Smarter Data Science
Succeeding with Enterprise-Grade Data and AI Projects
1. Edition July 2020
304 Pages, Softcover
Wiley & Sons Ltd
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
COLE STRYKER is a journalist based in Los Angeles. He is the author of Epic Win for Anonymous and Hacking the Future.