Tuesday, May 29, 2012

Synergies and value proposition between IBM SPSS and Zementis 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 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.

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

Thursday, May 17, 2012

IBM/Zementis Webcast: Predictive Analytics on the IBM SmartCloud, May 24, 10 am PST

You are invited to attend the IBM/Zementis webcast entitled "Have you fully tapped the business value of predictive analytics?"

WHEN: Thursday, May 24, 2012, 10:00 am PT / 1:00 pm ET

Free registration - Click HERE

In this webinar, we showcase the technical capabilities of the Zementis ADAPA Decision Engine on the IBM SmartCloud, a solution which combines open standards and cloud computing to reduce complexity and accelerate time-to-market for predictive analytics in any industry and for any business application.

Please join Ed Bottini, IBM's Global SmartCloud Services Ecosystem Leader, and Michael Zeller, Zementis CEO, for this joint IBM/Zementis webinar.


To extract value and insight from "Big Data", leading organizations increasingly leverage predictive analytics. By using statistical techniques that uncover important patterns present in historical data, companies are able to predict the future.  In doing so, they become more precise, consistent and automated in everyday business decisions.

To fully benefit from Big Data solutions, however, an infrastructure must be in place for the agile deployment and execution of predictive analytics. The open Predictive Model Markup Language (PMML) standard allows for the instantaneous delivery of solutions from scientific development to operational IT environments that support critical business processes. Eliminating costly up-front investments, cloud computing and Software-as-a-Service (SaaS) now offer the power and flexibility necessary to bring them into reach for many organizations.

Friday, May 4, 2012

The Netflix Prize, Occam's Razor and PMML

I just finished reading an excellent posting on the Netflix Tech Blog by Xavier Amatriain and Justin Basilico. Entitled "Netflix Recommendations: Beyond the 5 stars (Part 1)", it gives a very nice account of the actual application of the 107 algorithms submitted as the winner solution to the Netflix prize.

I recall reading about the prize when the winner team was finally announced. I basically asked myself: "How on earth will Netflix implement all these algorithms and put them to work?" The obvious answer was "they won't". Too complicated, too time consuming.

As pointed out by Xavier and Justin, at the end, two algorithms (SVD and Restricted Boltzmann Machine) were selected, based on performance, and eventually made into production where they are busy making recommendations.
In their posting, Xavier and Justin put it clearly:

"We evaluated some of the new methods offline but the additional accuracy gains that we measured did not seem to justify the engineering effort needed to bring them into a production environment."

On top of that, they go on to mention that the focus of the recommendations algorithm has changed as business has shifted to on-line streaming. With that, the input data has changed considerably as well as customer behavior. Basically, the proposed solution (an intricate combination of 107 algorithms) no longer applies since the rules of the game have changed. That's the very nature of business.

You may be wondering about Occam's razor at this point. How does it relate to the Netflix Prize? Simple, the razor "asserts that one should proceed to simpler theories until simplicity can be traded for greater explanatory power. The simplest available theory need not be the most accurate" (Wikipedia). Obviously, the razor does not apply to a contest in which participants are battling each other for the prize. The more accurate, the better, right?

But, how about real life? We know that every predictive solution has a cost. As made clear by Xavier and Justin, in the Neflix case, they take time to implement and deploy. Can we somehow then create a data mining contest in which Occam's razor is taken into account? Is that possible? I believe the answer is "yes" and it involves the use of open standards. For example, if the proposed solutions were to be delivered in PMML (the Predictive Model Markup Language) format, they could be put to work immediately. Also, since PMML is supported by all the top data mining tools, re-creating the same solution using existing software would not take an arm and a leg. And, whenever represented in PMML, it could be easily understood. Given that PMML is XML-based, it contains a verbose but accurate representation of all model details. Whenever a solution is represented in PMML, there is no need for an extra document to explain it. The same file can be used for explaining the solution and for deploying it.

Finally, given the agility introduced by the standard itself, predictive solutions can be easily adjusted to new business requirements. In fact, they can even be used to drive new business opportunities. And, that's a prize worth winning.

Welcome to the World of Predictive Analytics!

© Predictive Analytics by Zementis, Inc. - All Rights Reserved.

Copyright © 2009 Zementis Incorporated. All rights reserved.

Privacy - Terms Of Use - Contact Us