How to stop data science projects from failing
Given the increasing prevalence of AI in data analytics, I was surprised to read that most data science projects fail: 3 steps for creating a data-to-value ecosystem.
Why is this the case? Perhaps lack of skilled talent, overcomplexity of the projects, or data security/governance issues? Do you agree most data science projects are expected to fail?