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

The value of analytics, made easy

Part of the issue lies with the leadership of the analytics team. Recently, I had a conversation with a group of subject matter experts, a project sponsor, and the analytics team members. The analytics team presented initial results from a test that was underway, they explained that we had seen positive and negative operating results from the test. I asked the team as a whole to highlight and extract the effects of the test into the following elemental components – naturally occurring business results; results from known, yet unrelated, operational changes; and results from external events (i.e. competitive activity, natural disasters, man-made or market changes) and from the tests that we designed and inserted into the operational mix. The people who fought the hardest against the request or directive to take the results down to the next level were the data scientists. The analytics team argued that there were no compelling reasons to tease...