Derek W. Gibson

Derek W. Gibson

I’m Derek Gibson. I write and teach about data, AI, and misinformation, and how to find the truth in the noise.

I have over 30 years of analytics leadership behind me, including more than two decades at Wells Fargo. Today I teach as an Adjunct Professor at the Wake Forest University School of Business. More about me

Author of two books · Adjunct Professor, Wake Forest University School of Business · Over 30 years in analytics leadership

My New Book

Book cover of Data, AI, and Noise by Derek W. Gibson

Data, AI, and Noise: The Search for Truth in Information and Algorithms

Coming November 2, 2026
Wiley Publishing

How AI and algorithm-driven platforms are reshaping trust, and why transparency, information literacy, and human oversight matter more than ever.

Also by Derek

Cover of Data Duped by Derek W. Gibson and Jeffrey D. Camm

Data Duped: How to Avoid Being Hoodwinked by Misinformation

With Jeffrey D. Camm. Rowman & Littlefield, 2023. A guide to spotting misleading numbers and building a healthy sense of data defense.

Praise for Data Duped

Being an effective consumer of data and information is a prerequisite for living a good life in the 21st century. This book has the tools and insights you need to become numerate and literate in a data-driven world.

Thomas H. Davenport, Distinguished Professor, Babson College, Research Fellow, MIT Initiative on the Digital Economy, Author of Competing on Analytics and All In on AI

All of us are drowning in data. Gibson and Camm provide a roadmap through the interpretation of data that will transform people who know little about data into intelligent consumers of all types of data.

Wayne L. Winston, Professor Emeritus of Decision Sciences, Kelley School of Business, Indiana University

Work With Me

Speaking & Workshops

Talks and workshops on AI, misinformation, and the decisions that define your organization.

Decision Consulting

Advising on AI strategy, predictive analytics, reporting metrics, and training.

Books

Data, AI, and Noise (Wiley, 2026) and Data Duped (Rowman & Littlefield, 2023).