Helping your analytic projects succeed with decision modeling and CRISP-DM

I recently wrote three articles for KDnuggets on the potential for decision modeling in the context of the CRISP-DM methodology for analytic projects:

• Four Problems in Using CRISP-DM and How To Fix Them
  CRISP-DM is the leading approach for managing data mining, predictive analytic and data science projects. CRISP-DM is effective but many analytic projects neglect key elements of the approach.
• Bringing Business Clarity To CRISP-DM
  Many analytic projects fail to understand the business problem they are trying to solve. Correctly applying decision modeling in the Business Understanding phase of CRISP-DM brings clarity to the business problem.
• Fixing Deployment and Iteration Problems in CRISP-DM
  Many analytic models are not deployed effectively into production while others are not maintained or updated. Applying decision modeling and decision management technology within CRISP-DM addresses this.

Check them out. And if you are interested in how one global leader in information technology is using decision modeling to bring clarity to its analytic and data science programs, check out this Leading Practices Brief from the International Institute for Analytics. We have found that a focus on decision modeling early really helps get and keep analytics projects on track and makes it much easier to operationalize the results.