SPSS Analytical Decision Management for Linux on System z

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Optimize and automate decisions that can consistently generate successful outcomes

IBM® SPSS® Analytical Decision Management for Linux on System z enables organizations to optimize high-volume transactional decisions before deployment, so that they can consistently maximize outcomes. Using a combination of predictive models, business rules, optimization and scoring, the solution guides employees and systems to instantly make the best possible data-driven decisions.

SPSS Analytical Decision Management for Linux on System z combines the power of predictive analytics with the performance, scalability and availability of the System z platform. This enables businesses to adapt to increasing volumes of data and a growing number of users, while deriving greater overall value from everyday transactional decisions.

With SPSS Analytical Decision Management for Linux on System z, your organization can:

Optimize transactional decisions that add up to a significant impact on the bottom line.
Deliver recommended actions at the point of impact using advanced techniques and algorithms.
Perform data mining and text analysis, based on predictive models, local rules, decision logic, scoring and optimization.
Make real-time, adaptive decisions to help business users continuously improve models and maximize every outcome.

Optimize transactional decisions

Develop new and reusable predictive analytic assets by leveraging existing structured and unstructured data.
Measure trade-offs at the execution level by solving very large optimization problems comprised of millions of constraints and variables.
Learn from previous decisions through repositories and feedback mechanisms.

Deliver recommended actions at the point of impact

Use the strengths and analytics of existing databases, including IBM Cognos® Business Intelligence, IBM DB2®, IBM Netezza, Informix, Microsoft SQL Server, Sun my SQL, Oracle and Teradata.
Automatically prepare, cleanse and transform data – in a single step – for the best possible analytics.
Use an array of pre-built algorithms to mine millions of data records and create accurate predictive models.
Automate and apply scoring, and deploy and distribute scores across the enterprise.
Build enterprise-wide business rules within IBM Operational Decision Management to improve compliance, governance and speed.

Make real-time, adaptive decisions

Combine new attitudinal, behavioral and transactional data with historical data to score a decision and receive the results in real time.
Adapt insights through feedback mechanisms and predictive analytics.
Refine the decision-making process by running multiple simulations and comparing the outcomes of different approaches (for example, by modifying variables such as cost and profit margin).
Deploy the best-performing models.

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