Data Architecture: Building the Foundation, by Bill Inmon and David Rapien
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.
A problem
A foundation of sand
About data architecture
A solid foundation of data
Accessibility of data
Metadata
Completeness
Currency of data
Relatability of data
Origins of data
Naming conventions
Accuracy
Atomicity of data
Integrity of naming conventions
Integration of data
Summary
The focus of the foundation
Organizing the foundation
Analysis
Summary
Legacy data
Early computation
How we got here
Summary
Structured data
The data architect
Transforming legacy data
The data model
Kinds of data
Summary
The activities of the data architect
The data architect career path
Perspectives
Summary
Simple transformation
Who needs data transformation?
Business value
Technology for transformation
Analytics across the different types of data
Summary
Storage media
OLTP
Data warehousing
Database design
Linked data
Normalization
Data warehouse
Data marts
Data lake
Data lakehouse
Computer evolution
Probability of access of data
Summary
Structured data
Textual data
Analog data
Summary
Issues
Different forms of storage
Probability of access
Accessing bulk storage
Summary
The need for a catalog
Extract Classify Load (ECL)
Metadata
Summary
Constant maintenance
Movement across the architecture
Periodic auditing
Summary
The source of data
Setting up standards for data lineage
Standards set by government regulations
Standards for metadata data lineage
Standards for ETL and data integration
Data selection
Summary
Structured data quality
Data relationships
Other elements of data quality
Textual data quality
Analog data quality
Summary
Data must be simple
Data must be factual
Data must be accurate
Data must be traceable
Data must be timely
Data must be believable
Summary
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.
With clear explanations, real-world examples, and strategic frameworks, Data Architecture helps you bridge the gap between IT and business, enabling informed decision-making and future-proofed technology investments. Learn how to transform your data into a trusted asset that drives innovation, not frustration.
In addition to covering the technical aspects, the authors underscore the business value of good architecture. By aligning your data foundation with business goals—such as improving profitability, customer engagement, and operational efficiency—you can unlock powerful insights and avoid the classic pitfalls of garbage-in, garbage-out.
Whether you’re building your first data strategy or looking to modernize an aging infrastructure, Data Architecture: Building the Foundation is the essential reference for aligning your technology with your goals—and building a future-proof foundation that actually delivers on the promise of modern data.
Bill Inmon, the “father of the data warehouse,” has written 60 books published in nine languages. ComputerWorld named Bill one of the ten most influential people in the history of the computer profession. Bill’s latest adventure is the building of technology known as textual disambiguation.
David Rapien is an Associate Professor at the University of Cincinnati’s Lindner College of Business. Along with teaching for over 25 years, Dave has developed and managed data integration systems in the Sports Management, Medical, Insurance, Banking, Legal, Horse Racing, and School Administration industries.
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