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
