Оглавление
Jeremy Adamson. Minding the Machines
Table of Contents
List of Tables
List of Illustrations
Guide
Pages
Minding the Machines. Building and Leading Data Science and Analytics Teams
Foreword
Introduction
How to Contact the Publisher
How to Contact the Author
CHAPTER 1 Prologue
For the Leader from the Business
For the Career Transitioner
For the Motivated Practitioner
For the Student
For the Analytics Leader
Structure of This Book
Why Is This Book Needed?
Communication Gap
Troubles with Taylorism
Rinse, Report, Repeat
Too Fast, Too Slow
More Data, More Problems
Summary
References
CHAPTER 2 Strategy
The Role of Analytics in the Organization
The Analytics Playbook
Data and Analytics as a Culture Change
Current State Assessment
Readiness Assessment
Capability Modeling and Mapping
Technology Stack Review
Data Quality and Governance
Stakeholder Engagement
Defining the Future State
Defining the Mandate
Analytics Governance Model
Target Operating Model
Define Your Principles
Functions, Services, and Capabilities
Interaction Models
Avatars and Personas
Mapping to Function
Organizational Design
Community of Practice
Project Delivery Model
Closing the Gap
Setting the Horizon
Establishing a Talent Roadmap
Consultants and Contractors
Change Management
Implementing Governance Models
Summary
References
CHAPTER 3 Process
Project Planning
Intake and Prioritization
Project Pipelines
Portfolio Project Management
Project Scoping and Planning
Scoping and Requirements Definition
Design Thinking
Regulatory
Operationalization
Planning
Statement of Work
Project Plan
Budget
Risks and Limitations
Project Execution
Governance Structure and Communication Plan
Project Kickoff
Agile Analytics
Change and Stakeholder Management
Skeuomorphs
AI 101 and Project Brainstorming
Iterative Insights
Closeout and Delivery
Automation
Project Debrief
Summary
References
CHAPTER 4 People
Building the Team
Success Factors
Team Composition
Hiring and Onboarding
Talent Development
Retention
Departures
The Data Scientist Hierarchy of Needs
Culture
Innovation
Communication
Succession Planning
Potential Pitfalls
Dunning-Kruger Effect
Diderot Effect
Leading the Team
Data Scientists as Craftspeople
Team Conventions
Formal Meetings
Daily—Optional Check-in/Huddle/Standup
Weekly—Mandatory Kickoffs/Tactical
Quarterly—Strategic
Coffee Chats
Managing Conflict
Relationship Management
Owning the Narrative
Performance Metrics
Summary
References
CHAPTER 5 Future of Business Analytics
AutoML and the No-Code Movement
Data Science Is Dead
The Data Warehouse
True Operationalization
Exogenous Data
Edge AI
Analytics for Good
Analytics for Evil
Ethics and Bias
Analytics Talent Shortages
Death of the Career Transitioner
References
CHAPTER 6 Summary
CHAPTER 7 Coda
Index
About the Author
About the Technical Editor
About the Foreword Author
Acknowledgments
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Jeremy Adamson
Many of the world's leading organizations can attribute their success to the fact that the practice of data science is increasingly becoming a strategic function. Analytics and data science enable consumer experiences that have become indispensable in our daily lives and deliver highly personalized recommendations and content, and this is now the expectation for almost everything else in our lives. The expectation of the customer has become immediate, personalized services that predict what it is they may want before they may even know it themselves. Data is what powers these great product experiences. Data science is no longer simply a technology function buried within IT or reserved purely for the tech giants in Silicon Valley. Data science and analytics will become increasingly indispensable in health care as it will improve diagnostic accuracy and efficiency. In finance, it will aid in the detection of anomalies and fraud. In manufacturing, it will aid in fault prediction and preventative maintenance. Whether you work in corporate strategy, research & insights, product development, human resources, marketing, technology, or finance, you will no longer be able to effectively compete without leveraging the talent and capabilities of the data science teams.
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For the current analytics leader, I hope that some parts of this book will challenge your views, other parts will confirm your experience, and the book as a whole will ultimately help you to build out a successful and engaged team.
The main body of this book has been organized within three key pillars: strategy, process, and people.
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