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

Establishing a data culture

AA: How important is it for an organization to have a data culture, and what does a successful one look like?

JTW: I think the data culture question is really hard. It’s easier to engineer software than to engineer data culture. Inherently, some people are just not built this way, and I think I need to acknowledge that. In the distribution of people in the company, there are large populations who are very number- and data-averse, and we need to live with that.

I also think data culture itself has different aspects. Sometimes, we think about it as the ability to interpret the output of a data science project because that seems the most applicable to a data team. But it is a lot of other things, and I think the way we approach it must have different facets.

For instance, someone might never want to see the output of an R model, but they might be OK to understand their own cognitive biases and how their mind plays tricks on them. We have this...