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Smarter Data Science

Succeeding with Enterprise-Grade Data and AI Projects

Fishman, Neal / Stryker, Cole


1. Edition July 2020
304 Pages, Softcover
Wiley & Sons Ltd

ISBN: 978-1-119-69341-3
John Wiley & Sons

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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.

Foreword for Smarter Data Science xix

Epigraph xxi

Preamble xxiii

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

Index 269
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.