finelybook
Designing the AI-Driven Data Foundations: Architecture, Principles, and Practice
Author(s): Sanjeev Mohan (Author)
- Publisher Finelybook 出版社: Wiley
- Publication Date 出版日期: August 18, 2026
- Edition 版本: 1st
- Language 语言: English
- Print length 页数: 272 pages
- ISBN-10: 139439666X
- ISBN-13: 9781394396665
Book Description
Build AI-powered data platforms that unify analytics and intelligence
The playbook for data and AI architecture has been rewritten. This is the guide to what comes next.
For decades, data architectures were optimized for a predictable set of workloads. AI changes every layer of the stack where autonomous agents, not just humans, consume data. The architectures that worked for traditional analytics cannot meet these demands.
Designing the AI-Driven Data Foundations is a comprehensive guide to architecting platforms for this new reality.
Drawing on his experience as principal analyst at SanjMo and former VP of Research at Gartner, where he advised thousands of enterprises on data strategy, renowned analyst Sanjeev Mohan delivers vendor-neutral guidance for navigating a landscape where yesterday’s best practices no longer apply. The book systematically unpacks each layer of contemporary data stacks, from operational and analytical data stores through ingestion, integration, analytics, generative AI, governance, security, privacy, and operations. The data architecture landscape is experiencing unprecedented disruption. AI has catalyzed changes across the entire data management stack: hardware optimized for vector operations, engineering practices reimagined for unstructured content, and consumption patterns transformed by autonomous agents. Designing the AI-Driven Data Foundations examines how the rise of unstructured data and AI agents as first-class consumers are redefining architectural principles that have guided the industry for decades.
You’ll also discover:
- A unified and comprehensive data and AI strategy
- Techniques to avoid vendor lock-in
- Frameworks for evaluating data stores
- Contextual data integration pattern that replaces ETL for AI workloads
- Coverage of AI-ready data governance, quality and security
- DataOps and observability practices that operationalize trust at scale
Perfect for data architects, and technical leaders who must make consequential platform decisions, Designing the AI-Driven Data Foundations translates the complexity of data and AI infrastructure into actionable architectural decisions that serve both human analysts and autonomous agents.
Editorial Reviews
Editorial Reviews
From the Back Cover
How to create AI-powered data platforms that unify analytics and intelligence
AI doesn’t fail because of bad models. It fails because the data foundation wasn’t built for it.
Models hallucinate because data lacks context. Pipelines break because governance was an afterthought. Agents fail because the infrastructure was designed for dashboards, not autonomous systems. The playbook for data architecture has been rewritten. This is the guide to what comes next.
Designing the AI-Driven Data Foundations delivers a vendor-neutral roadmap for building data infrastructure where AI is the design principle, not a bolt-on. Sanjeev Mohan walks technical leaders and architects through every layer of the stack: operational and analytical data stores, data engineering and integration patterns, analytics and data products, AI agents, governance frameworks, security and privacy controls, and operational practices for production AI systems. Each chapter delivers practical decision frameworks grounded in real-world trade-offs, comparing architectural approaches and identifying what actually works. This is not a book of prescriptions. It is a book of trade-offs, and the frameworks to navigate them.
About the Author
SANJEEV MOHANis the founder of SanjMo, a data and AI advisory firm, and a former Gartner Research Vice President. He advises Fortune 500 companies, cloud hyperscalers, and startups on data architectures and AI strategy. A prolific writer and speaker, Sanjeev has spent over three decades helping organizations turn data investments into measurable business outcomes.
