Open Data Group

Some FAQ on Predictive Analytics

rlg — Mon, 12 Sep 2011 14:51:08 +0000

Robert Grossman, a Partner at Open Data Group, was interviewed about predictive analytics, data mining, and related topics recently. You can find the video interview here.

He also updated a FAQ about predictive analytics and data mining that you can here

PMML Workshop

rlg — Mon, 22 Aug 2011 15:09:09 +0000

A Workshop on the Predictive Model Markup Language (PMML) took place on August 21, 2011 at the KDD 2011 Conference in San Diego.

The essential idea of PMML is that a predictive model, and more generally a statistical or data mining model, should not be thought of as code, but rather abstracted and described as metadata about the underlying data that it models. The PMML standard specifies an XML format for this metatadata.

The reason that this point of view is important is so that one application (the model producer) can produce the model, while another application (the model consumer) can use the model for scoring data. The model consumer can be integrated into production and operational systems and models can then be updated simply by reading new PMML files.

With this approach predictive models in operational systems can be updated quickly and easily. In contrast, when predictive models are viewed as code and new code is added to operational systems, a careful QA process is required before any new code can be deployed.

The upcoming version of PMML (PMML version 4.1, which should be released in the Fall of 2011) supports multiple models. This PMML feature was championed by Open Data Group over the past few years based upon its experience building predictive models over big data.

As the amount of data increases, building predictive models using multiple models (segmented models, hierarchical models, and related techniques) is absolutely critical. For big data, there is really no alternative.

I expect that with the explosion of big data and big data analytics, and with PMML’s support for multiple models, that PMML will begin to be an essential component of any analytic infrastructure that supports big data.

Specializing in Building Predictive Models Over Big Data

opendatagroup — Sun, 08 May 2011 22:53:38 +0000

Open Data provides management consulting, outsourced analytic services, and analytic staffing so that companies and organizations can analyze data and build analytic models that increase revenues, decrease costs, reduce risk, and improve business operations.

Open Data began operations in 2001 and has built predictive models for companies for over ten years.

Open Data specializes in building predictive models over large data and is one of the pioneers using technologies such as Hadoop and NoSQL databases so that companies can build predictive models efficiently over all of their data.

Open Data has also participated in the development of the Predictive Model Markup Language (PMML) so that companies can more quickly deploy analytic models into their operational systems.

For the past several years, Open Data has helped companies build build predictive models and score data in real time using elastic clouds, such as provided with Amazon’s AWS.