Managing data science projects

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.