Syndicated from BeyeNetwork
Curt Monash has been Thinking About Analytic Speed over on The Intelligent Enterprise Blog and makes some good points about the different kinds of analytic speed. One area I find lots of confusion in discussions of analytic speed that Curt does not touch on is the difference in time to build an analytic model and the time to execute it.
When you are using analytics in a real-time, operational system (and you should be) there is a big difference between building a new model in real-time and executing an existing model (scoring the transaction) in real time. Many systems require that you calculate the value of a predictive model for the current transaction in real-time so you can use it – how likely is this transaction to be fraudulent, what’s the retention risk of this inbound customer – but many of these models can be built offline.
You can harness offline processing power to build models, crunching lots of data and trying many different algorithms before deploying the result of all this work as a simple to execute element of a decision – it is often just a few rules, an additive scorecard or a formula. Just because you need the result in real time does not mean you need to figure out the math in real time. Something else to bear in mind when worrying about analytic speed.
If this is a topic that interests you, why not come to Predictive Analytics World next month and hear me and a bunch of other interesting people tell you all about it?