The success of today's most advanced technologies-AI, machine learning, data mesh, and analytics-depends on one critical element: a solid foundation of high-quality, well-architected data. In Data Architecture: Building the Foundation, bestselling author Bill Inmon and seasoned data expert Dave Rapien deliver a definitive guide to creating, managing, and evolving a data architecture that truly supports modern business needs. Whether you're implementing AI, driving business analytics, or transforming legacy systems, this book equips you with the foundational strategies and architectural principles to make it work with a focus on sustainability and scalability. This comprehensive guide helps readers understand why most organizations struggle with fractured, incomplete, and inaccessible data-and what can be done about it. You'll explore the role of a data architect, the essential elements of a solid data foundation, and how to integrate structured, textual, and analog data into a unified, coherent framework. From metadata and data lineage to integrity, accessibility, and completeness, each chapter delivers practical knowledge that drives real business value. Aimed at data architects, analysts, business leaders, and IT professionals, this book answers the question: What does a truly usable, scalable, and business-aligned data architecture look like? Readers will learn how to assess and transform legacy data systems, build effective data models, and implement robust data governance and integration strategies. The book delves into how to use Extract, Classify, and Load (ECL) to harmonize disparate datasets across business units.
It also explores the different types of data-structured, textual, and analog-and how each requires different techniques for transformation and analysis. If you're wrestling with data quality issues or trying to make sense of disconnected systems, this is the roadmap you've been missing. You'll gain an understanding of metadata management, naming conventions, and data completeness, while also mastering the crucial role of data accuracy, atomicity, and relatability in enabling trustworthy AI, predictive modeling, and operational efficiency. Practical insights into data currency, integration, and the probability of access ensure that you're not just managing data, but unlocking its potential.