|What main methodology are you using for your analytics, data mining, or data science projects ? [200 votes total]
2014 poll 2007 poll
|CRISP-DM (86)|| 43%
|My own (55)|| 27.5%
|SEMMA (17)|| 8.5%
|Other, not domain-specific (16)|| 8%
|KDD Process (15)|| 7.5%
|My organizations' (7)|| 3.5%
|A domain-specific methodology (4)|| 2%
|None (0)|| 0%
Gregory Piatetsky, Editor, Business Understanding
Ralph, Business (domain) understanding is not binary - you can always have more! Part of the knowledge discovery and CRISP-DM process is to increase your business understanding
Ralph Winters, Business Understanding
I always thought of the Business Understanding part as a chicken or egg problem. You either have it and you can mine it, or you need to mine it to define it, if you don't.
James Taylor, Decision Modeling
I like CRISP-DM because it puts business understanding front and center at the beginning of the project. We have had some success with using decision modeling - based on the new Decision Model and Notation standard - as a way to express understanding of the business problem by modeling the decision that the analytic is designed to improve. More focused than simply saying "improve this metric", decision modeling helps focus analytic projects on improving the way the business acts today while providing great assets for planning deployment and adoption.
See http://decisionmanagementsolutions.com/decision-modeling-for-predictive- analytic-projects for more.
Robin Way, in his model deployment red paper, outlines a very nice ongoing methodology that covers not just model deployment but model maintenance as well. Model maintenance is a very important aspect for financial institutions.
Breno C. Costa, crisp-dm update?
In past, I looked for a data mining methodology and found crisp-dm, but it was not updated for a long time. Is there any initiative to update that methodology, and where i found documentation about it (specification, book or paper)?