Data as the Fourth Pillar An Executive Guide for Scaling AI (Sujay Dutta Siddharth Rajagopal) (Z-Library)

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“This book reveals how strategic data use fuels growth, unlocks value, and powers AI-driven innovation. It equips business leaders with practical frameworks and actionable insights to maximize data’s potential, accelerate decision-making, and stay ahead in rapidly evolving markets.” Liselotte Engstam, Professional Board Member and Strategic Advisor, Sweden “The future belongs to companies that see data not as an IT problem but as a strategic driver and the fourth pillar of the enterprise. Let this book be your guide to making that shift.” Binny Gill, CEO of Kognitos, Inc., USA “This book underscores the critical role of data in scaling enterprise-grade AI systems. It provides foundational concepts, practical frameworks, and real-world examples. With actionable insights and a maturity journey for leveraging data to enhance business outcomes, this book is a compelling read for anyone wanting to stay ahead in the AI-driven world.” Sandeep Kishore, Founder and CEO of Agivant Technologies, USA “The emphasis on the CEO and Board’s critical role in driving this transformation is particularly insightful. This book is a must-read for any leader seeking to unlock the full potential of data and propel their organization towards a future of sustainable growth and competitive advantage.” Mukundan Ramakrishnan, Managing Director of Tata Chemicals Limited, India “Data is the First Pillar, for me.” Ramana Kumar, ex-CEO of Magnati, UAE “This book introduces the data intelligence layer as essential for optimizing business operations and fostering innovation. It also offers compelling insights and practical strategies for business leaders to boost efficiency and sustain a competitive edge by leveraging data as a strategic asset.” Rüdiger Eck, Head of Data and Analytics Factory for Production & Logistics at Audi AG, Germany “This book distills the insights, frameworks, and strategies needed for every enterprise leader to take data seriously and unlock its full potential. Enterprises that capitalize on their data as a strategic asset and mature their data pillar will be able to create competitive advantages through initiatives like leveraging AI at scale.” Sandeep Kalra, CEO, Member of the Board, Persistent Systems, USA

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“This book is more than just an insightful read–it is a blueprint for action. The time to act is now. AI is no longer an experiment but an enterprise-wide imperative. Enterprises that fail to embed data as a core pillar will struggle to compete in the AI-first world.” Rainer Deutschmann, Senior Executive in Telecom and Technology Industries. Board Director, Advisor, and Investor, Malaysia “I would strongly recommend this book to any executive because it shows the benefits of investing in data management, data governance, data engineering, and common semantics within their business, in helping to achieve their business vision. The book explains why data is so important, lays out a systematic approach to building reusable data products, and provides a maturity model for your journey.” Mike Ferguson, CEO of Intelligent Business, UK “With the concept of the data pillar as a strategic component of the operating model, data is treated as an independent economic asset that generates continuous added value for companies. The authors provide clear correlations and framework conditions to enable a transformation of companies and mindsets in daily business.” Thomas Hußlein, CEO and Founder of OptWare, Germany

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Data as the Fourth Pillar Data as the Fourth Pillar reasons that data should be considered the fourth pillar of every enterprise, alongside people, processes, and technology. Aimed at Boards, CEOs, and CxOs, this book provides a compelling case for why and how they should treat data as a strategic asset. It presents a comprehensive, success-by- design approach for enterprises, guiding them through a maturity framework to accelerate their data-centric journey. This book addresses the “why,” the “what,” and the “how” of achieving this goal in measurable terms. It introduces key performance indicators (KPIs) such as total addressable value (TAV) and expected addressable value (EAV) through data to help measure the impact provided by the data pillar. This book also explores the symbiotic relationship between artificial intelligence (AI) and data, illustrating how both enable and benefit from each other. A case study by Rüdiger Eck from Audi AG provides practical insights into the concepts and frameworks discussed. This book is an essential resource for business executives in both small to medium businesses (SMBs) and large enterprises, helping them navigate a highly complex and hypercompetitive business landscape while accelerating business value for their stakeholder communities.

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Data as the Fourth Pillar An Executive Guide for Scaling AI Sujay Dutta and Siddharth Rajagopal

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Designed cover image: Sujay Dutta and Siddharth Rajagopal First edition published 2026 by CRC Press 2385 NW Executive Center Drive, Suite 320, Boca Raton FL 33431 and by CRC Press 4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN CRC Press is an imprint of Taylor & Francis Group, LLC © 2026 Sujay Dutta and Siddharth Rajagopal Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, access www.copyright.com or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978–750–8400. For works that are not available on CCC please contact mpkbookspermissions@ tandf.co.uk Trademark notice : Product or corporate names may be trademarks or registered trademarks and are used only for identification and explanation without intent to infringe. ISBN: 978-1-032-84460-2 (hbk) ISBN: 978-1-032-83599-0 (pbk) ISBN: 978-1-003-51277-6 (ebk) DOI: 10.1201/9781003512776 Typeset in Minion by Apex CoVantage, LLC

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To our families, friends, and supporters.

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ix Contents Foreword, xv By Rainer Deutschmann Foreword, xix By Sandeep Kalra Acknowledgments, xxi About the Authors, xxiii About the Case Study Author, xxv Introduction 1 0.1 KEY PERSONAS AND PURPOSES FOR READING THIS BOOK 3 0.1.1 This Book’s Website 4 0.2 INTRODUCTION TO THE CASE STUDY BY RÜDIGER ECK 4 Chapter 1 ◾ Why Establish Data as the Fourth Pillar for Scaling AI? 6 1.1 SCALING THE USE OF AI FOR ENHANCING ENTERPRISE VALUE 6 1.1.1 Meaning of Leveraging AI at Scale 7 1.2 DATA IS CRITICAL FOR SCALING AI 10 1.2.1 Defining Data Intensity 10 1.2.2 Data Intensity Required on the Basis of Enterprises’ AI Journey 14

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x ◾ Contents 1.2.3 Data Intensity Required for Data Monetization 22 1.2.4 Challenges to Meet the Data Intensity Requirements 23 1.2.5 For Enterprises to Alleviate Their Challenges – Treat Data Equally Important as People, Processes, and Technologies 27 1.3 CASE STUDY: WHY AUDI HAS DECIDED TO MAKE DATA A NEW PILLAR OF ITS OPERATING MODEL 28 Chapter 2 ◾ Data as the Fourth Pillar: What Does It Mean, and What Does It Include? 41 2.1 PRINCIPLES FOR THE DATA PILLAR 43 2.2 THE CHIEF DATA OFFICER 45 2.2.1 Business Impact Delivered by the CDO 53 2.2.2 Positioning of the CDO in the Organization Structure 53 2.2.3 Selecting the Right Person for the CDO Role 56 2.3 DATA CAPABILITIES 56 2.3.1 Hypothetical Example – A Retail Enterprise Undergoing a D2C Business Transformation 57 2.3.2 Level 2 and 3 Data Capabilities 60 2.4 DATA STRATEGY 61 2.5 DATA ARCHITECTURE 62 2.6 SEMANTIC DATA MANAGEMENT 64 2.6.1 Knowledge Graph Management 64 2.6.2 Metadata Management 65 2.6.3 Data Lineage Management 66 2.7 DATA ADOPTION 68 2.8 DATA GOVERNANCE 69 2.8.1 Data Quality and Observability 69 2.8.2 Data Access Management 70 2.8.3 Data Risk Management 72 2.9 DATA ENGINEERING 73 2.10 DATA STORAGE AND COMPUTING 74

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Contents    ◾   xi 2.11 DATA VALUE MANAGEMENT 74 2.12 OTHER DATA CAPABILITIES 75 2.13 ROADMAP FOR DATA CAPABILITIES 76 2.14 WHAT IS A DATA OPERATING MODEL? 77 2.14.1 Layers of the DOM 85 2.15 CASE STUDY: WHAT DOES DATA AS A NEW PILLAR MEAN FOR AUDI? 87 Chapter 3 ◾ Operationalizing Data as the Fourth Pillar: The Data Operating Model 97 3.1 SUPPORTING LAYER 98 3.1.1 People Capabilities Enabling the Supporting Layer 100 3.1.2 Process Capabilities Enabling the Supporting Layer 104 3.1.3 Technology Capabilities Enabling the Supporting Layer 111 3.1.4 KPIs for the Supporting Layer 113 3.2 DATA PRODUCTS LAYER 114 3.2.1 Data Contract 115 3.2.2 People Capabilities Enabling the Data Products Layer 119 3.2.3 Process Capabilities Enabling the Data Products Layer 123 3.2.4 Technology Capabilities Enabling the Data Products Layer 132 3.2.5 KPIs for the Data Products Layer 134 3.3 DATA INTELLIGENCE LAYER 135 3.3.1 People Capabilities Enabling the Data Intelligence Layer 136 3.3.2 Process Capabilities Enabling the Data Intelligence Layer 140 3.3.3 Technology Capabilities Enabling the Data Intelligence Layer 143 3.3.4 KPIs for the Data Intelligence Layer 145

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xii ◾ Contents 3.4 RAW DATA LAYER 146 3.4.1 People Capabilities Enabling the Raw Data Layer 147 3.4.2 Process Pillar Enabling the Raw Data Layer 149 3.4.3 Technology Capabilities Enabling the Raw Data Layer 152 3.4.4 KPIs for the Raw Data Layer 153 3.5 CASE STUDY: HOW DOES AUDI MAKE DATA A NEW PILLAR? 154 Chapter 4 ◾ Maturity Journey for the Data Pillar 160 4.1 DETERMINING ENTERPRISE POSITIONING IN THE MATURITY FRAMEWORK 166 4.1.1 Positioning on the Y-axis: Demand for Data 166 4.1.2 Positioning on the X-axis: Supply of Data 171 4.1.3 Positioning in the Maturity Framework 172 4.2 SCENARIOS IN THE FUNDAMENTAL STAGE 173 4.3 JOURNEY FROM THE FUNDAMENTAL STAGE TO THE SCALED STAGE 176 4.4 MATURING FROM THE SCALED STAGE TO THE AUTOMATED STAGE 181 4.5 CASE STUDY: MATURITY JOURNEY FOR THE DATA PILLAR AT AUDI 185 Chapter 5 ◾ Visualizing the Future: An Autonomous Enterprise, with Data as the Lifeblood 190 5.1 THE STATE OF ENTERPRISES IN 2035 – AUTONOMOUS ENTERPRISES 190 5.2 DATA AS A SOURCE OF DIFFERENTIATION FOR AUTONOMOUS ENTERPRISES 191 5.3 THE COMPELLING REASON FOR ENTERPRISES TO ACT 192 5.4 WHAT SHOULD ENTERPRISES DO NOW TO MAKE IT TO 2035? 192 5.5 BOARDS AND CEOS MUST BE THE DATA CHAMPIONS 194

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Contents    ◾   xiii 5.6 CXOS CREATE THE “FLYWHEEL” EFFECT – ENABLING THE DATA PILLAR AND BENEFITTING FROM IT 195 5.7 CDO’S SUCCESS BECOMES THE LEADING INDICATOR OF THE ENTERPRISE’S SUCCESS 196 5.8 THE CALL TO ACTION FOR BUSINESS LEADERS: FUTURE-PROOF YOUR ENTERPRISE! 197 INDEX, 199

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xv Foreword By Rainer Deutschmann We are living in an AI-first world – a world where access to cutting- edge technology is abundant, yet enterprises face a critical chal- lenge: How to actually employ AI to create sustainable enterprise value and competitive advantage. The most successful companies not only lever- age AI for top-line growth and operational efficiency but also establish a self-reinforcing flywheel effect whereby investments into AI capabilities generate greater cash flow, the fuel for further AI investments. Over the past two decades, I have had the privilege of leading digital and AI-driven transformations across major telecommunications and technology enterprises worldwide. Whether in Europe and the Nordics, Asia, or in globally disruptive ventures, the common thread has always been unlocking value through data-driven innovation. However, one lesson remains clear: Without a solid foundation and a structured approach, initiatives struggle to scale, business impact remains limited, and competitive advantage fades. While AI transforms industries, it is not the technology alone that drives success – it is how enterprises embed AI into their business models and organizational processes and build the necessary organizational capabilities to leverage AI at scale. Traditionally, enterprises have structured their operating models around three pillars: People, processes, and technology. These pillars have served to drive productivity, efficiency, and innovation efforts across industries. However, this book – launched at a pivotal time – makes the compelling case that we are now at an inflection point at which enterprises must establish data as the fourth pillar – complementing people, processes, and technology – to successfully scale AI and unlock its full potential. Four years ago, my team and I  were faced with declining customer satisfaction and increasing cost to serve, due to broken processes and

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xvi ◾ Foreword fragmented platforms in IT and customer operations. We radically simpli- fied and digitized the end-to-end operations process, consolidated all tick- eting systems, connected customer and IT operations, and harmonized data. As a result, system outages and call center volumes reduced sig- nificantly, and net promoter scores increased. However, this was only the beginning. On this digital and data foundation, we were able to establish a large language model (LLM)-based self-reinforcing flywheel. The data on how incidents were resolved was used to train an AI model, such that for further cases, the AI agent, rather than the human agent, could take over, thus continuously improving operations. This case is a good example of how data complements people, processes, and technology to unlock the full potential of AI for better customer expe- rience and lower cost. In this book, you will find similar real-world case studies that illustrate how enterprises can embed data-driven intelligence into business operations. The frameworks introduced offer structured guidance for execution. Concretely, the authors introduce several key frameworks to help enter- prises establish data as the fourth pillar and bridge the gap between AI aspiration and AI execution: • To truly leverage AI, enterprises must ensure that the data is fit for purpose. The QCS framework (quality, compliance, and speed) helps leaders assess the data intensity required for AI-driven transformation – ensuring that the data is accurate, regulatory- compliant, and available at the speed of business needs. Without this, AI initiatives risk failure due to poor data quality, compliance risks, or slow decision-making. • Furthermore, a successful AI-driven enterprise is not built on iso- lated data projects but on a structured and scalable foundation. The data operating model (DOM) serves as this blueprint, ensuring that people, processes, technology, and data are aligned to deliver trusted, governed, and accessible data across the enterprise. Without a well- architected DOM, enterprises risk data silos, operational inefficien- cies, and AI initiatives that fail beyond experimentation. • Achieving AI at scale is a journey, not a one-time project. The book’s maturity model for the data pillar provides a structured roadmap to help enterprises evolve from basic data management to full AI automation. It defines three key stages – fundamental, scaled, and automated – each representing a critical milestone in AI readiness.

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Foreword    ◾   xvii Many enterprises struggle because they try to implement advanced AI without first building a solid data foundation. By following this model, organizations can assess where they stand today, pri- oritize key enablers, and systematically progress toward AI-driven decision-making. Besides the practical frameworks, what makes this book particularly valu- able is the link to business value creation. It introduces the total address- able value (TAV) versus expected addressable value (EAV) model, guiding strategic investments in AI and data to maximize business impact. I have seen data and AI initiatives succeed or fail depending on the clarity of leadership accountability. This book, therefore, rightfully details key roles, such as the chief operating officer (COO) and chief data officer (CDO), in the context of the organization’s maturity. As enterprises prog- ress on their data maturity journey, data is no longer a byproduct but a strategic asset, becoming deeply embedded across business functions. In such a mature state, every leader will have assumed data accountability, making data-driven decision-making a fundamental enterprise capability rather than a separate function. This book is essential reading for business leaders, COOs, CDOs, CIOs, and executives responsible for driving digital transformation and AI adoption. As enterprises navigate an AI-first world, leaders must go beyond technol- ogy implementation and focus on establishing data as a strategic asset – one that fuels AI at scale, enhances operational efficiency, and drives competitive advantage. Whether you are a COO optimizing business processes, a CDO shaping data strategy, or a CEO looking to future-proof your enterprise, this book provides a practical roadmap to break down data silos, embed AI into core operations, and turn data into a sustained source of enterprise value. This book is more than just an insightful read – it is a blueprint for action. The time to act is now. AI is no longer an experiment but an enterprise-wide imperative. Enterprises that fail to embed data as a core pillar will struggle to compete in the AI-first world. I highly recommend this book to any leader looking to navigate the complexities of AI adoption, digital transformation, and data-driven value creation. Rainer Deutschmann Senior Executive in Telecom and Technology Industries Board Director, Advisor, and Investor February 2025, Malaysia

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xix Foreword By Sandeep Kalra We stand at a pivotal moment in history – a time when the conflu- ence of data and artificial intelligence (AI) is reshaping indus- tries, redefining business models, and reimagining how enterprises create value. In Data as The Fourth Pillar – An Executive Guide for Scaling AI, the authors explore a fundamental shift: The recognition of data as the fourth pillar of the enterprise operating model, alongside people, processes, and technology. For years, enterprises have focused on people, processes, and tech- nologies as the core pillars of their operating models. However, my own journey at Persistent Systems, as well as the experiences of our clients, has demonstrated a significant evolution. The explosion of AI, fueled by data, demands a fundamental shift. Data must be elevated to the same level as people, processes, and technologies, a change that is now vital in a hypercompetitive landscape. Data must not be treated as a “by-product” of doing business; it is a key ingredient for innovation, powering AI and enabling strategic decisions. Data drives innovation, differentiation, and growth for enterprises. It is the fuel that powers AI-driven business outcomes. At Persistent Systems, our journey – from being a $500 million organization to a $1.4 billion global leader in 5 years – proves that data, when elevated to a strategic pillar, becomes the lifeblood of innovation, efficiency, and growth. Take our AI-driven contract management system: It empowers sales teams with real-time insights into pricing, deal terms, and risks. Similarly, our shift from “systems of record” to “systems of insight” has democratized deci- sion-making, enabling AI-powered forecasting, budgeting, and planning. But scaling AI is not without challenges. Siloed data, fragmented gov- ernance, and cultural inertia hinder progress. This book offers a blueprint