As James points out, predictive analytics is a great way to reduce cost and drive efficiency — and a predictive analytics project no longer has to be a high risk or costly undertaking! Once you have developed your decision models, it is time to reduce the cost of deployment, integration and operational execution of predictive analytics.

Open standards, like the Predictive Model Markup Language (PMML) , and Cloud Computing deployment solutions offer a cost-effective, on-demand entry into real-time, operational predictive analytics.

For an overview of what the leading data mining vendors have to say about the topic, please see the
KDD 2009 Panel Report: Open Standards and Cloud Computing

RT @tweetmeme Two more dimensions of analytic speed — JT on EDM [link to post] #analytics #datamining #cloud #aws #ec2

– Posted using Chat Catcher

I would like to add one more dimension of analytic speed – the time to deploy an analytic model for use in any operational system or Enterprise Decision Management context. Let’s call it time-to-market for the predictive model.

We often see a significant gap between building a model and executing it, e.g., in real-time. Often the effort to translate such a predictive model from the scientists’ data mining environment into an executable decision element takes weeks, if not months, and often relies on custom coding.

The Predictive Model Markup Language (PMML) standard closes this gap and allows users to share models between vendors and environments, literally cutting the time-to-market (deployment) to a few minutes.

For example, Zementis leverages the PMML standard to deploy models from virtually all major data mining vendor tools on the Amazon Elastic Compute Cloud (EC2).