About this episode
Guess what? Data science and AI initiatives are still failing here in 2024—despite widespread awareness. Is that news? Candidly, you’ll hear me share with Evan Shellshear—author of the new book Why Data Science Projects Fail: The Harsh Realities of Implementing AI and Analytics—about how much I actually didn’t want to talk about this story originally on my podcast—because it’s not news! However, what is news is what the data says behind Evan’s findings—and guess what? It’s not the technology.
In our chat, Evan shares why he wanted to leverage a human approach to understand the root cause of multiple organizations’ failures and how this approach highlighted the disconnect between data scientists and decision-makers. He explains the human factors at play, such as poor problem surfacing and organizational culture challenges—and how these human-centered design skills are rarely taught or offered to data scientists. The conversation delves into why these failures are more prevalent in data science compared to other fields, attributing it to the complexity and scale of data-related problems. We also discuss how analytically mature companies can mitigate these issues through strategic approaches and stakeholder buy-in. Join us as we delve into these critical insights for improving data science project outcomes.
Highlights/ Skip to:
(4:45) Why are data science projects still failing?
(9:17) Why is the disconnect between data scientists and decision-makers so pronounced relative to, say, engineering?
(13:08) Why are data scientists not getting enough training for real-world problems?
(16:18) What the data says about failure rates for mature data teams vs. immature data teams
(19:39) How to change people’s opinions so they value data more
(25:16) What happens at the stage where the beneficiaries of data don’t actually see the benefits?
(31:09) What are the skills needed to prevent a repeating pattern of creating data products that customers ignore??
(37:10) Where do more mature organizations find non-technical help to complement their data science and AI teams?
(41:44) Are executives and directors aware of the skills needed to level up their data science and AI teams?
Quotes from Today’s Episode
“People know this stuff. It’s not news anymore. And so, the reason why we needed this was really to dig in. And exactly like you did, like, keeping that list of articles is brilliant, and knowing what’s causing the failures and w
