“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 stepby-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 THE INTELLIGENT ENTERPRISE IN THE ERA OF BIG DATA VENKAT SRINIVASAN Copyright © 2017 by John Wiley & Sons, Inc All rights reserved Published by John Wiley & Sons, Inc., Hoboken, New Jersey Published simultaneously in Canada No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 750-4470, or on the web at www.copyright.com Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permission Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose No warranty may be created or extended by sales representatives or written sales materials The advice and strategies contained herein may not be suitable for your situation You should consult with a professional where appropriate Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages For general information on our other products and services or for technical support, please contact our Customer Care Department within the United States at (800) 762-2974, outside the United States at (317) 572-3993 or fax (317) 572-4002 Wiley also publishes its books in a variety of electronic formats Some content that appears in print may not be available in electronic formats For more information about Wiley products, visit our web site at www.wiley.com Library of Congress Cataloging-in-Publication Data applied for ISBN: 9781118834626 Printed in the United States of America 10 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 CONTENTS PREFACE xiii ACKNOWLEDGMENTS xix PART I CHALLENGES OF THE DIGITAL AGE THE CRISIS HAS NOT GONE AWAY: OPPORTUNITY BECKONS 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 / 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 vii viii CONTENTS PART II AN ARCHITECTURE FOR THE INTELLIGENT ENTERPRISE 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 INSIGHT AND INTELLIGENCE 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 51 ix CONTENTS 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 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 ACTIVE ADVISING WITH INTELLIGENT AGENTS 5.1 Introduction / 135 135 x CONTENTS 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 FINDING ALPHA IN MARKETS 153 6.1 6.2 6.3 6.4 Introduction / 153 Information Asymmetry and Financial Markets / 154 Machine Intelligence and Alpha / 157 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 WILL FINANCIAL AUDITORS BECOME EXTINCT? 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 / 176 171 CONTENTS xi 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 187 180 REAL WORLD CASE STUDIES procedures can also be provided to the IAM In a semi-supervised implementation of the IAM, such standards can be codified into an actionable form so that the IAM can test for compliance to standards Figure 7.4 illustrates in more detail the types of agents and automation that can be achieved within each area The solution can also automatically integrate with external systems that perform background checks We also expect to add an independence network to examine if a change in one executive’s or client situation would be consequential for other clients 7.3.2 Audit Planning Audit planning tasks consist of workflow and risk assessment Workflow tasks can be supported by functionality related to scheduling, calendaring, budgeting, and resource allocation There are several commercial products that provide this functionality However, risk assessment is an area of audit planning that the IAM can enhance significantly In this regard we differentiate risk assessment from detection of fraud The objective of the external audit does not, in the normal course, include the detection of fraud The IAM may discover fraud but that is typically an unintended result An automated journal entry agent would examine all journal entries and flag unusual or suspicious entries In the IAM, the audit team can implement analytical rules and logic using the extensive set of knowledge-based components in the RAGE platform, mainly rules, decision tree, model, and model network Such logic can be at the account item level or at the client or industry level Besides the ability to define rules and logic, an extensive set of fundamental metrics and risk assessment parameters can be automatically executed and assessed (Figure 7.5) In using RAGE AITM ’s natural language based extraction and normalization components, the IAM can extract and normalize fundamental data directly from source documents, such as corporate filings and annual reports Figure 7.5 Fundamental analysis WILL FINANCIAL AUDITORS BECOME EXTINCT? 181 Figure 7.6 Continuous risk signals For private sector companies, such data can be accessed from all the ledgers and sub-ledgers across divisions, subsidiaries, and normalized/consolidated as needed IAM can also assess entity risk continuously by integrating and analyzing external information such as news and social media posts Figure 7.6 illustrates such analysis of unstructured information from around the world and the interpretation of unstructured information from a solvency point of view This IAM automated sub-machine can generate a continuous signal so that risks can be incorporated at any time in the audit cycle IAM can be integrated with credit information sources in all countries These sources transmit alerts on the businesses they monitor on a daily basis IAM can dynamically produce audit checklists by account and also generate information requests for the client Depending on the client, the request can be either through the audit portal or through a printed document 7.3.3 Fieldwork Fieldwork consumes a huge amount of time in most audits The audit team spends considerable effort in substantives tests of assertions Fieldwork is also amenable to significant automation As illustrated in Figures 7.3 and 7.4, the IAM can include as many secondary machines as necessary for specific items, such as AR, AP, inventory, and intangibles Each of the sub-machines can handle the details of a specific account and feed the results to other submachines as needed 7.3.4 Existence Tests In financial accounts, the existence of assets and liabilities often involve third parties, such as debtors, creditors, banks, and custodians, and involve a 182 REAL WORLD CASE STUDIES confirmation of existence by the third party This is referred to as the “confirmations” process Most existence assertions can be automated through the IAM Typically, auditors verify a sample of the transactions in an account that leads to the closing balance of assets and liabilities From intelligently selecting the sample to generating the confirmation and processing the response from the third party, the IAM can automate the entire process In fact, the IAM can automatically process a lot more confirmations and not restrict itself to a sample as is done usually in the manual process Through its patented extraction component, the process of matching confirmed amounts and other attributes with the recorded amounts and attributes can be automated 7.3.5 Rights and Obligations Auditors also review contractual documents to understand the rights and obligations of the client as a contracting party to any contracts An important example is a software contract in the case of a software firm The audit team will be interested in understanding terms of the contract such as software licensing fees, implementation or setup fees, liabilities as indicated by acceptance language, and any extent of warranties All this is needed to determine the appropriateness of revenue recognition by the client The IAM solution is unique in its ability to automatically review contracts and reconcile the terms and conditions with other derived data and documents such as invoices and bills RAGE AITM ’s natural language understanding capabilities enable such automated analysis of contractual documents Its capabilities go far beyond mere identification and extraction of key terms and extend to a qualitative assessment of different clauses or semantic matching of attributes with external data 7.3.6 Substantive Analytical Procedures The IAM can further provide automated assistance in analytical procedures where audit teams examine ratios relative to past trends, other firms in the industry and/or industry as a whole Auditors can be alerted for unusual values 7.3.7 Closing Balance Tests The IAM can automatically create schedules and work papers at an account level identifying significant and unusual transactions in the process WILL FINANCIAL AUDITORS BECOME EXTINCT? 183 Figure 7.7 Audit standards actionable intelligence 7.3.8 Analyze and Issue Financials We envision the IAM automatically generating a draft of the financials and audit report with all relevant footnotes The IAM could also generate the management letter for internal control deficiencies The audit team would then edit these documents on the system and issue them electronically 7.3.9 Audit Standards In today’s largely manual world, audit standards are typically known to the auditors In IAM, the audit firm can codify standards and make them more actionable (Figure 7.7) For example, often standards are expressed in a series of questions the answers that help determine the treatment of a specific item Such standards can be codified into IAM whereby the IAM would not only identify/obtain the answers to such questions, but potentially arrive at a conclusion that is completely traceable Auditors could then trace the reasoning of the IAM to their satisfaction An example is the recent standard issued by the AICPA for revenue recognition in the case of Software as a Service (SaaS) contracts Such a process of codifying the firm’s implementation of standards would lift the burden on auditors’ having to scrutinize new and updated standards in order to operationalize them Once the standards are codified using a metadata structure in RAGE AITM , they are effectively institutionalized and can influence every audit that is affected by the new standards Such a process is also likely to be viewed very favorably by the regulatory bodies 7.3.10 Workflow/Configuration IAM can already intelligently control and manage workflow to optimize resource allocation on all sides through various stages of the audit process This functionality can address scheduling, calendaring, and resourcing type functions 184 REAL WORLD CASE STUDIES IAM is entirely configurable From adding and setting up clients to defining interpretation rules for different sectors, industries, and account types to rules for text generation, the audit firm can create highly reusable metadata for automating client audits in different industries New standards can be codified and mapped to the respective account set 7.4 SUMMARY There is consensus in academe and among practicing auditors that external and internal audits can benefit significantly with knowledge-based technologies In this chapter, we have delineated our conception of a comprehensive intelligent machine for external financial audits The intelligent audit machine that we have designed will dramatically change the way external audits are conducted The depth and breadth of a typical audit will expand improving the quality of audits Consistency will also improve across the board Audit firms and clients will see significant reduction in effort for an audit Audit firms will move to the Enterprise 4.0 state As discussed, the IAM can be extended to many other audit processes Besides use by internal auditors, regulators can configure IAMs for their purpose substantially easing the regulatory burden on the companies being regulated and, at the same time, improving the quality of their work REFERENCES Abdolmohammadi, M J (1999) A comprehensive taxonomy of audit task structure, professional rank and decision aids for behavioral research Behavioral Research in Accounting 11: 51–92 Alles, M A., and Datar, S (2004) Cooking the books: A management-control perspective on financial accounting standard setting and the Section 404 requirements of the Sarbanes–Oxley Act International Journal of Disclosure and Governance (2): 119–137 American Institute of Public Accountants (AICPA) (2002) Consideration of Fraud in Financial Statement Audit Statement of Auditing Standards No 99 New York: AICPA American Institute of Public Accountants (AICPA) (2006) Audit Documentation Statement of Auditing Standards No 103 New York: AICPA American Institute of Public Accountants (AICPA) (2006) Risk Assessment Standards Statement of Auditing Standards 104-111 New York\: AICPA WILL FINANCIAL AUDITORS BECOME EXTINCT? 185 Bamber, E M., and Ramsey, R J (2000) The effects of specialization in audit workpaper review on review of efficiency and reviewers’ confidence Auditing: A Journal of Practice and Theory 19 (Fall): 147–157 Banker, R D., Chang, H., and Kao, Y (2002) Impact of information technology on public accounting firm productivity Journal of Information Systems 16 (2): 209– 222 Bell, T B., Bedard, J C., Johnstone, K M., and Smith, E F (2002) KriskSM: A computerized decision aid for client acceptance and continuous risk assessment Auditing: A Journal of Practice and Theory 21 (September): 97–113 Elliott, R K., and Jacobson, P D (1987) Audit technology: A heritage and a promise Journal of Accountancy (May): 198–217 Fischer, M J (1996) “Realizing” the benefits of new technologies as a source of audit evidence: An interpretive field study Accounting, Organizations and Society 21 (February–April): 219–242 Hunton, J E., Wright, A M., and Wright, S (2004) Are financial auditors overconfident in their ability to assess risks associated with enterprise resource planning systems? Journal of Information Systems 18(2): 7–28 International Accounting Bulletin (2005) Interview – BDO Seidman: Grabbing new opportunities International Accounting Bulletin, London, September 8: Janvrin, D., Bierstaker, J., and Lowe, D J (2008) An examination of audit information technology use and perceived importance Accounting Horizons 22 (1): 1–21 Keenoy, C L (1958) The impact of automation on the field of accounting Accounting Review 33 (2): 230–236 Knechel, W R (1988) The effectiveness of statistical analytical review as a substantive accounting procedure: A simulation analysis Accounting Review 63 (January): 74–96 Leech, S A., and Dowling, C (2006) An investigation of decision aids in audit firms: Current practice and opportunities for future research Working paper University of Melbourne Manson, S., McCartney, S., and Sherer, M (1997) Audit Automation: The Use of Information Technology in the Planning, Controlling and Recording of Audit Work Edinburgh: Institute of Chartered Accountants of Scotland Manson, S., McCartney, S., Sherer, M., and Wallace, W A (1998) Audit Automation in the US and UK: A comparative study International Journal of Auditing 2: 233– 246 Messier, W F Jr., and Hansen, W A (1987) Expert systems in auditing: The state of the art Auditing: A Journal of Practice and Theory (Spring) 94–105 O’Donnell, E., and Schultz, J (2003) The influence of business-process-focused audit support software on analytical procedures judgements Auditing: A Journal of Practice and Theory 22 (September): 265–279 186 REAL WORLD CASE STUDIES Public Company Accounting Oversight Board (PCAOB) (2007) Auditing Standard No An Audit of Internal Control Over Financial Reporting That is Integrated with an Audit of Financial Statements RAGE Frameworks (2013) Real time credit risk assessment and monitoring RAGE White Paper, April RAGE Frameworks (2013) On information diffusion in financial markets – Evidence from news, blogs and social media RAGE White Paper, May Elkhoury, Marwan (2008) Credit Rating Agencies and Their Potential Impact on Developing Countries United Nations Conference on Trade and Development Discussion Paper 186, January New York: UNCTAD Sprinkle, G B., and Tubbs, R M (1998) The effects of audit risk and information importance on auditor memory during working paper review Accounting Review 73 (October): 475–502 Vera-Munoz, S C., Ho, J L., and Chow, C W (2006) Enhancing knowledge sharing in public accounting firms Accounting Horizons 20 (2): 133–155 Winters, B I (2004) Choosing the right tools for internal control reporting Journal of Accountancy (February): 34–41 INDEX Note: Page numbers followed by “f” refer to figures access-driven information asymmetry, 56–57, 57f active advising, 140–142 active managers, 153, 157 advisory services market, 135 aggregation agent, 144 AI, see artificial intelligence Akaike’s information criteria (AIC), 162 Akst, D., 126 Alpaydim, E., 66, 71 Altman, E I., 69 American Express, 26 analyst rating changes (ARC), 166 Anderson, R P., 69 ANNs, see artificial neural networks artificial intelligence (AI), 59–60 defined, 60 dimensions of, 60f machine learning, see machine learning; AI methods, 66–68, 67f problem types and methods knowledge acquisition and representation, 60 machine learning, 60 machine perception/computer vision, 61 natural language processing, 61 solution outcomes classification, 62–64, 63f clustering, 62, 63f extraction, 64–65, 65f interpretation, 65–66, 66f taxonomy of, 62f types, 61–66 artificial neural networks (ANNs), 71–74, 72f Ashvin, B., 137 aspirational investor, 137 audit automation, 171 planning tasks, 173–174, 180 auditors, 172 Aue, A., 81 The Intelligent Enterprise in the Era of Big Data, First Edition Venkat Srinivasan © 2017 John Wiley & Sons, Ltd Published 2017 by John Wiley & Sons, Ltd 187 188 INDEX Aulagnier, S., 69 Autor, D H., 126–127 “Bag-of-words” representation, of natural language, 78–79 Baker, L D., 79 balance tests, 183 Baldridge, J., 89 Banker, R D., 172 Baran, P., 69 Bartlett, P., 70 Barwise, J., 82 Basili, R., 79 Bayesian networks, 83 behavioral portfolio theory (BPT), 137 belief networks, see Bayesian networks Bell, J F., 70 Bengio, Y., 76 Bengston, E., 93 Berning, C K., 54 Bienenstock, E., 73 big data computers to analyze, ability of, 53 in computing evolution, 53 defined, 53 facts, 52 unstructured text to analyze, ability of, 53–54 variety, 53 velocity, 53 veracity, 53 volume, 53 Bishop, C M., 72–73 black box, 145 Blomert, L., 85 Bottou, L., 76 BPM, see business process management BPM software, 11, 30–32 BPO industry, 27 BPR, see business process re-engineering Brache, A P., 21 brain, human, 84–86, 84f Brav, A., 156 Breiman, L., 68, 70 Brooks, C., 153 Brynjolfsson, E., 126 business process definition of, 21–22 management, outsourcing, 127 business process automation (BPA), 150, 172 business process re-engineering (BPR), 4–5 case studies, 5–8 technology paradigms and, business technology application development technologies, 10–11 BPM software, 11 and business process re-engineering, challenges of time to market and flexibility in, 9–11 effective enterprise architecture and, 10 emergence of packaged applications, 11–12 enterprise applications, 10 Capability Maturity Model (CMM), 10, 115 Cardie, C., 93 Carenini, G., 81 Caropreso, M F., 79 CARTs, see classification and regression trees case management frameworks (CMF), 31 categorizer, 87 Chambers, N., 93 Chan, W S., 157 Chang, H., 172 Champy, J., 3–4, 11, 14, 21, 109 Chhabra, A., 137 Cho, E., 69 Chon, T.-S., 69 classification, AI solution outcomes, 62–64, 63f classification and regression trees (CARTs), 69–71 in applied sciences, 69 building, 70 classifier selection, 70–71, 71f CLE, see computational linguistics engine client–server architectures, 115 clustering, AI solution outcomes, 62, 63f CMF, see case management frameworks CMM, see Capability Maturity Model cognitive intelligence (CI) platform, 150 cognitive overload, 54 cognitive understanding engine (CUE), 89 INDEX Cohen, L., 85 Coleman, A., 56 Collier, N., 80 communication overload, 54 computational linguistics engine (CLE), 89, 92–95, 93f computer vision, 61 conceptual semantic network (CSE), 89–91 Corston-Oliver, S., 81 Crammer, K., 76 Cristianini, N., 79 CSE, see conceptual semantic network CUE, see cognitive understanding engine Das, S., 137 data model, 68 Dave, K., 80 Davenport, T H., 21, 29 De’ath, G., 69–70 decision trees, 69–71 deep learning architectures, AI, 76–77 deep learning framework and inference, 83–89 computational linguistics engine, 92–95, 93f conceptual semantic network, 89–91 impact analysis, 95–96 knowledge discoverer, 91–92, 92f Dehaene, S., 85 Delacoste, M., 69 dependent variable, in AI classification outcome, 64 Di Eugenio, B., 89 Dimopoulos, I., 69 Dorn, D., 126 Doursat, R., 73 Drake, J M., 69 duVerle, D A., 89 efficiency and agility, 19–45 new technology paradigm for, 35–44 RAGE abstract components, 39–40 RAGETM AI (Rage), 38–40 real time software development, 43–44 RIMTM , 40–43 overview, 19 189 process-oriented enterprise, 19–26 become process oriented, 23–24 business process, 21–22 design and execution, 25–26 selection of, 24–25 role of outsourcing in, 26–29 FTE-based outsourcing model, 28 “global arbitrage,” 26–27 Lean Digital approach, 28–29 offshoring destinations, 27 role of technology in, 29–35 Agile methodology, 33–35 BPM software, 30–32 current challenges with technology, 30 role of methodology, 32–33 efficient market hypothesis (EMH), 154 Efron, B., 165 Elith, J., 69 enterprise architecture, 14–15 design versus execution in, 25f environment of, 14f functional, 20f principle of, 14 process-centric, 21f enterprise resource planning (ERP) systems, 119 Eppler, M J., 54 external audits, 171 extraction, AI solution outcomes, 64–65, 65f Fabricius, K E., 69–70 Fama, E F., 154 Fellbaum, C., 83 Ferrier, S., 69 final prediction errors (FPE), 162 financial audit process, 173 Finkel, J., 93 firm-specific operating models, 158–159 first-order logic (FOL), 82 FOL, see first-order logic forecasts, 58 Freund, Y., 70 Friedman, J H., 70 Friedmann, M., 54 Froyen, D., 85 Frydman, H., 69 190 INDEX functional enterprise architecture, 110–111, 110f future advisor, 139 Gallupe, R B., 54 Gamon, M., 81 Gantz, J., 52 GE Capital International Services (GECIS), 26 Geman, S., 73 General Electric, Genpact, 26, 28–29 Ghazanfar, A A., 84 Gilbert, N., 93 Givon, T., 85 goals assessment agent, 143, 145, 147 Godbole, N., 81 Goebel, R., 60–61 Golden Content, 165 Gorden, L A., 55 Graham, C H., 69 Grise, M., 54 Guha, R., 83 Guisan, A., 69 Haas, M R., 54 Haffner, P., 76 Haka, S., 55 Hammer, M., 3–4, 11, 14, 21–22, 109 Hannan, K., 81 Hannan–Quinn information criterion (HQ), 162 Hansen, M T., 54 Hanson, G H., 126 Hastie, T., 70 Hatzivassiloglou, V., 80 Heaton, J B., 156 Hernault, H., 89 high net worth individuals (HNWI), 136 technology for, 140 high-touch advisory models, 149 high-touch advisory services, 137–138 Hijmans, R J., 69 Hogeweg, P., 69 holistic technology framework, 136 Hopfield, J J., 69 household balance sheet, 142–143 Hovy, E., 80 Hsu, C., 76 Hu, M., 81 human brain, 84–86, 84f humans versus machines, 126–129, 127f Hunt, R E., 54 impact analysis, 95–96 impact analyzer, 87 independent variable, in AI classification outcome, 64 industrial Revolution, 26 information asymmetry, 154–155 access-driven, 56, 57f access to different information at same time, 56–57, 57f differences in interpretation of information, 57–58, 58f intelligent system, 160 intentional misinformation, 58–59, 59f information fatigue syndrome, 54 information overload cognitive, 54 communication, 54 and decision accuracy, 55, 55f information fatigue syndrome, 54 knowledge, 54 sensory, 54 sequential position effect, 56 information overload problem, 13f intelligent agent framework, 150 intelligent audit machine (IAM), 176–177 intelligent enterprise design versus execution, 111–112 evolution, 113 availability of information, 123–126 flexible, near real time software development, 121–122 machine intelligence, 122–123 technology, 113–121, 114f five-step approach to, 130–131, 131f functional architecture, 110, 110f for future, 15 humans versus machines, 126–129, 127f road to, 109–113 internal audits, 172 interpretation, AI solution outcomes, 65–66, 66f inverse document frequency factor, 79 INDEX Ishizuka, M., 89 ITES industries, in India, 27 Jackson, D A., 69, 73 Jacobs, P J., 79 Jacoby, J., 54 Jurafsky, D., 93 Kahnemann, D., 137, 155 Kanalley, C., 128 Kao, D., 69 Kao, Y., 172 KD, see knowledge discoverer Keenoy, C L., 171 kernel machines, 74–76 Keynes, J M., 126 Kiagias, E., 79 Kim, O., 157 Kim, S M., 80 KN, see KnowledgeNet knowledge acquisition and representation, 60 of natural language, 82–83 knowledge discoverer (KD), 87, 91–92, 92f KnowledgeNet (KN), 87, 89 knowledge overload, 54 knowledge process outsourcing (KPO), 128 Kodak, 26 Kohlbacher, M., 22 Kohonen, T., 69 KPO, see knowledge process outsourcing Kudo, T., 80 Laird, J E., 84 Langley, P., 84 Lascarides, A., 89 Lauga, J., 69 Lawrence, S., 80 lead–lag relationship, 162–163 Lean Digital approach, 28–29 Lecun, Y., 76 Lee, H., 93 Lee, L., 80, 81 Lee, W., 70 Lek, S., 69 Lenat, D., 83 liability optimization agent, 147 Libowski, Z., 54 Lin, C., 76 191 Liu, B., 81 Lodhi, H., 79 logistic regression, 68 Lopes, L., 137 Low, R., 84 Luger, G., 60 Luhn, H P., 78 machine intelligence, 122–123 machine learning, AI, 60 using computational statistics, 68–69 artificial neural networks, 71–74, 72f decision trees, 69–71 deep learning architectures, 76–77 kernel machines, 74–76 with natural language, 78 “bag-of-words” representation, 78–79 knowledge acquisition and representation, 82–83 sentiment analysis, 80–82 tasks reinforcement learning, 68 semi-supervised learning, 67–68 supervised learning, 68 unsupervised learning, 67 machine perception, AI, 61 Mackworth, A., 60–61 Manning, C., 93 Mann, W C., 86, 89 Marcu, D., 89 Marcus, G., 77 market and flexibility, issues of time to, 9–11 Markovitz, H., 137 Matsumoto, Y., 80 Matwin, S., 79 McAfee, A., 126 McCallum, A K., 79 McCandliss, B D., 85 McCarthy, J., 60 McDonald, R., 81 McGuinness, D., 82 McKeown, K R., 80 McKinsey, 28 Meier, R L., 54 Mengis, J., 54 Miller, G A., 80 misclassification costs, in AI classification outcome, 64 192 INDEX Mitchell, T., 68 modern machine learning, 69 Moschitti, A., 79 Motorola, Mousavi, S Y., 84 Moxon, R., 27 Mullen, T., 80 natural language, 78 “bag-of-words” representation of, 78–79 deep learning framework for, 86–87, 86f knowledge acquisition and representation of, 82–83 sentiment analysis of, 80–82 natural language processing (NLP), 61 natural language understanding (NLU), 61 N-dimensional score, 161 neurons, 71–72, 72f Newman, R G., 54 Neylon, T., 81 Ng, R., 81 Nilsson, N., 60 NLP, see natural language processing NLU, see natural language understanding Noble, I R., 69 Norvig, P., 60–61 null hypothesis, 163 Olden, J D., 69, 73 Olshen, R A., 70 ontology web language (OWL), 82 Open Mind Common Sense project, 83 opinion mining, see sentiment analysis, of natural language ordinary least squares (OLS) estimation, 162 OWL, see ontology web language packaged applications, emergence of, 11–12 Pang, B., 80–81 parametric statistical methods, 68 Pauls, A., 81 Pazienza, M T., 79 Pearl, J., 83 Pennock, D M., 80 petabytes, 53 Peterson, A T., 69 Phillips, S J., 69 Poole, D., 60–61 Porter, M., 78 portfolio sensing agent, 147 Posner, M I., 85 post-earnings announcement drift (PEAD), 156 Prendinger, H., 89 process-centric organization, 111, 112f process-oriented enterprise, 19–26 become process oriented, 23–24 business process, 21–22 design and execution, 25–26 selection of, 24–25 Prusak, L., 55 RAGETM AI (Rage), 38–40 RAGE AITM , 86–87, 129 conceptual semantic network, 89–91 deep learning architecture, 87–88, 88f deep learning framework for natural language, 86f RAGE enterprise platform, 150 Raghunathan, K., 93 Randin, C., 69 random forests, 70 Rangarajan, S., 93 RDF, see resource description framework real time software development, 43–44 rebalancing agent, 146 Reengineering the Corporation, registered independent advisors (RIAs), 138 regulatory audits, 172 Reijers, H A., 31 reinforcement learning, 68 Reinsel, D., 52 Reitter, D., 89 resource description framework (RDF), 82 Rew, A G., 153 Reynar, J., 81 Riloff, E., 93 Ringger, E., 81 Ripley, B D., 69 risk signals, continuous, 181 Ritson, S., 153 robo-advisors, 136, 139 robotic process automation (RPA) software, 29 Rogers, S., 84 Roth, D., 93 INDEX Rousseau, F., 79 RTI-based trading, 167 RTI signals, 165 Rummler, G A., 21 Russell, S J., 60–61 Sagae, K., 89 Salski, A., 69 Saunders, C., 79 Sawhney, M., 27 Schapire, R E., 69 Scheid, J., 137 Schick, A G., 55 Schneider, S C., 55 Schroeder, C E., 84 Schwarz’s criteria (SC), 162 SDLC, see software development lifecycle Sebastiani, F., 79 The Second Machine Age, 126 Securities and Exchange Commission, 56 semantic network, 83 semantics, 82 semi-supervised learning, 67–68 sensing agent heat map, 149 sensory overload, 54 sentiment analysis, of natural language, 80–82 sequential position effect, information overload, 56 Shapira, R., 70 Shawe-Taylor, J., 79 SigFig Wealth Management, 139 Simnet, R., 54 Simpson, C W., 55 Singer, Y., 76 Singh, P., 83 Six Sigma, Skiena, S., 81 Smith, A., 14 software development lifecycle (SDLC), 32–33, 115 Soricut, R., 89 Sowa, J., 83 Sparck Jones, K., 79 Sparrow, P R., 54–55 Speller, D E., 54 Sperlbaum, C., 69 Srinivasaiah, M., 81 193 Srinivasan, V., 68, 70, 128 Standard and Poor’s Index Versus Active (SPIVA), 153 Statman, M., 137 Stock, 162 Stockwell, D R B., 69 Stone, C J., 70 “Stop” words, 79 Stoyanov, V., 93 Stubblefield, W., 60 Subba, R., 89 substantive analytical procedures, 175 supervised learning, 68 support vector machines (SVM), 74–76, 75f Surdeanu, M., 93 Sutton, C D., 70 SVM, see support vector machines Sweller, J., 84 synapses, 71–72, 72f syntax, 82 tax planning agent, 147 tax-loss harvesting agent, 148 term frequency, 78–79 Thompson, S A., 86, 89 Tibshirani, R., 70 total quality management (TQM), 3–4 TQM, see total quality management Trading Rules, 166 training data set, in AI classification outcome, 64 Turing, A., 68 Tversky, A., 137, 155 Tyagarajan, T., 29 unstructured information/data, 12–13 unstructured text, 53–54 unsupervised learning, 67 Vaithyanathan, S., 80–81 validation data set, in AI classification outcome, 64 Van Harmelen, F., 82 Vazirgiannis, M., 79 vector autoregression (VAR) model, 162 Vector Error Correction Model (VECM), 163–164 194 INDEX Vega, C., 156 Verrecchia, R., 157 Vieglais, D A., 69 visual percepts, 85 Vollmann, T E., 54 “Wealth Transfer” phenomenon, 138 Wells, M., 81 Wiebe, J., 80 WordNet, 80, 83 Wurman, R S., 54 Watkins, C., 79 Wealth advisors (WAs), 136 Zaharoff, L., 137 Zimmermann, N E., 69 ... 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... then using the same media to steer opinion or take other action And there is a rapidly growing population of data scientists, who are being given the task of taming big data by applying mathematics,... binders of TQM flows The Intelligent Enterprise in the Era of Big Data, First Edition Venkat Srinivasan © 2017 John Wiley & Sons, Ltd Published 2017 by John Wiley & Sons, Ltd CHALLENGES OF THE