By integrating predictive analytics with decision management, scoring and optimization in your organization's processes and operational systems, IBM® SPSS® Modeler helps your users and systems make the right decision every time.
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Analytical decision management.
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Automated modeling.
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Entity analytics.
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Social network analysis.
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Geospatial analytics.
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Modeling algorithms
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Deployment
More reasons to use SPSS Modeler
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Analyze almost all data. Conduct analysis regardless of where the data is stored (such as in a data warehouse, a database, Hadoop or flat file) and regardless of whether it is structured (age, price, product, location) or unstructured (text, emails, social media). With its client server architecture, SPSS Modeler can push the analysis back to the source for execution, which can minimize data movement and increase performance.
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Solve a variety of business problems with an extensive range of analytics. SPSS Modeler offers analytics that range from descriptive analytics to advanced algorithms and that include automated modeling, text analytics, entity analytics, social network analysis, decision management and optimization.
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Get up and running quickly. A comprehensive platform that provides analytics is next to worthless if no one can use it. The intuitive SPSS Modeler interface appeals to a wide range of users from the non-technical business user to the statistician, data miner or data scientist because it enables them analyze data in a way that suits them. The short learning curve for SPSS Modeler enables novices and advanced users to begin uncovering results almost immediately.
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Meet your needs with flexible deployment. Choose the deployment that best suits your requirements to optimize your existing IT investment. SPSS Modeler can operate independently or in operational systems on multiple platforms in your own environment or in the cloud.
New features in IBM SPSS Modeler 18
IBM SPSS Modeler adds the following features in this release.
Time Series node. A new Time Series node is available. The new node is similar to the Time Series node that was available in previous releases, but it runs with either embedded or remote IBM SPSS Analytic Server to process big data and display the resulting model in the output viewer. A new Streaming Time Series node that is based on the new Time Series node is also available.
Note: The previous Time Series, Streaming Time Series, and Time Intervals nodes have been deprecated in this release; however, their functionality is still supported in any existing streams.
Extensions Hub. A new Extensions Hub has been added (access from Extensions > Extensions Hub in IBM SPSS Modeler Client). The Extensions Hub is an interface for searching for, downloading, and installing extensions from the IBM SPSS Predictive Analytics collection on GitHub. For details, see the IBM SPSS Modeler Extensions documentation.
Custom Dialog Builder. Many improvements have been made and enhancements have been added to Custom Dialog Builder. For more information, see the SPSS Modeler Extensions documentation.
Python Spark on Modeler Server. In version 17.1, support for Python Spark was added (only R was supported previously) when running against IBM SPSS Analytic Server. Now SPSS Modeler Server is also supported. For more information, see the SPSS Modeler Extensions documentation.
Predictive Enterprise View and scenario files. IBM SPSS Collaboration and Deployment Services Enterprise View and scenario files (.scn) are no longer supported. Enterprise View source nodes cannot be modified in Deployment Manager. Old scenario files can be modified in IBM SPSS Modeler client and resaved as stream files. Also, scoring configurations that used a scenario file must be deleted and recreated based on a stream file.