June, 2009

Malstone v0.8.2 released

This stylized analytic computation, a benchmark for clouds designed for data intensive computing, is available at the Google Code project of the Open Cloud Consortium.   MalStone is designed to use records generated by MalGen, also accessible at the same source.

One Day Course in Cloud Computing and Analytics

Posted by admin in Blog, analytics, cloud computing, courses on June 9, 2009

We’ll be offering a one day course introducing cloud computing and analytics in Chicago on June 22 and in San Mateo on July 14, 2009.

Information about the course can be found at blog.opendatagroup.com/courses

If you are currently between jobs, please send a cover letter indicating why you would like to attend the course and a resume to fee-waived-workshop@opendatagroup.com. Those selected can attend the course without charge. We have reserved five seats in the course for this purpose.

Three Common Mistakes in Analytic Projects

Posted by Robert Grossman in Blog, analytics on June 1, 2009

In this post, I’ll describe some of the most common mistakes that occur when managing analytic projects.

Mistake 1. Underestimating the time required to get the data. This is probably the most common mistake in modeling projects. Getting the data required for analytic projects usually requires a special request to the IT department. Any special requests made to IT departments can take time. Usually, several meetings are required between the business owners of the analytic problem, the statisticians building the models, and the IT department in order to decide what data is required and whether it is available. Once there is agreement on what data is required, then the special request to the IT department is made and the wait begins. Project managers are sometimes under the impression that good models can be built without data, just as statisticians are sometimes under the impression that modeling projects can be managed without a project plan.

Mistake 2. There is not a good plan for deploying the model. There are several phases in a modeling project. In one phase, data is acquired from the IT department and the model is built. A statistician is usually in charge of building the model. In the next phase, the model is deployed. This is the responsibility of the IT department. This requires providing the model with the appropriate data, post-processing the scores produced by the model to compute the associated actions, and then integrating these actions into the required business processes. Deploying models is in many cases just as complicated or more complicated than building the models and requires a plan. A good standards-compliant architecture can help here. It is often useful for the statistician to export the model as PMML. The model can then be imported by the application used in the operational system.

Mistake 3. Trying to build the perfect model. Another common mistake is trying to build the perfect statistical model. Usually, the impact of a model will be much higher if a model that is good enough is deployed and then a process is put in place that: i) reviews the effectiveness of the model frequently with the business owner of the problem; ii) refreshes the model on a regular basis with the most recent data; and, iii) rebuilds the model on a periodic basis with the lessons learned from the reviews.