Data for AI

Artificial Intelligence (AI) is only as powerful as the data that fuels it, and this book is your comprehensive guide to understanding the critical data infrastructure that makes AI work. AI has become a transformative force across industries, from healthcare and finance to retail and manufacturing. However, while much attention is given to AI models and algorithms, the data that feeds these systems is often overlooked. This book shifts the focus to the foundational elements of AI-data architecture, storage, processing, and governance-so that organizations can effectively harness the potential of AI. Without high-quality, well-structured data, even the most advanced AI models cannot deliver reliable results. In the first part of this book, we explore the evolution of AI and its reliance on data. We begin with an overview of AI's history, including data mining's role in early machine learning. From there, we examine the challenges of managing machine learning data, the infrastructure required for deep learning, and the unique data needs of large language models such as ChatGPT. The book also delves into generative AI, which requires vast datasets and specialized storage and processing solutions. The second part of this book moves from theory to practice, detailing how organizations can operationalize data for AI. This includes modern storage solutions, master data management (MDM), data quality, governance, and the ethical considerations surrounding AI-driven decision-making. We explore real-time data pipelines, how data moves within AI-powered organizations, and the technical and business processes required to make AI truly operational. Additionally, we discuss common pitfalls and provide insights into the future of AI data infrastructure. Whether you are a data professional, AI practitioner, or business leader, this book provides the knowledge necessary to navigate the complex world of AI data. By mastering data infrastructure, you will be better equipped to build, deploy, and scale AI systems that drive meaningful impact. We are in the midst of the rise and evolution of Generative AI. Foundational AI continues to deliver reliable and valuable insights and Causal AI is on the horizon. What do these three pillars of AI have in common and require? Data, and an immense amount of it, and not just a one-time infusion, but an ongoing flood of data to keep all these models working at the optimal level. I am so pleased that Scott Burk and Kinshuk Dutta took on the challenge of writing a book that dives into how to obtain, clean, integrate, and use data in this rapidly evolving landscape of Al. Having worked with Scott, I know first-hand his abilities and capabilities in working with all types of data. If you are interested in learning world-class best practices of preparing data for use in your AI environment, this book will be an invaluable resource for your journey into the multifaceted world of data for AI. John Thompson Author, Innovator, Adjunct Professor, University of Michigan, School of Information

Artificial Intelligence (AI) is only as powerful as the data that fuels it, and this book is your comprehensive guide to understanding the critical data infrastructure that makes AI work. AI has become a transformative force across industries, from healthcare and finance to retail and manufacturing. However, while much attention is given to AI models and algorithms, the data that feeds these systems is often overlooked. This book shifts the focus to the foundational elements of AI-data architecture, storage, processing, and governance-so that organizations can effectively harness the potential of AI. Without high-quality, well-structured data, even the most advanced AI models cannot deliver reliable results. In the first part of this book, we explore the evolution of AI and its reliance on data. We begin with an overview of AI's history, including data mining's role in early machine learning. From there, we examine the challenges of managing machine learning data, the infrastructure required for deep learning, and the unique data needs of large language models such as ChatGPT. The book also delves into generative AI, which requires vast datasets and specialized storage and processing solutions. The second part of this book moves from theory to practice, detailing how organizations can operationalize data for AI. This includes modern storage solutions, master data management (MDM), data quality, governance, and the ethical considerations surrounding AI-driven decision-making. We explore real-time data pipelines, how data moves within AI-powered organizations, and the technical and business processes required to make AI truly operational. Additionally, we discuss common pitfalls and provide insights into the future of AI data infrastructure. Whether you are a data professional, AI practitioner, or business leader, this book provides the knowledge necessary to navigate the complex world of AI data. By mastering data infrastructure, you will be better equipped to build, deploy, and scale AI systems that drive meaningful impact. We are in the midst of the rise and evolution of Generative AI. Foundational AI continues to deliver reliable and valuable insights and Causal AI is on the horizon. What do these three pillars of AI have in common and require? Data, and an immense amount of it, and not just a one-time infusion, but an ongoing flood of data to keep all these models working at the optimal level. I am so pleased that Scott Burk and Kinshuk Dutta took on the challenge of writing a book that dives into how to obtain, clean, integrate, and use data in this rapidly evolving landscape of Al. Having worked with Scott, I know first-hand his abilities and capabilities in working with all types of data. If you are interested in learning world-class best practices of preparing data for use in your AI environment, this book will be an invaluable resource for your journey into the multifaceted world of data for AI. John Thompson Author, Innovator, Adjunct Professor, University of Michigan, School of Information