Smarter Data Science: Succeeding with Enterprise-Grade Data and AI Projects
29 July 2020
By 作者:Neal Fishman , Cole Stryker, Grady Booch (Foreword)
pages 页数: 304 pages
Publisher Finelybook 出版社: John Wiley & Sons (29 July 2020)
Language 语言: English
The Book Description robot was collected from Amazon and arranged by Finelybook
Organizations can make data science a repeatable, predictable tool, which business professionals use to get more value from their data
Enterprise data and AI projects are often scattershot, underbaked, siloed, and not adaptable to predictable business changes. As a result, the vast majority fail. These expensive quagmires can be avoided, and this book explains precisely how.
Data science is emerging as a hands-on tool for not just data scientists, but business professionals as well. Managers, directors, IT leaders, and analysts must expand their use of data science capabilities for the organization to stay competitive. Smarter Data Science helps them achieve their enterprise-grade data projects and AI goals. It serves as a guide to building a robust and comprehensive information architecture program that enables sustainable and scalable AI deployments.
When an organization manages its data effectively, its data science program becomes a fully scalable function that’s both prescriptive and repeatable. With an understanding of data science principles, practitioners are also empowered to lead their organizations in establishing and deploying viable AI. They employ the tools of machine learning, deep learning, and AI to extract greater value from data for the benefit of the enterprise.
By following a ladder framework that promotes prescriptive capabilities, organizations can make data science accessible to a range of team members, democratizing data science throughout the organization. Companies that collect, organize, and analyze data can move forward to additional data science achievements:
Improving time-to-value with infused AI models for common use cases
Optimizing knowledge work and business processes
Utilizing AI-based business intelligence and data visualization
Establishing a data topology to support general or highly specialized needs
Successfully completing AI projects in a predictable manner
Coordinating the use of AI from any compute node. From inner edges to outer edges: cloud, fog, and mist computing
When they climb the ladder presented in this book, businesspeople and data scientists alike will be able to improve and foster repeatable capabilities. They will have the knowledge to maximize their AI and data assets for the benefit of their organizations.
Chapter 1 Climbing the AI Ladder 1
Chapter 2 Framing Part I: Considerations for Organizations Using AI 17
Chapter 3 Framing Part II: Considerations for Working with Data and AI 35
Chapter 4 A Look Back on Analytics: More Than One Hammer 57
Chapter 5 A Look Forward on Analytics: Not Everything Can Be a Nail 87
Chapter 6 Addressing Operational Disciplines on the AI Ladder 121
Chapter 7 Maximizing the Use of Your Data: Being Value Driven 147
Chapter 8 Valuing Data with Statistical Analysis and Enabling
Meaningful Access 175
Chapter 9 Constructing for the Long-Term 199
Chapter 10 A Journey’s End: An IA for AI 223
Appendix Glossary of Terms 263
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