Smarter Data Science: Succeeding with Enterprise-Grade Data and AI Projects (智慧數據科學：成功實現企業級數據與人工智慧專案)

PRAISE FOR SMARTER DATA SCIENCE

"This work provides benefit to a variety of roles, including architects, developers, product owners, and business executives. For organizations exploring AI, this book is the cornerstone to becoming successful."
--Harry Xuegang Huang Ph.D., External Consultant, A.P. Moller - Maersk

"Presents a holistic model that emphasizes how critical data and data management are in implementing successful value-driven data analytics and AI solutions. The book presents an elegant and novel approach to data management."
--Ali Farahani, Ph.D., Former Chief Data Officer, County of Los Angeles; Adjunct Associate Professor, USC

"The authors seek and speak the truth, and penetrate into the core of the challenge most organizations face in finding value in their data. Our industry needs to move away from trying to connect the winning dots by 'magical' technologies and overly simplified approaches. This book provides the necessary guidance."
--Jan Gravesen, M.Sc., IBM Distinguished Engineer, Director and Chief Technology Officer, IBM

BUILD A ROBUST INFORMATION ARCHITECTURE THAT SCALES AND DELIVERS LONG-TERM VALUE

Large organizations are racing to implement advanced data science. All too often, our AI endeavors turn out to be dead-end science projects that never deliver sustainable business value. What are we missing? In Smarter Data Science: Succeeding with Enterprise-Grade Data and AI Projects, you'll discover the pillars of information architecture that you must understand and implement.

Data analytics and AI only add value when they can predictably and consistently deliver business insights and scale across the organization. Smarter Data Science outlines an effective and practical way for organizing, managing, and evaluating data, so you can establish an information architecture to better drive AI and data science.

You'll learn how to:

• Simplify data management, making data available when and where it is needed
• Improve time to value for operationalizing AI use cases
• Make AI and data insights accessible across the enterprise
• Scale complex AI scenarios dynamically and in real time
• Develop an information architecture that brings predictable, repeatable value