Failure is an option in predictive analytics

Like any historic big system implementation — think ERP — predictive analytics implementations offer lots of exposure for error. John Elder, CEO of data mining firm Elder Research, told ComputerWorld’s Robert L. Mitchell that while 90 per cent of the company’s projects are technical success, only 65 per cent of those ever get customer rollout.
 
RELATED CONTENT
 
While Mitchell’s story in ComputerWorld lists 12 predictive analytics screw-ups — based on interviews with expertts from  Elder, Abbott Analytics and Prediction Impact — most can be categories in a few broad categories.
 
* Data issues: Trying to create the perfect data set — on, conversely, not scrubbing enough garbage data — can both slow down or derail a predictive analytics project.
 
* Scoping issues: A firm grasp of the end result and sound project management principles, particularly with regard to timelines, are critical to a predictive analytics success. Starting with a massive, high-profile project is not the best way to go.
 
* Staff issues: There is a potential minefield here of competing agenda among the owners of the data, the IT department and subject-matter experts. Everyone has to be on the same page, so executive support vital.
 

Would you recommend this article?

0
0

Share

Thanks for taking the time to let us know what you think of this article!
We'd love to hear your opinion about this or any other story you read in our publication. Click this link to send me a note →

Jim Love, Chief Content Officer, IT World Canada
IT World Canada Staff
IT World Canada Staffhttp://www.itworldcanada.com/
The online resource for Canadian Information Technology professionals.

Related Tech News