Building Analytics Teams by Thompson, John K. -- Read -- Imperial Library of Trantor

Index

Preface

Who this book is for What this book covers To get the most out of this book Get in touch

Introduction

Becoming data and analytically driven An analytical mindset Building an analytics team and an environment for collaboration Collaborators in the analytics journey Selecting successful projects Organizational dynamics Competitive advantage or simply staying competitive The core collaboration/innovation cycle Focusing on self-renewing processes, not projects – an example Summary

An Overview of Successful and High-Performing Analytics Teams

Introduction AI in the education system We are different The original sin The right home Ethics Summary Chapter 1 footnotes

Building an Analytics Team

Organizational context and consideration Internships and co-op programs Diversity and inclusion Neurodiversity Disciplinary action Labor market dynamics A fit to be found Evolved leadership is a requirement for success Continual learning and data literacy at the organizational level Defining a high-performing analytical team The general data science process Team architecture/structure options The implications of proprietary versus open source tools Summary Chapter 2 footnotes

Managing and Growing an Analytics Team

Managerial focus and balance Sponsor and stakeholder management An open or fixed mindset? Productivity premium The rhythm of work Personal project portfolio Managing team dynamics The front end of the talent pipeline It takes a team Simply the best Organizational maxims Summary Chapter 3 footnotes

Leadership for Analytics Teams

Artificial intelligence and leadership Traits of successful analytics leaders Building a supportive and engaged team Managing team cohesion Being the smartest person in the room Good (and bad) ideas can come from anywhere Emerging leadership roles – Chief Data Officer and Chief Analytics Officer Hiring the Chief Data Officer or Chief Analytics Officer – where to start? Summary Chapter 4 footnotes

Managing Executive Expectations

You are not the only game in town Know what to say Know how to say it Shaping and directing the narrative Know before you go How many of us are out there? There is a proven path to success What are you hoping to accomplish? Outsourcing Elephants and squirrels Daily operations Summary Chapter 5 footnotes

Ensuring Engagement with Business Professionals

Overcoming roadblocks to analytics adoption Organizational culture Data or algorithms – the knee of the curve or the inflection point A managerial mindset The skills gap Linear and non-linear thinking Do you really need a budget? Not big data but lots of small data Introductory projects Value realization Summary Chapter 6 footnotes

Selecting Winning Projects

Analytics self determination Communicating the value of analytics Relative value of analytics The value of analytics, made easy Enabling understanding Enterprise-class project selection process Understanding and communicating the value of projects Delegation of decision making Technical or organizational factors Guidance to end users Where is the value in a project? Operational considerations Selling a project – vision, value, or both? Don't make all the decisions Do the subject matter experts know what "good" looks like? The project mix – small and large Opportunity and responsibility Summary

Operationalizing Analytics – How to Move from Projects to Production

The change management process Getting to know the business Change management Analytics and discovery Analytical and production cycles and systems – initial projects Summary

Managing the New Analytical Ecosystem

Stakeholder engagement – your primary purpose Bias – accounting for it and minimizing it Ethics Summary

The Future of Analytics – What Will We See Next?

Data AI today Quantum computing and AI Artificial General Intelligence Today, we are failing Teaching children to love numbers, patterns, and math Blending rote memorization with critical thinking as a teaching paradigm Summary Chapter 10 footnotes

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