Minding the Machines

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.

Autorentext

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.

Klappentext
Make data science part of the lifeblood of your business, and avoid costly missteps By now, almost every organization has stepped into the world of big data analyticsand some of us have already tried again and again to get it right. We know that data science has the potential to unlock tremendous value, so why is it so hard to get it right? In Minding the Machines, analytics strategy expert Jeremy Adamson explains that the problem is often one of too many cooks in the kitchen, and too few recipes for success. This book shows you how to blend data science with business acumen. Inside, you'll find a roadmap for internalizing the three pillars of analytics: strategy, process, and people. Create an analytics operation that can truly transform outcomes.

• Install the right leaders and position your analytics unit within your organization
• Integrate analytics insights into the business in a constructive and meaningful way
• Identify and fully commit to projects that will add value over the long term
• Hire the right people and get them working for the organization as a whole Anyone who aspires to be part of a world-class analytics team will benefit from the tasks, methods, and frameworks clearly presented in this valuable book. For leaders craving a playbook that puts together all the key elements required for building a high performing Advanced Analytics & AI function in a comprehensive but down to earth manner, this book is most definitely a must read.
  Celia Wanderley, Chief Customer Officer and Head of Services at AltaML with detailed explanations and checklists throughout, anyone looking to grow an analytics and data science organization can find support within the book.
  Bill Franks, author of The Analytics Revolution, and Director, Center for Statistics and Analytical Research, Kennesaw State University a valuable resource for all parties as they advance the role of analytics, and being digital, in their organization
  Lee Ackerman, Director of Digital Strategy, SAIT's School for Advanced Digital Technology

Inhalt
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 NoCode 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