Tuesday, May 29, 2012

Synergies and value proposition between IBM SPSS and ADAPA

The ADAPA Decision Engine provides additional value to all your predictive assets. It is complimentary to IBM SPSS Modeler and IBM SPSS Statistics, since it extends these modeling environments into the IT operational domain.

ADAPA is compatible with Modeler and Statistics through PMML, the Predictive Model Markup Language, which is the de facto standard to represent predictive models. PMML allows for models to be developed in one application and deployed on another, as long as both are PMML-compliant.

Immediate benefits of using ADAPA


Once a model built in any of the IBM SPSS tools is saved as a PMML file, it can be directly uploaded in ADAPA. With ADAPA, you can:
  • Execute your models independently of the IBM SPSS model development tool
  • Overcome any speed limitations
  • Dramatically lower your infrastructure cost
  • Tap into all the advantages of cloud computing with ADAPA on the Cloud (IBM SmartCloud or Amazon EC2)
  • Produce scores in real-time (using Web Services or Java API), on-demand, or batch-mode
  • Execute your models directly from Excel, by using the ADAPA Add-in for Excel
  • Benefit from using other PMML-compliant model development tools such as R, KNIME, or SAS
  • Deploy your models in minutes, not months (no need for recoding models into production)
  • Manage models via Web Services or a Web console
  • Upload one or many models into ADAPA at once
  • Use rules to implement model segmentation
  • Benefit from the seamless integration of business rules and predictive models

IBM SPSS PMML support


IBM SPSS offers vast support for PMML through IBM SPSS Modeler (formerly known as Clementine) and Statistics. Both systems allow users to export a multitude of models in PMML (for details, click HERE). IBM products such as DB2 Intelligent Miner and ILOG JRules also offer support for PMML.

A common industry standard


PMML allows for the de-coupling of two very important modeling phases: development and operational deployment. With PMML, scientists can focus on data analysis and model building using the best of breed model development tools, whereas operational deployment and actual use of the model is made extremely easy and simple with ADAPA.

ADAPA Solutions For

For example, if a data mining scientist develops a decision tree model using IBM SPSS Modeler, all he/she needs to do to effectively deploy his/her model operationally is to save it as a PMML file and uploaded it in ADAPA. Once in ADAPA, the decision tree model is available for all to use, directly by business users and applications. It may be used by a business user directly from within Excel to score customers for a marketing campaign.

By doing that, PMML allows for the model development environment to be used just for that, model development. Scoring, real-time or batch-mode from anywhere and at anytime, is handled by ADAPA.

Friday, May 25, 2012

Predictive Analytics at the Speed of Business

Decision Management Solutions/Zementis Webinar (presented, May 3rd, 2012)

Organizations are looking to maximize the value of their analytics investment. They need to accelerate the deployment process, reduce costs and get the analytic insight where they need it, when they need it. Increasingly organizations must deploy and manage many predictive models, use those models in real-time and integrate predictive analytics into a wide range of operational systems – in the cloud, on-premise, for Hadoop and in-database.

In this webinar you will learn how Decision Management and ADAPA – a proven approach and real-time infrastructure – transform passive models into operational success. This webinar is jointly presented by James Taylor, CEO of Decision Management Solutions and Dr. Alex Guazzelli, Vice President of Analytics at Zementis.

Presentation (on YouTube):





Demo (on YouTube):






Presentation and demo cover:
  • The current challenges in getting a return on your predictive analytic investment
  • The role of decision management in applying analytics when and where they are needed
  • The roles of predictive analytics and business rules technologies in decision management
  • How real-time infrastructure and rapid deployment maximizes analytic value
  • The importance of continuous monitoring and improvement in delivering ongoing results
Decision Management Solutions & Zementis are leaders in Decision Management, providing consulting services in Decision Management, business rules and predictive analytics as well as a flexible platform for deploying predictive analytics on premise, in the cloud, for Hadoop or in-database.

Download slides
 

Monday, May 7, 2012

ADAPA Demo: Seamless Integration of Predictive Analytics and Business Rules

The Zementis ADAPA Decision Engine offers the perfect platform for moving complex solutions, which may include a combination of predictive analytics and business rules, from the development to the operational environment -- on the cloud, on-site, for Hadoop and in-database. 


In this demo, Dr. Alex Guazzelli, VP of Analytics at Zementis, shows a pre-qualification app that uses predictive models and rules to analyze the risk of mortgage default on loan applications. An application is accepted or referred for a variety of loan products depending on its perceived risk. ADAPA is the engine driving this application in the back-end.

Once logged in ADAPA, Dr. Guazzelli uses the ADAPA Web Console to download the mortgage solution files which are used throughout the demo. Predictive models expressed in PMML format are uploaded and verified in ADAPA along with rulesets expressed in tabular format. The ADAPA Web Console is used for managing predictive models, rulesets, and resource files as well as for batch-scoring. Real-time scoring is obtained via web-services or the Java API.

Finally, Dr. Guazzelli shows how the ADAPA Add-in for Excel is used to score data directly from within Excel. This part of the demo features the scoring of loan and tax data as well as the visualization of results via dashboards.





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