Smarter Data Science: Succeeding with Enterprise-Grade Data and AI Projects

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

《更聰明的數據科學：成功實施企業級數據和人工智慧項目》讚譽

「這本書對於各種角色都有益處，包括架構師、開發人員、產品負責人和企業高管。對於探索人工智慧的組織來說，這本書是成功的基石。」
-- Harry Xuegang Huang博士，A.P. Moller - Maersk外部顧問

「本書提出了一個全面的模型，強調數據和數據管理在實施價值驅動的數據分析和人工智慧解決方案中的重要性。這本書提出了一種優雅而新穎的數據管理方法。」
-- Ali Farahani博士，洛杉磯縣前首席數據官，南加州大學兼職副教授

「作者們追求並講述真相，深入核心挑戰，這是大多數組織在發現數據價值方面面臨的。我們的行業需要擺脫試圖通過'神奇'技術和過度簡化的方法來連接成功點的方式。這本書提供了必要的指導。」
-- Jan Gravesen碩士，IBM杰出工程師，IBM董事兼首席技術官

建立一個可擴展且提供長期價值的強大信息架構

大型組織正在競相實施先進的數據科學。然而，我們的人工智慧努力往往只是無法提供可持續商業價值的死胡同科學項目。我們錯過了什麼？在《更聰明的數據科學：成功實施企業級數據和人工智慧項目》中，您將了解必須理解和實施的信息架構支柱。

只有在能夠可預測且持續提供業務洞察並在組織中擴展時，數據分析和人工智慧才能增加價值。《更聰明的數據科學》概述了一種有效且實用的方式，用於組織、管理和評估數據，以便更好地推動人工智慧和數據科學。

您將學到如何：
- 簡化數據管理，使數據在需要時和需要的地方可用
- 提高實現人工智慧用例的價值交付時間
- 使人工智慧和數據洞察在整個企業中可訪問
- 動態且實時地擴展複雜的人工智慧場景
- 建立一個可預測且可重複提供價值的信息架構

NEAL FISHMAN is a Distinguished Engineer and CTO of Data-Based Pathology at IBM. He is an IBM-certified Senior IT Architect and Open Group Distinguished Chief Architect.

COLE STRYKER is a journalist based in Los Angeles. He is the author of Epic Win for Anonymous and Hacking the Future.

NEAL FISHMAN 是 IBM 的杰出工程師和數據病理學首席技術官。他是 IBM 認證的高級 IT 架構師和開放組織杰出首席架構師。

COLE STRYKER 是一位位於洛杉磯的記者。他是《Epic Win for Anonymous》和《Hacking the Future》的作者。