Book Image

Building Analytics Teams

By : John K. Thompson
5 (1)
Book Image

Building Analytics Teams

5 (1)
By: John K. Thompson

Overview of this book

In Building Analytics Teams, John K. Thompson, with his 30+ years of experience and expertise, illustrates the fundamental concepts of building and managing a high-performance analytics team, including what to do, who to hire, projects to undertake, and what to avoid in the journey of building an analytically sound team. The core processes in creating an effective analytics team and the importance of the business decision-making life cycle are explored to help achieve initial and sustainable success. The book demonstrates the various traits of a successful and high-performing analytics team and then delineates the path to achieve this with insights on the mindset, advanced analytics models, and predictions based on data analytics. It also emphasizes the significance of the macro and micro processes required to evolve in response to rapidly changing business needs. The book dives into the methods and practices of managing, developing, and leading an analytics team. Once you've brought the team up to speed, the book explains how to govern executive expectations and select winning projects. By the end of this book, you will have acquired the knowledge to create an effective business analytics team and develop a production environment that delivers ongoing operational improvements for your organization.
Table of Contents (14 chapters)
12
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13
Index

Bias – accounting for it and minimizing it

We briefly discussed bias in Chapter 6, Ensuring Engagement with Business Professionals, but bias is a significant issue that we must face when building and managing an active and engaged advanced analytics and AI ecosystem.

Most people think of bias and they immediately talk about the data that is used to train systems. That is one very important part of bias. This is selection bias. We select data that we use to train our systems. Given that many aspects of our world are dominated by limited groups of people, we further institutionalize bias when selecting data from historical or current operational systems. Let's examine a few examples to bring the point to life.

Most C-level executives and board members are men, and more specifically, white men. When we select and use data about this group of people, we are including bias toward and related to white men toward the later stages of their careers. We bias toward men...