Book Image

Building Analytics Teams

By : John K. Thompson
4 (2)
Book Image

Building Analytics Teams

4 (2)
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

Team architecture/structure options

In my mind, most concepts exist on a continuum. Building a successful advanced analytics and AI team is typified by two approaches that inhabit the two poles of the relevant continuum – Artisanal or Factory:

Figure 2.1: Artisanal and Factory team structure comparison

The Artisanal team architecture/structure

Let's start with the Artisanal approach.

The Artisanal approach is where the data scientists are the owners, managers, experts, and driving force behind their projects.

The data scientists design and execute every step of the process. The data scientists engage with the project sponsors, the subject matter experts, internal and external consultants, syndicated data providers, and any other individual or group that has a role to play in the project.

Data scientists capable of executing and managing the artisanal approach possess exemplary communication skills, are open to listening to a wide range of...