Machine Learning Times
EXCLUSIVE HIGHLIGHTS
Jev Fever: Predictive AI Is Superhot Again
  Originally published in Forbes The more AI changes, the...
GenAI Owns the Hype, but Predictive AI Is Thriving
  EDITOR’S NOTE: This article is adapted from the...
The Data Disconnect: A Key Challenge for Machine Learning Deployment
  EDITOR’S NOTE: This article is excerpted from The...
A Brief History of Why Machine Learning Projects Stall
  EDITOR’S NOTE: This is the preface to The...
SHARE THIS:

11 years ago
The Case Against Quick Wins in Predictive Analytics Projects

 

When beginning a new predictive analytics project, the client often mentions the importance of a “quick win”. It makes sense to think about delivering fast results, in a limited area, that excites important stakeholders and gains support and funding for more predictive projects. A great goal.

It’s the implementation of the quick win in a predictive project that can be difficult. There are at least 2 challenges with using a traditional quick win approach to predictive analytics projects.

Challenge #1: Predicting Something That Doesn’t Get Stakeholders Excited

Almost daily I hear of another predictive project that was limited in scope and allowed people to dip their toe in the predictive water and get a “quick win”. The problem was the results of the project predicted something stakeholders didn’t care about or couldn’t take action on.

This content is restricted to site members. If you are an existing user, please log in on the right (desktop) or below (mobile). If not, register today and gain free access to original content and industry news. See the details here.

Comments are closed.