More from the machine learning meetings at UC a few weeks ago. SAS went over some interesting case studies I am now examining: Managing the Analytical Life Cycle for Continuous Innovation
From Data to Decision, The following describes an old problem with analytical models of any form. On the right the diagram I use to talk this, a slightly different view than the SAS approach.
" ... The organization has nearly 120 analytical models in production to support marketing, pricing, operational risk, credit risk, fraud and finance functions. Analysts develop these models without formalized or standard processes across business units to store, deploy and manage the portfolio of models. Some models don’t have any documentation describing the model’s owner, business purpose, usage guidelines or other information necessary for managing the model or explaining it to regulators. Model results are provided to management with limited controls and requirements. Because different data sets and variables are used to create the models, results are inconsistent. There is little validation or back testing. Managers make decisions based on the model results they receive, and everyone hopes for the best. ... "