SPSS Modeler and DSX — Better Together

Steven Astorino
May 22, 2018 · 5 min read

The phrase “better together” often implies that the sum of the parts is greater than the individual components. I believe this to be true for the combination of SPSS Modeler embedded within the IBM Data Science Experience (DSX).

SPSS Modeler is a visual predictive tool that has been redesigned with a new advanced architecture based both on open source as well as IBM research technology along with a new fresh experience to fit the persona of a visual data scientist and even an advanced line of business user.

With the recent industry focus on A.I. and machine learning, data science is top of mind for many organizations. IBM responded to this market need with the award-winning IBM Data Science Experience — a collaborative environment for multiple personas to build next generation A.I. solutions through an intuitive cognitive driven interface.

A Platform for Collaborative Data Science

DSX has been infused into Power AI, Linux on Z, PureApplication, Decision Optimzation, IBM Cloud Private for Data, IBM Integrated Analytics System, Cognos analytics, our Hadoop based offerings and more — so as the tool becomes pervasive and standard across these environments it becomes an ideal host for including more ML and AI capabilities. It made sense that SPSS Modeler should be integrated into DSX offerings — private cloud, public cloud and DSX Local.

There are two main audiences for DSX + SPSS Modeler:

  1. “Traditionalists” are used to and prefer packaged solutions — analytics are a means to an end and there are typically multiple people working together on various analytics projects either within the department or across an organization.

BOTH of these profiles have been taken into account by this data science platform.

Our primary strategy is to serve data science teams working in the enterprise to achieve business outcomes. Common to all data science teams is the need to access and manage data, the need to build and experiment with models, deploy and manage these models in production and the need to allow individuals with varying specialties and skills to build on each other’s work as shown in figure 1.

Not all data science teams are the same, therefore the way we serve our customers is to provide a platform that allows integration into tools that a team might need.

Figure #1: DSX Local and SPSS Modeler serving multiple customer scenarios

IBM Data Science Experience (DSX)’s intent is to serve the core set of needs for data science teams.

SPSS Modeler’s intent is to serve the visual productivity needs of a data scientist and business analysts. When a data scientist is part of a broader team, they can use the integration into the DSX platform. It is also offered for an individual that is not part of a team of data scientists, for example somebody in a line of business that’s looking to analyze their marketing data.

A Closer Look….

SPSS Modeler integrates with DSX Local. You can add SPSS Modeler streams to a project in DSX Local for example and use the shared data sources and connections of the project (figure 2).

Figure #2: DSX Local integration and importing SPSS Modeler Streams

It should be easy to import existing SPSS Modeler streams into SPSS Modeler for DSX (figure 2). In the desktop client a stream can be created. When creating a new SPSS Modeler flow users can simply import the stream file with a couple of mouse clicks. The stream will render into the new SPSS Modeler UI. The newly rebuilt interface offers improved navigation and ease of use. Users will be exposed to new interactive visualizations (see figure 3) and have the ability to deploy the results in model management and deployment.

Figure #3: SPSS Modeler Interactive Visualization

The combination of optimization, a visual productivity tool for data science (SPSS Modeler) and a full set of open source tools and capabilities helps provide a flexible, open, unified platform.

SPSS Modeler is an operation-ready solution that can scale to meet the needs of the business and can help reduce time to value from months and weeks to days or shorter. Sharing the same code base users can build on-premise and deploy data science solutions in public cloud or vice versa — or a hybrid / multi-cloud. It is suitable for organizations of any size and helps accelerate go-live tasks as part of their data science lifecycle.

Your Next Steps…

For data scientists looking for a sophisticated modeling tool that is part of a collaborative cognitive environment supporting the A.I. / data science life cycle — from data ingest, to the creation, training, test, deploy, monitor and management of models — try DSX with SPSS Modeler today. Together this combination of capabilities can help organizations address a lack of skills around coding, while supporting Notebook integration, provide an environment for quick prototyping/experimentation with built in cognitive assistance and provide a simplified path to deployment, monitoring and management.

Try it for free here.


Steven Astorino, Vice President of Development, Private Cloud Platform and z Analytics

Follow me on twitter @astorino_steven

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