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

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:
Foreword xiii Introduction xvi Chapter 1 Prologue 1 For the Leader from the Business 5 For the Career Transitioner 6 For the Motivated Practitioner 6 For the Student 7 For the Analytics Leader 8 Structure of This Book 8 Why is This Book Needed? 9 Communication Gap 9 Troubles with Taylorism 10 Rinse, Report, Repeat 12 Too Fast, Too Slow 13 More Data, More Problems 14 Summary 15 Chapter 2 Strategy 17 The Role of Analytics in the Organization 20 The Analytics Playbook 20 Data and Analytics as a Culture Change 24 Current State Assessment 26 Readiness Assessment 26 Capability Modeling and Mapping 28 Technology Stack Review 32 Data Quality and Governance 34 Stakeholder Engagement 35 Defining the Future State 37 Defining the Mandate 39 Analytics Governance Model 40 Target Operating Model 42 Define Your Principles 43 Functions, Services, and Capabilities 43 Interaction Models 44 Organizational Design 48 Community of Practice 52 Project Delivery Model 55 Closing the Gap 57 Setting the Horizon 58 Establishing a Talent Roadmap 59 Consultants and Contractors 60 Change Management 62 Implementing Governance Models 64 Summary 65 Chapter 3 Process 69 Project Planning 73 Intake and Prioritization 73 Project Pipelines 77 Portfolio Project Management 80 Project Scoping and Planning 83 Scoping and Requirements Definition 86 Planning 92 Project Execution 96 Governance Structure and Communication Plan 99 Project Kickoff 102 Agile Analytics 103 Change and Stakeholder Management 106 Skeuomorphs 106 AI 101 and Project Brainstorming 107 Iterative Insights 110 Closeout and Delivery 111 Automation 112 Project Debrief 114 Summary 118 Chapter 4 People 121 Building the Team 122 Success Factors 123 Team Composition 128 Hiring and Onboarding 129 Talent Development 131 Retention 136 Departures 137 The Data Scientist Hierarchy of Needs 139 Culture 140 Innovation 145 Communication 147 Succession Planning 149 Potential Pitfalls 151 Dunning-Kruger Effect 152 Diderot Effect 153 Leading the Team 154 Data Scientists as Craftspeople 157 Team Conventions 160 Formal Meetings 162 Coffee Chats 164 Managing Conflict 167 Relationship Management 169 Owning the Narrative 175 Performance Metrics 177 Summary 181 Chapter 5 Future of Business Analytics 187 AutoML and the No‐Code Movement 189 Data Science is Dead 192 The Data Warehouse 195 True Operationalization 196 Exogenous Data 198 Edge AI 199 Analytics for Good 200 Analytics for Evil 201 Ethics and Bias 203 Analytics Talent Shortages 204 Death of the Career Transitioner 206 Chapter 6 Summary 211 Chapter 7 Coda 213 Index 215

About the Author :
Jeremy Adamson is a leader in AI and analytics strategy across industries. Jeremy has worked with major organizations to establish leadership positions in data science and to unlock real business value using advanced analytics. He holds an MBA from the University of Calgary and is a professional engineer in the province of Alberta.