Augmented Analytics Enabling Analytics Transformation for Data-Informed Decisions (Tobias Zwingmann, Willi Weber)（Z-Library）

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Willi Weber & Tobias Zwingmann Augmented Analytics Enabling Analytics Transformation for Data-Informed Decisions

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DATA “Packed with technical depth and practical insights, this book clearly lays out the path to becoming a data- driven organization in the era of AI.” —Donald Farmer Principal, Treehive Strategy Augmented Analytics linkedin.com/company/oreilly-media youtube.com/oreillymedia Augmented Analytics isn’t just another book on data and analytics; it’s a holistic resource for reimagining the way your entire organization interacts with information to become insight-driven. Moving beyond traditional, limited ways of making sense of data, Augmented Analytics provides a dynamic, actionable strategy for improving your organization’s analytical capabilities. With this book, you can infuse your workflows with intelligent automation and modern artificial intelligence, empowering more team members to make better decisions. You’ll find more in these pages than just how to add a forecast to your dashboard; you’ll discover a complete approach to achieving analytical excellence in your organization. You’ll explore: • Key elements and building blocks of augmented analytics, including its benefits, potential challenges, and relevance in today’s business landscape • Best practices for preparing and implementing augmented analytics in your organization, including analytics roles, workflows, mindsets, tool sets, and skill sets • Best practices for data enablement, liberalization, trust, and accessibility • How to apply a use-case approach to drive business value and use augmented analytics as an enabler, with selected case studies This book provides a clear, actionable path to accelerate your journey to analytical excellence. Willi Weber is head of data analytics at HDI Global SE, a leading European commercial insurer. He transitioned from software developer to analytics pioneer, leading the development of probabilistic models and pricing software and architecting HDI Global’s analytics transformation into an insight-driven company. Tobias Zwingmann is a leading AI expert and managing partner of RAPYD.AI, a Germany-based AI advisory firm that specializes in helping B2B companies adopt AI and machine-learning solutions faster. He’s also the author of AI-Powered Business Intelligence (O’Reilly, 2022). US $59.99 CAN $74.99 ISBN: 978-1-098-15172-0

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Praise for Augmented Analytics An essential read for any organization embarking on analytics transformation. The detailed coverage of Augmented Frames alone is worth the price of the book. Packed with technical depth and practical insights, use cases and code samples, this book clearly lays out the path to becoming a data-driven organization in the era of AI. — Donald Farmer, principal, Treehive Strategy Augmented Analytics comes at the right time in mid-2024 to provide much needed clarity to both business decision makers as well as Data and AI managers and practitioners alike on how to reach the next level of analytical maturity. It is full of pragmatic advice and sound foundational knowledge on applying the concepts of Augmented Workflows and Augmented Frames. Readers equipped with this knowledge will be in a better position to connect all the “analytical” dots to design and execute digitization projects in the enterprise. —Karl Ivo Sokolov, managing partner for Data, Specific Group Augmented Analytics is your guide to transformational insights. Crafted with wisdom, this handbook offers practical guidance for navigating analytics transformation, whether you’re an analyst or a leader. Essential reading for insight-driven decision making. —Noro Chalise, data scientist, QSystems AI

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This fine book brilliantly maps out essential strategies for leveraging AI in corporate change. A must-read for leaders seeking practical insights. Many companies need to change and I hope the book will support many on their way! —Dr. Tristan Behrens, principal AI hands-on advisor, AI Guru Augmented analytics represents the pinnacle of an organization’s data-driven evolution—an aspiration every value-focused data scientist should embrace. This book outlines the path to get there with a primary focus on strategy and people. —Jonas Schröder, data scientist, OTTO A masterclass addressing a critical subject that is heavily discussed in recent times—namely, augmented analytics/AI. The future success of analytics/AI will determine how it leverages human intelligence with the support of machines to help minimize human and data bias to achieve the best results when working together to create a win-win position. This book helps organizations understand how to define and execute augmented analytics strategy. —Ram Kumar, chief data and analytics officer, International Health, Cigna Most professionals and organizations struggle to uncover actionable insights from vast amounts of data. Augmented Analytics—packed with insights, best practices, and practical advice—is valuable for every data and analytics professional aiming to leverage modern data and analytics capabilities to drive business value and build a culture of data-driven decision making. This book discusses critical data and analytics domains— including data sourcing, preparation, insight generation, insight consumption, ethics, and more—enabling users of all skill levels to benefit from data and analytics for improved operations, compliance, and decision making. In one line, Augmented Analytics should be in the bookshelf of every data and analytics professional who aspires to leverage data and analytics for improved business performance. — Prashanth Southekal, data analytics consultant, author, professor, and founder and managing principal of DBP Institute

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Willi Weber and Tobias Zwingmann Augmented Analytics Enabling Analytics Transformation for Data-Informed Decisions Boston Farnham Sebastopol TokyoBeijing

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978-1-098-15172-0 [LSI] Augmented Analytics by Willi Weber and Tobias Zwingmann Copyright © 2024 Willi Weber and Tobias Zwingmann. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com. Acquisitions Editor: Michelle Smith Development Editor: Sarah Grey Production Editor: Clare Laylock Copyeditor: Shannon Turlington Proofreader: Emily Wydeven Indexer: nSight, Inc. Interior Designer: David Futato Cover Designer: Karen Montgomery Illustrator: Kate Dullea June 2024: First Edition Revision History for the First Edition 2024-05-31: First Release See http://oreilly.com/catalog/errata.csp?isbn=9781098151720 for release details. The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Augmented Analytics, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc. The views expressed in this work are those of the authors and do not represent the publisher’s views. While the publisher and the authors have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the authors disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights.

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Table of Contents Foreword. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii 1. The Business Transformation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Why Businesses Are Transforming 1 Factor 1: The Speed of Change 2 Factor 2: The Convergence of Multiple Technologies 2 Factor 3: The Importance of Data 2 Factor 4: Changing Consumer Behavior and Customer Centricity 4 Industries Heavily Impacted by Digital Transformation 5 The Consequences for Your Business 7 There’s No Analytics Transformation Without Augmented Analytics 8 A Data-Driven Culture 9 The “People Problem” and the Limits of Upskilling 9 Conclusion 10 2. The Analytics Problem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Finding Your Analytics Purpose 12 Competition and Customer Expectations 12 Operational Efficiency 12 Availability and User Friendliness 12 Innovation 13 Regulatory Compliance 13 How to Start Your Analytics Journey 13 Industry Examples 14 Ecommerce 14 Healthcare 14 v

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Manufacturing 14 Financial Services 14 Government 15 Commercial Insurance 15 The Concept of Analytical Maturity 16 Determine Your Current—and Future—Data Maturity 20 Stage 1: Data Reactive 20 Stage 2: Data Active 23 Stage 3: Data Progressive 31 Stage 4: Data Fluent 36 Conclusion 40 3. Understanding Augmented Analytics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 Definition 41 The Five I’s of Augmented Analytics 43 Overcoming the Limitations of Traditional Analytics Approaches 44 Augmented Workflows 45 The Benefits of Augmented Analytics 46 AA Gives Nonexpert Users a Better Experience 48 Automated Integration Provides More Complete Insights 49 AA Gives Faster, More Efficient Insights 49 Standardization Reduces Human Errors and Bias for Better Insights 50 AA Tools Are Easier to Scale Up 51 AA Reaches Further Afield to Generate Unexpected Insights 51 Overcoming Bias 51 Key Enablers of Augmented Analytics 55 Automation and AI 56 Artificial Intelligence: The Five Archetypes 57 The Limitations of Augmented Analytics 64 The Challenges of Augmented Analytics 66 Conclusion 67 4. Preparing People and the Organization for Augmented Analytics. . . . . . . . . . . . . . . . . . 69 Tailoring Augmented Analytics for Different Organizational Roles 70 Analytics Leader 71 Analytics Translator 73 Analytics User 74 Analytics Professional 78 Analytics Transformation Manager 82 Summary of Key Roles 84 vi | Table of Contents

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The Center of Excellence 86 Creating a Center of Excellence 86 Approaches to Organizing a CoE 89 Driving Transformational Change with the Influence Model 94 Fostering Understanding and Conviction 95 Reinforcing with Formal Mechanisms 95 Developing Talent and Skills 96 Role Modeling 96 Cultivating a Data-Literate Culture 97 Cultivating Analytics Awareness 98 Storytelling with Data 99 Embracing Data-Driven Management 100 Leading in the Age of AI 100 The Enablement Program 101 Training Formats for Analytics Leaders 101 Training Formats for Analytics Translators 105 Data Literacy Training 107 Technical Training 107 Conclusion 108 5. Augmented Workflows. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Types of Workflow Augmentation 110 Fixed-Rule, High-Confidence Augmentation 110 Idea and Insight Enrichment 111 Conversational Augmentation 111 Contextual Augmentation 112 Collaborative Augmentation 112 The Analytics Use-Case Approach: Finding Workflows to Augment 112 Phase 1. Idea: The Initial Spark 114 Phase 2. Concept: Structuring the Idea 115 Phase 3. Proof of Concept: Testing the Waters 118 Phase 4. Prototyping: Shaping the Concept 121 Phase 5. Pilot: The Test Run 123 Phase 6. Product: Full Deployment 124 Making the Make-or-Buy Decision 124 Decision Scenarios 127 Overarching Success Factors 129 Balancing Automation and Integration 130 The Use-Case Library 133 Technical Requirements for Implementing AA 139 Table of Contents | vii

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Infrastructure Setup Challenges 141 IT System Integration Challenges 149 Governance Challenges 151 Conclusion 157 6. Augmented Frames. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 Business Objects and Frame Units 159 Understanding Frames 162 Key Features of Frames 164 Frame Types 165 Frame Engines 167 Frame Engine Types 169 Attribute Aggregation 170 Engine Interfaces 172 Result Objects 179 Implementation Challenges 186 Frame Agent 187 Dissolving Frames 188 Identifying Types 188 Translating Frame Units 188 Enriching Frames 188 Orchestrating Calls 189 Standardizing Results 189 Central Repository 189 Monitoring and Performance Analysis 189 User Access and Security 190 User Interface 190 Frame Dissolver 193 Frame Adapter 195 Dealing with Group Variables 196 Dealing with Bottom-up Business Object Structures 198 Dealing with Unconnected Business Objects 199 Frame Creator 200 Case Study: AP/TP Frame Engine 201 Infrastructure and Technology 207 An Iterative Approach to Introducing Augmented Frames 211 Iteration 1: Free Frames and Frame Engines 211 Iteration 2: A Frame Agent and Frame Adapter 212 Iteration 3: The Frame Dissolver, ID Frames, and Indexed Frames 213 Iteration 4: Static Frames 213 viii | Table of Contents

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Iteration 5: Dynamic Frames 214 Iteration 6: The Frame Creator 215 Iteration Wrap-up 215 Conclusion 216 7. Applied Examples. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 The Underwriting Process 219 Types of Augmented Workflows in Underwriting 220 The Workflows in Detail 221 Example 1: Location Workflow 224 Situation and Problem Statement 224 Solution Overview 224 Solution Breakdown 226 Example Summary 228 Example 2: Benchmarking Workflow 229 Situation and Problem Statement 229 Solution Overview 229 Solution Breakdown 231 Example Summary 234 Example 3: Proposal Workflow 235 Situation and Problem Statement 235 Solution Overview 236 Solution Breakdown 236 Example Summary 238 Example 4: Improved Forecasting in Agile Projects 238 Situation and Problem Statement 238 Solution Overview 239 Solution Breakdown 240 Example Summary 245 Example 5: Quick Sales Intelligence 246 Situation and Problem Statement 247 Solution Overview 248 Solution Breakdown 249 Example Summary 256 Conclusion 257 Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 Table of Contents | ix

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Foreword When it comes to artificial intelligence (AI) and analytics, the explosion is almost tangible. People are starting to dream about dashboards that show the brightest futures and algorithms that change the way we work. The possibilities are endless. The shift to analytics is forcing us to reinvent the way we think, the way we work, and the way we collaborate—with humans and with AI. Everyone wants to know how to take advantage of all these opportunities. How can we outperform our competitors? How can we make the most of what we already have? And how can we prepare for a future that we can’t even imagine now? Of course, tools will help: algorithms, generative AI, models, transformers, and more. But the basis for answering these questions is not tool-driven. To successfully manage this change, it is important to identify your organization’s specific needs, audiences, and potential and to develop a tailored implementation plan. This book will help you identify the potential to implement analytics in your own unique business environment—and find your own ways to bring it to life. It provides a solid framework, with plenty of room to customize. Most importantly, it will show you the limits of trainings and upskilling and offers you a good way of bringing analytics right where it brings value: augmenting the workflows of your target groups. In addition to infrastructure improvements, it’s worth looking at the people who are shaping the change in organizations around the world: the softer, more human side of analytics is critical to success. This includes empathy, understanding, enthusiasm, excitement—but also perhaps fear, uncertainty, or skepticism. It is therefore essential to integrate, support, involve, and train every role. More than ever, “it’s all about the people.” And it’s not only about the people being trained. People who love AI and data analytics bring their enthusiasm and innovations to work every day, and their passion and euphoria can spread quickly, inspiring colleagues and helping to overcome obstacles. xi

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As an analytics transformation manager, I am very excited to be a part of this story, together with Tobi and Willi. In my role at HDI Global, I was able to shape the analytics transformation through various initiatives and formats and had the chance to see its impact with my own eyes. Seeing the endless potential and, most of all, empowering so many people to take an active part in this change were (and are) great experiences. I am excited to bring our own transformation to the next level with augmented analytics as its enabler. Most of all, I’m excited about continuing to be with people with understanding, empathy, and passion as they go even further through this transformation. Readers, I wish you the best of luck in making the most of these infinite possibilities. With this great book as your guide during your own transformation, I am convinced that you will help your organization find the best way forward. — Christiane Busche Analytics Transformation at Data Analytics HDI Global January 2024 xii | Foreword

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Preface Becoming data-driven is an ambitious goal—yet, from our perspective, there is not a single company that hasn’t in some way embarked on this journey in recent years. They all have different goals and approaches, and basically a different understanding of what data-driven really means, but they have started. The questions everyone needs to ask are about why you’re doing this, what you plan to do, and how you’ll achieve this goal. Are you striving for a good, stable business intelligence (BI) ecosystem? Do you want to scale your analytics capabilities across the organization? Do you understand analytics and its related potentials as part of your business assets, which absolutely need to be aligned with your business strategies? Depending on your organization’s goals and ambitions, these questions— and, ultimately, your journey to analytical maturity—will vary. It’s important to also understand where you are on this journey: are you still at the beginning, halfway through, or almost to the end? Whatever’s on your agenda, your ambition, your strategy, and the journey to becoming a data-driven organization inevitably leads to a fundamental, often far-reaching transformation. This transfor‐ mation will change how you process data and deal with the resulting insights, what you use them for, how they influence business decisions, and above all, how these insights affect people and change their actions. We call this the analytics transforma‐ tion, whether or not it is part of an overarching digital-transformation initiative in your company. This is so specific, important, and extensive that it requires its own strategic roadmap and a comprehensive approach. Think about the last trip you took. We work through every journey in iterations, whether it’s a road trip through Europe, your personal career, or the transformation of your company. A journey needs milestones, waypoints, junctions where you decide to go left or right, and pauses where you recapitulate the journey so far, develop new strategies, and prepare for the next stage. Whatever kind of journey you’ve embarked on, you’ll always need the same basic things: the right equipment, resources, and environment; the right skills; and above all, the right mindset. xiii

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The same goes for your company’s analytics transformation. You’ll need wide-ranging skills to address your environment and tooling, drive the transformation, and bring your aspirations to life. You’ll need all relevant business stakeholders involved, and at least a few of them must be passionate about the journey. But most important, without the right mindset, there will be no successful transformation. One evening, around bedtime, Willi’s phone pinged him with a new article that caught his attention: “Ten Unsung Digital and AI Ideas Shaping Business” by Kate Smaje and Rodney Zemmel. The article ended up giving him a sleepless night. The ideas that Smaje and Zemmel discuss are shaping digital and analytics transforma‐ tions in the modern business landscape, even though they don’t necessarily dominate the headlines. These ideas address all the aspects we touch on in this book: busi‐ ness, culture, value, strategy, technology, AI, awareness, leading by example, process, competency, collaboration, operating model, executing initiatives, scaling solutions, rapidly adapting to changing market conditions, and the need to combine all of these to be successful. Augmented analytics is a significant part of addressing each and every one of these, if not always a direct solution. That was a sleepless night for Willi, who realized that the concepts and possibilities we describe in this book are farther reaching and more important for transformation than even we had realized. For the first time, it felt like all of our ideas had come together into a comprehensive whole. He read every single question raised in the article and answered them multiple times: once for each idea. Here are some of Smaje and Zemmel’s questions: • Are you using software to build products, services, or businesses that create a true competitive advantage for your business? • What specific initiatives on your roadmap directly support scaling? • Have you identified the specific roadblocks to achieving scale, and are you clear about how to deal with them? • Are you developing those hard-to-copy capabilities (processes, workflows, auto‐ mations) that power the products and services you need to build and improve? • Do you have a clear view of which emerging technologies could most enhance your competitive differentiation? • What standards and best practices do you have in place for building data prod‐ ucts across the organization, and are they easily accessible by relevant teams? • Have you identified the most important roles in your business that could benefit from a generative AI copilot? • How quickly are you able to conceive of, build, and launch a new product or service? xiv | Preface

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1 Sample questions from the article “Ten Unsung Digital and AI Ideas Shaping Business,” Kate Smaje and Rodney Zemmel, McKinsey Digital, January 9, 2024, https://oreil.ly/T_Udr. • How much value have your digital and AI initiatives generated in the past six months? • How well is your digital-twin platform integrated into your product, solution, or business development?1 It has become clearer and clearer that augmented analytics is what connects these ideas and makes them work together. AA allows businesses to literally be “rewired” (thus fulfilling the metaphor of the book on which the previous bulletpoints are based, Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI by Eric Lamarre, Kate Smaje, and Rodney Zemmel [Wiley]). AA is the enabler that brings business and analytics together. That’s why we’re showing a way forward that deals with more than just the hard, technical aspects of an analytics transformation. If you really want to transform your organization successfully, you need to take a much broader approach. It will not be enough to establish infrastructure, develop BI and analytical prod‐ ucts, and ensure good data governance. You also need to consider adaptation with dynamic business strategies, the changing organization, diverse people and processes, and finally, the cultural environment in which you want to operate. We cover all these topics because only their combination will lead you to a point where you can successfully introduce augmented analytics to achieve a higher level of analytics maturity and transform from a data-driven business into an insight-driven one. Are standard data-driven methods, such as reporting, data-science models, and self-service BI, really meeting organizations’ needs in a rapidly changing business environment? Given the scope of the changes they are intended to inform, their effectiveness is severely limited. This book challenges you to rethink the paradigms of analytics transformation and take them a step further—to a place where augmented analytics closes the gap between data and strategic insights like never before. For example, in commercial insurance, new topics like augmented underwriting and augmented portfolio steering are starting to dominate the insurance-industry headlines and will become standard over the next few years. Other industries and their workflows will follow and experience the seamless integration of insights. In fact, augmenting our human capabilities with technology has always been a key driver of innovation and progress. We learned to control fire and cook food, amplified our physical capabilities with the wheel, and developed the printing press, providing wider access to knowledge than ever before. We’ve been creating more capable versions of ourselves since the Ice Age. Preface | xv

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Today, our entire digital lifestyle is based on augmentation. We choose shows on streaming services recommended by algorithms. On the road, our navigation systems suggest the fastest routes, augmenting our knowledge of directions. Restaurant rat‐ ings help us find the best options, supplementing our preferences. And the next frontier is augmenting our own intelligence. This is particularly important for businesses. To explain why, let us share a little story. Back in late 2022, at a conference of high-profile European data leaders, one speaker projected a picture similar to Figure P-1 on the wall. It became the whole conference’s key talking point because literally every company in the room was facing the situation it depicts. Figure P-1. Data experts in the total workforce What’s happening here? In most businesses, there’s a group—usually a minority—of what we call data experts or data professionals. Depending on the company’s analyti‐ cal maturity, they usually account for between 10% and 20% of the total workforce. During the last few years, employers have spent a lot of energy on empowering this group: providing them with better tools, building them labs, and so on. Expectations for their output have been high, and some companies did find their “high-value” use cases. But this approach has reached its limit: transformation isn’t about empowering this minority anymore. Now it’s about empowering the other 80% of the organization and unlocking more data-driven use cases (not always high in individual value, but many in number). How can you achieve this? The goal here is not to turn every knowledge worker (shown on the right in Fig‐ ure P-1) into a data expert (on the left). It’s not about turning every accountant into a data analyst. The hard truth that most organizations need to swallow is that most accountants do not want to become data scientists. They want to stay accountants. Instead, it’s about turning those accountants into better accountants—and the same holds true for virtually every knowledge-worker role. This book shows you how to achieve that—and how to do so at scale. xvi | Preface

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Who Should Read This Book? This book covers many aspects, both methodological and technical, of the broad topic of analytics transformation. The methodological topics are particularly foun‐ dational for understanding analytics maturity while the technical topics help you increase that maturity. The book is especially valuable for experienced analysts and for people in strategic and managerial positions who are responsible for some facet of an analytical or digital transformation. Engineers and other roles concerned with implementing and integrating analytics solutions will, however, appreciate the technical concepts and frameworks we provide to help start enhancing workflows in your organization, complete with a minimum viable product (MVP). In summary, this book is for anyone who understands that, while you can separate analytics into its technological, methodological, and business components, you can make use of them effectively only when you have a comprehensive perspective. You will see in these pages many transformative concepts and ideas that have a strong strategic character. You will also see technical implementations in dedicated analytics programming languages, such as Python and R. But even if you are not an analytics professional with programming skills, this book is for you: we explain all concepts, including the technical implementations, in a way that business professionals can understand while providing a complement for people who would like to dive deeper technically. Learning Objectives By the end of this book, you will understand the following: • The importance of aligning technological advances with dynamic business strate‐ gies and organizational processes • The critical components of an analytics transformation and how they relate to overall business strategy • How augmented analytics bridges the gap between traditional analytics environ‐ ments and insight-driven decision making • The fundamental roles of the organization’s culture, skills, and mindsets as pre‐ requisites for successfully adopting augmented analytics • The relevance of analytical roles and a holistic use-case approach • The limitations of traditional approaches to analytics • How augmentation can move your organization forward and make workflows more efficient Preface | xvii

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• The challenges of implementing augmented analytics • How to introduce augmentation into the enterprise infrastructure • How combining analytics with software-engineering techniques can kick-start your first analytics-augmentation MVPs You’ll also be able to: • Assess your organization’s current analytical maturity and identify areas for improvement • Evaluate and adapt your analytics strategies in response to evolving business needs and market trends • Develop a strategic roadmap for analytics transformation tailored to your organi‐ zation’s unique needs and goals • Address the technological, organizational, and cultural challenges associated with analytics transformation • Develop methodological and technical concepts for your individual augmentations • Implement augmented analytics infrastructures and integrate them into your business processes Navigating This Book Now that you understand the intention of this book and its goals, let’s outline its structure in more detail and show you how it reflects the needs of readers from both the methodological and the technical worlds. The first section, which is the less technical part of this book, addresses the organiza‐ tional and transformational foundations of analytic transformation holistically and explains the concept of augmented analytics in detail. The second section of the book, which starts at Chapter 5, makes a seamless transition into the technical aspects of implementing augmented analytics and integrating it into your analytics ecosystem in an architecture-independent way. Let’s take a closer look at each chapter: • Chapter 1, “The Business Transformation”, makes a general case for the impor‐ tance of analytics in today’s business landscape. We look at how different indus‐ tries are increasingly moving to incorporate analytics as part of their corporate culture. xviii | Preface