Book Image

Creators of Intelligence

By : Dr. Alex Antic
Book Image

Creators of Intelligence

By: Dr. Alex Antic

Overview of this book

A Gartner prediction in 2018 led to numerous articles stating that "85% of AI and machine learning projects fail to deliver.” Although it's unclear whether a mass extinction event occurred for AI implementations at the end of 2022, the question remains: how can I ensure that my project delivers value and doesn't become a statistic? The demand for data scientists has only grown since 2015, when they were dubbed the new “rock stars” of business. But how can you become a data science rock star? As a new senior data leader, how can you build and manage a productive team? And what is the path to becoming a chief data officer? Creators of Intelligence is a collection of in-depth, one-on-one interviews where Dr. Alex Antic, a recognized data science leader, explores the answers to these questions and more with some of the world's leading data science leaders and CDOs. Interviews with: Cortnie Abercrombie, Edward Santow, Kshira Saagar, Charles Martin, Petar Veličković, Kathleen Maley, Kirk Borne, Nikolaj Van Omme, Jason Tamara Widjaja, Jon Whittle, Althea Davis, Igor Halperin, Christina Stathopoulos, Angshuman Ghosh, Maria Milosavljevic, Dr. Meri Rosich, Dat Tran, and Stephane Doyen.
Table of Contents (23 chapters)
1
Chapter 1: Introducing the Creators of Intelligence

Summary

I really enjoyed my discussion with Jason. He provided great insight into the human side of making an analytics capability successful. I particularly liked his concept of categorising the value that can be gained from analytics into three buckets: insights, automation, and new capabilities.

One of the themes we discussed was that for an organisation to be data-driven, decision-makers need to be prepared to change their minds if the data suggests it. This means the change to become data-driven involves not just data and modeling but also the psychology and environment of the decision-maker.

Jason raised the a heavy reliance on data and analytics maturity models without understanding their context and the nuances of doing data science in practice can be a recipe for failure. Business needs and value ultimately drive what type/mix of descriptive/predictive modeling needs to be done over time, rather than some notion of which type is more “mature.” Maturity...