IBM SPSS Modeler

Why IBM SPSS Modeler?

 IBM SPSS Modeler is a complete Advanced Analytics workbench. It includes functionality in the three major phases of your project:

  1. ETL and Data Preparation
  2. Modeling
  3. Deployment

Modeler is not simply about exploring your data. It is most powerful when used to uncover powerful patterns in your ongoing business processes and then capitalizing on those patterns by deploying models into your business to make more accurate predictions leading to better decisions and optimal outcomes.

 How does it do it?

Data Merging 1

 ETL and Data Preparation

Modeler is compatible with all of your data. Analysts can have direct access to multiple sources of data, and can merge, rearrange, and augment their data on their own. Data maninpulation in Modeler can be learned in a matter of days even by analysts without a lot of experience with ETL. Expert users can direct Modeler towards today’s Big Data and efficiently tackle many millions of rows of data.


Modeler has dozens of advanced algorithms for finding patterns in your data. Modeler even has algorithms that help you narrow your search. These automated Modeling techniques are quite powerful. They multiply the analyst’s effort getting you to your destination faster. While even a novice user can provide value to their organization, in the hands of an expert Modeler is even more powerful.

Building Models 3


Modeler has some deployment capability built right into the workbench. An analyst can easily score new data to predict likely future outcomes. But in the hands of a deployment expert, Modeler can be even more powerful. Rerunning models nightly, scoring your new data in real time, and automatically comminicating to your decision support systems are all possible. Don’t let your organization be counseled by a team that can not work with you on all of these options. Insist on expertise in IBM Analytical Decision Managment and IBM SPSS Collaboration and Deployment Services. Make sure that your deployment strategy is complete.

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