EPISODE · Dec 17, 2025 · 20 MIN
Managing data science projects
from Pomodoro Breaks · host Panigrahi Nirma
Data science and AI projects fail frequently, often exceeding 80%, due to exaggerated promises and complexity beyond technical modeling. Written by experts, this book reveals the harsh realities necessary for success. Failures usually fall into four areas: Strategy, Process, People, and Technology. Crucial non-technical challenges include insufficient leadership buy-in, poor data quality, and inadequate communication, leading to billions in wasted resources and lost trust in data-driven decisions.
What this episode covers
Data science and AI projects fail frequently, often exceeding 80%, due to exaggerated promises and complexity beyond technical modeling. Written by experts, this book reveals the harsh realities necessary for success. Failures usually fall into four areas: Strategy, Process, People, and Technology. Crucial non-technical challenges include insufficient leadership buy-in, poor data quality, and inadequate communication, leading to billions in wasted resources and lost trust in data-driven decisions.
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Managing data science projects
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