Minding the Machines: Building and Leading Data Science and Analytics Teams

Description

Organize, plan, and build an exceptional data analytics team within your organization In Minding the Machines: Building and Leading Data Science and Analytics Teams, AI and analytics strategy expert Jeremy Adamson delivers an accessible and insightful roadmap to structuring and leading a successful analytics team. The book explores the tasks, strategies, methods, and frameworks necessary for an organization beginning their first foray into the analytics space or one that is rebooting its team for the umpteenth time in search of success. In this book, you’ll discover: A focus on the three pillars of strategy, process, and people and their role in the iterative and ongoing effort of building an analytics team Repeated emphasis on three guiding principles followed by successful analytics teams: start early, go slow, and fully commit The importance of creating clear goals and objectives when creating a new analytics unit in an organization Perfect for executives, managers, team leads, and other business leaders tasked with structuring and leading a successful analytics team, Minding the Machines is also an indispensable resource for data scientists and analysts who seek to better understand how their individual efforts fit into their team’s overall results. Table of Contents Title Page Foreword Introduction 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? Summary References Chapter 2: Strategy The Role of Analytics in the Organization Current State Assessment Defining the Future State Closing the Gap References Chapter 3: Process Project Planning Project Execution Summary References Chapter 4: People Building the Team Leading the Team 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 Copyright Dedication About the Author About the Technical Editor About the Foreword Author Acknowledgments End User License Agreement