yet another front for businesses to factor into their strategies.

Most enterprises are severely constrained by their inability to change their processes in response to market needs. Despite all the attention toward business process management and process orientation, businesses still struggle with time to market and flexibility issues with technology. Technology instead of enabling such changes has become a serious inhibitor. Changing business processes embedded in software applications is often a lengthy, arduous process replete with cost overruns, missed timelines, and failures. The rapid pace of technology obsolescence has continued to require specialized training and skills and has exacerbated this issue further.

To keep up with business demands, businesses have gravitated toward packaged applications at least for what they perceived to be non-core functions like resource planning and financial accounting. For most enterprises, it is too expensive and difficult to maintain a custom technology application environment. Initially it was widely believed that the new world business order implied standardization of business processes even beyond non-core functions. It was argued that firms would seek to standardize business processes for several reasons – to facilitate communications, enable smooth handoffs across process boundaries, and allow comparative analyses across similar processes. This was hypothesized to revolutionize how businesses organized themselves. But such thinking has resulted in enterprises being forced to operate within the limits of the prevalent technology paradigms.

The Internet phenomenon was still nascent in the late 1980s/early 1990s. Since the mid-1990s, the Internet has become pervasive in businesses and peoples' personal lives; the rate of new information flow has been and is staggering. The rapidly emerging Internet of Things promises to add a whole new dimension of information at an extraordinary scale. If we add the viral spread of social media to an overabundance of information, corporations face an enormous challenge and opportunity to intelligently harness the wealth of knowledge and insight contained in such information.

Yet, over the last decade, the gap between ”technology speak” and ”business speak” has narrowed considerably. The ability to create and maintain a technology application has got considerably easier. The age of highly flexible process-oriented software frameworks that enable a corporation to configure its business processes, is now available to enterprises. Simultaneously, a whole new class of technologies has emerged to help enterprises deal with the explosive growth in data, and developments in cognitive computing promise a range of capabilities that will enable machines to do much more than be keepers and facilitators of data.

The enterprise of tomorrow has the opportunity to be intelligent in addition to being efficient. It requires the ability to monitor and analyze internal and external threats and opportunities continuously, and to make adjustments in operational processes to counter such threats or leverage opportunities. In doing so, it is not sufficient to analyze the enormous amount of unstructured information that has become available. An intelligent enterprise will need to seamlessly integrate such analytical processes into its normal operational processes. These two worlds are not distinct and dichotomous; rather, they are part of the same continuum. Without integrating these two sets of processes, enterprises will not achieve the desired results. Remember, enterprises are far from having solved the challenge of rapidly adapting their operational processes to the dynamic business environment. Most firms are still struggling to get their myriad systems to talk to each other, data quality issues are still bogging them down, and the list goes on.

These developments portend an enormous change in how enterprises architect themselves and operate. The historical constraints of unresponsive technology paradigms will now be history. By being able to configure technology to suit their business process needs, enterprises will be able to move away from tightly packaged applications without the overhead of custom software maintenance. Coupled with the ability to potentially understand unstructured data in addition to structured data, enterprises have the opportunity to think entirely differently.

Another fact is that today's enterprise architecture is largely people-centric. People have been largely the business process execution glue in an enterprise. In many enterprises people function as the process orchestrators and especially in the knowledge-based industries, people often execute their tasks manually. The time has come for technology to be the process orchestrator in the enterprise, control business process execution, increasingly enabling repetitive tasks to be executed in an automated fashion. Humans will have the opportunity to focus on design and not repeated execution. Flexible software frameworks and the ability to understand the meaning of unstructured documents will provide enormous power to enterprises in designing an entirely new architecture for doing business. This is the central idea of this book.

This book is divided into three parts. Part I frames the challenge enterprises face in greater detail – the challenges of the digital age, the need to adapt to the increasingly dynamic business environment, the inflexibility of systems and the inability to change business processes as needed, the constraints of working within the tight boundaries of packaged applications, the disadvantages of customizing packaged applications thereby rendering their core advantages invalid, and the explosive growth in information and the overload and asymmetry it has created.

Part II outlines an architecture for the intelligent enterprise. How should enterprises architect themselves in the digital age? Has business technology matured enough to allow businesses to configure and re-configure their business processes at will? Are we at a point where businesses can un-commoditize business processes without the overhead of expensive custom software development and maintenance? And how can enterprises systematically harness intelligence from all this data?

First, Chapter 2 delves into efficiency and agility, with focus on the benefits and challenges of a process-oriented enterprise. All of us recognize that labor arbitrage driven outsourcing is clearly not the answer in the long term. The discussion takes you through the current state of business technology and the reasons for why even contemporary software development platforms and methods are not delivering the efficiency and agility enterprises need to be competitive. This may sound surprising, but agile methodologies will not deliver speed and flexibility that businesses need. No code model-driven software platforms with an extensive set of model-driven abstract components can address the efficiency and agility challenge. Instead, such a platform can enable near real time, flexible software development and cut typical software development lifecycles to a fraction of what they are otherwise. The chapter discussion walks the reader through a no-code, meta model-driven platform that makes near real-time software development a reality.

Chapter 3 addresses the intelligence dimension with a focus on big data and artificial intelligence. I have intentionally excluded a discussion of computer vision from the scope of this book because of space and time. The chapter presents a taxonomy of AI problems and outcomes to demystify it to the reader. An overview of popular AI solution methods follows. I have tried to balance the treatment between being too technical and yet provide the reader with enough detail to develop a good appreciation for the nature of these methods. By relating these methods back to the taxonomy, I hope the reader will develop an overall understanding of how and where AI is beneficial.

Ninety percent of the content growth on the Internet is unstructured text. Especially as it relates to the handling of natural language, the chapter addresses the important point that most of the current methods, platforms, and tools, including IBM Watson and Google, are based on computational statistics and do not attempt to understand the natural language text at all. The chapter presents the reader with a cognitive intelligence framework that attempts to describe natural language and provide contextually relevant results. Further, there is a trade-off to be made between methods that yield black box solutions and methods that provide traceable, contextually relevant solutions. The cognitive intelligence framework presented in the chapter is not a black box, and its results and reasoning are completely traceable.

Chapter 4 presents an architecture for an intelligence enterprise. The architecture integrates the no-code meta model-driven architectural paradigm for efficiency and agility from Chapter 2 and the traceable cognitive intelligence framework from Chapter 3. The resulting architecture will consist of intelligent machines that learn from humans and data. Fundamentally, I suggest that in the enterprise of tomorrow, the execution aspects of a business will be largely machine run whereby people will be directed by machines and the design aspect of a business will be machine informed as a result of the intelligence gathered by machines. I also review the implications of such an architecture on the current people-centric workplace. Specifically, we revisit the humans versus machines debate and potential impact of the intelligent enterprise on jobs.

Part III presents three real world case studies incorporating the ideas discussed in the previous chapters.

Chapter 5 presents a next-generation architecture for wealth management advisory firms. The wealth management industry is in the throes of a seismic shift with the massive millennial transition, recognition that the historical focus on diversification without explicitly considering investor needs is suboptimal, and the rise of robo-advisors challenging the hegemony of large wire houses. We describe a flexible intelligent framework comprising intelligent machines that can enable wealth advisory firms and advisors to transition to E4.0.

Chapter 6 presents an application to systematically harness real time intelligence to enable active asset managers generate alpha to outperform financial markets. Finding alpha consistently is the Holy Grail in the asset management world. Few sectors in the economy are affected as fundamentally as the investing world with the enormous increase in the availability and flow of information. The application described is a flexible end-to-end solution that includes natural language understanding to process huge amounts of information intelligently and identify possible inefficiencies. Active asset management will move to E4.0 with such an approach.

Chapter 7 explores the use of machine intelligence in the audit profession. This industry is ripe for a major disruption. The fiduciary audit and assurance process is largely manual today and has not changed much since my days as an auditor in the late 1970s. The solution, as presented in the chapter, is an intelligent architecture for the audit firm.

As I show in this book, today there is a fundamentally transformative opportunity to leverage technology like never before in architecting a digital transformation of any enterprise. The opportunity will soon become an imperative. It is my hope that the central ideas of this book will help the business or technology leader see the enormous possibilities for change. The real solutions and options that illustrate this thesis are presented through case studies that demonstrate how to realize these possibilities.


This book is about a big, broad topic and has been in the making for at least two decades. It is the reflection of a lot of learning from colleagues, customers, teachers and friends.

I got the computing bug in the late 1970s working at a large US multinational in Delhi, India. I used to hang around the freezing cold area of the office floor where a couple of IBM 1401s were housed along with all the card punching and reading machines! Later I learned that those machines were already dinosaurs here in the United States, but they were operated with awe back in India those days. I was not trained as a computer programmer but bribed my way into the computer center by helping several programmer friends with punching and running the cards through the readers. From those days to now Internet, tablets, and smart phones, I have witnessed an incredible rate of technology advance in my lifetime to date, and the pace of acceleration seems to be only gaining even more momentum!

Just as Warren Buffett famously talks of his ovarian lottery, I feel incredibly lucky and privileged to have had the ability to learn the way I did and for the breaks and opportunities that came along the way to shape that learning and my professional journey. There are so many that I owe a deep debt of gratitude to. Thanks to my dear friend, Dr. Sanjiv Chopra, I am reminded of Captain Charlie Plumb and his deeply incisive “who packed your parachute” parable as I think back to the times and people who have helped me get to where I have.

I would like to start by thanking my manager at the US multinational who took a chance with me in a significant role as Cash Manager, which got me initiated with my love for management and data science. I had the freedom to solve numerous operational challenges that I believe created in me a self-belief to innovate and solve problems however difficult they might seem.

My advisor at the University of Cincinnati, Professor Yong H. Kim, apart from being an accomplished academic, a patient and wise mentor, had the fortitude and courage to deal with an unconventional doctoral thesis combining finance and expert support systems. I learned a great deal at the University of Cincinnati from some incredibly brilliant teachers who taught me rigorous methods of scientific inquiry and problem solving, apart from teaching me subject matter expertise.

My six years at Northeastern University were very fruitful. I benefited greatly from an environment that was conducive to research and was fortunate to work with a group of like-minded colleagues who were all so passionate about their respective fields of research and so wonderfully collaborative. I would single out the late Professor Jonathan Welch, Finance Department Head at that time, Professor Paul Bolster, and the late Thomas Moore, my Associate Dean, for their encouragement and support.

The roots of my entrepreneurial journey were sown a fateful day in April 1985 when I returned a call from Norm Thomson, then a senior executive at Procter & Gamble. What ensued was a series of research projects that evolved into consulting assignments and eventually, I decided to turn an entrepreneur. I learned a lot from watching Norm and several other credit executives in other Fortune 500 firms when we would all get together to discuss credit-related research. I have a great deal of admiration for Norm and his practical, progressive, visionary approach to his work and life. In the same vein, Lamar Potts and his team in worldwide financial services at Apple provided me a global platform to implement my ideas. I owe Lamar a great deal having the belief in me to engage with me for four very productive years and for being a true friend to this day.

I have learned an unimaginable amount in my entrepreneurial efforts from so many people – colleagues, investors, and customers. There are too many to list here. One person stands a clear distance from all in this regard. Mark Nunnelly has been an extremely valuable mentor, incredibly supportive and a true friend. I have learned a tremendous amount from him both about business and life.

I owe a deep debt of gratitude to my senior team at RAGE which has believed in me for over 20 years through successive ventures and working with whom, I have been able to generate and implement so many of the ideas in this book. Aashish Mehta, Jim DeWaele, Monty Kothiwale, Nadeem Yunus, Rummana Alam, Srini Bharadwaj, you have been a bedrock of support for me and the ideas in this book. Even when it might not have made sense to you at that time, you went along enthusiastically trusting my vision. Thanks also to Joy Dasgupta and Vikram Mahidhar, both of whom have added immeasurably to the conversation surrounding this book in a very short period of time.

I am equally indebted to our wonderful team in India. While I have benefited from my interactions with all RAGE teams, I have to single out the RAGE AI Platform team – Vishaal, Nitin, Manasi, Amit J, and Atin for their passionate belief in our challenge of conventional wisdom. Vishaal and Nitin, in particular, have truly kept alive our pioneering quest to find an effective computational paradigm for natural language understanding.

This book has gained immensely from the numerous reviews of earlier drafts by Rummana Alam, Joy Dasgupta, and Vikram Mahidhar. I am most appreciative of Sanjiv Chopra's constant encouragement and reminders in our frequent meetings at Starbucks. Special thanks also to Rummana who kept nagging me to commit to writing the book and then constantly reminding me to finish it. Thanks also to Andraea DeWaele for reviewing the book for language consistency, flow, typos, and format consistency with the editorial style requirements at Wiley.

I am lucky to have such a cooperative publisher and editorial team at Wiley. Steve Quigley, Jon Gurstelle, and Allison McGinniss have been terrific to work with. They have been patient as I have kept delaying timelines amidst my compulsions running RAGE.

Above all, I am blessed with a wonderfully supportive family, my lovely wife Pratima, and our wonderful girls, Meghana and Nandini. They have borne the brunt of my constant preoccupation with intellectual and entrepreneurial pursuits with unconditional love and encouragement. I am truly thankful to them.

Over the last 28 years, I have learned from and contributed actively to the understanding and practice of knowledge-based technology and finance, first in an academic capacity and later in an entrepreneurial capacity. I have successfully created and commercialized a number of significant innovations starting with my first entrepreneurial venture, eCredit, and in subsequent ventures. My work over the previous 25+ years on knowledge process automation and more recently, tractable/traceable machine intelligence have fructified into a robust body of knowledge which I believe has great relevance in the context of the information and technology revolution that is upon us.

There are several reasons for me to write this book at this juncture of my life. First, I would like to lift the ongoing active conversation around big data and machine intelligence to a higher more strategic level by recognizing the rightful place for such intelligent technology in an enterprise architecture. Senior business executives reading the book should get a sense of how to leverage machine intelligence in their strategic and operational activities. Second, by describing real life solutions in a robust conceptual setting, I hope to afford practitioners an opportunity to extrapolate the solutions and ideas to their own situation. Third, I have seen many hype cycles come and go before the recent big data and machine intelligence hype cycle. I believe the book offers important insights that could minimize the disappointments that invariably follow a hype cycle. Firms should not think about big data in isolation. Firms can't lose sight of their existing operational issues. Firms should not blindly adopt computational statistics based machine learning without understanding the fit with the problems they are trying to solve. And finally, the book provides the opportunity for me to add to the body of knowledge in the field and hopefully enable new research and advances by others.

This book will be interesting to CEOs, CXOs, senior executives, data scientists, information technology professionals, consultants, and academics alike. I have attempted the difficult task of balancing the content so it does not get too technical and at the same time, include enough rigorous material to satisfy the more technically inclined. I hope you, the reader, find it worthwhile.



1.1 Introduction

It has been over twenty years since the first edition of the Champy and Hammer book Reengineering the Corporation