Driving Digital Transformation through Data and AI: A Practical Guide to Delivering Data Science and Machine Learning Products

✍ Scribed by Alexander Borek, Nadine Prill

• Publisher: Kogan Page
• Year: 2020
• Tongue: English
• Leaves: 265
• Edition: 1
• Category: Library

✦ Synopsis

Leading tech companies such as Netflix, Amazon and Uber use data science and machine learning at scale in their core business processes, whereas most traditional companies struggle to expand their machine learning projects beyond a small pilot scope. This book enables organizations to truly embrace the benefits of digital transformation by anchoring data and AI products at the core of their business.

It provides executives with the essential tools and concepts to establish a data and AI portfolio strategy as well as the organizational setup and agile processes that are required to deliver machine learning products at scale. Key consideration is given to advancing the data architecture and governance, balancing stakeholder needs and breaking organizational silos through new ways of working.

Each chapter includes templates, common pitfalls and global case studies covering industries such as insurance, fashion, consumer goods, finance, manufacturing and automotive. Covering a holistic perspective on strategy, technology, product and company culture, Driving Digital Transformation through Data and AI guides the organizational transformation required to get ahead in the age of AI.

✦ Table of Contents

Cover
Contents
List of figures
List of tables
01 Introduction
Learning objectives for this chapter
What you can expect from this book
A new competitive playing field driven by data and AI
What makes a machine intelligent?
How digital players use machine learning to their advantage
Why many digital transformation initiatives struggle
How to scale data and AI across the organization
Summary and conclusion
Notes
02 Strategy and vision for data and AI
Learning objectives for this chapter
Key principles for creating the strategy and vision
Reinventing the corporate strategy and business vision
Creating the data product strategy
Deriving the capability strategy
Developing the transformation strategy
Lessons learned and pitfalls for strategy and vision
Template for a transformation roadmap
Summary and conclusion
Notes
03 Data product design
Learning objectives for this chapter
Key principles for designing data products
Ideating data products
Designing data products
Validating data products
Checklist for data product design
Summary and conclusion
Note
04 Data product delivery
Learning objectives for this chapter
Key principles for delivering data products
Planning the delivery of data products
Developing and deploying data products
Operating and scaling data products
Lessons learned and pitfalls for data product delivery
Checklist for data product delivery
Summary and conclusion
Note
05 Capabilities and agile organization
Learning objectives for this chapter
Key principles for capabilities and agile organization
Determining core capabilities for data and AI
Defining roles and responsibilities to implement capabilities
Setting up the agile organization
Implementing and adapting processes
Lessons learned and pitfalls for capabilities and agile organization
Checklist for capabilities and agile organization
Summary and conclusion
Note
06 Technology and governance
Learning objectives for this chapter
Key principles for technology and governance
Building the data platform
Setting up the architecture and development standards
Implementing data and AI governance
Lessons learned and pitfalls for technology and governance
Checklist for technology and governance
Summary and conclusion
Notes
07 Transformation and culture
Learning objectives for this chapter
Key principles for transformation and culture
Growing a data and AI driven culture
Successfully managing transformational change
Implementing the strategy and vision
Lessons learned and pitfalls for transformation and culture
Checklist for transformation and culture
Summary and conclusion
Notes
Index