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WebFOCUS RStat upgrade released

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Information Builders has announced the release of WebFOCUS RStat 1.2, an integrated BI platform that enables employees at all levels to create predictive applications.
“As RStat has become a general-purpose data mining and statistical tool that significantly reduces the IT complexity for non-statistically trained users, it appeals to a broad group of business analysts and operational employees alike,” says Johan Jurd, MD of InfoBuild, an Information Builders representative in SA.
By enabling more employees to take advantage of a predictive solution and make better decisions, RStat provides greater business benefits at all levels of an organisation.
“RStat 1.2 lowers the cost of predictive analytics. By fully integrating RStat with WebFOCUS, customers benefit from significantly lower costs of mining projects simply by providing efficient access to data. Ninety percent of costs in statistical and mining projects are in data access and manipulation – two activities that are easier with BI tools,” he says.
Additionally, the integration on the reporting server lowers deployment costs by minimising the need for additional hardware. Instead of having a statistical server and a BI server, RStat 1.2 enables customers to conduct both BI and predictive modeling from a single server.
Having this type of integrated environment makes the deployment of a predictive scoring solution much simpler and faster than older statistical systems. Information Builders is providing RStat free for modeling and analysis services as part of its Developer Studio product.
WebFOCUS RStat 1.2 includes several new features such as:
* Survival analysis – included as an additional statistical modeling technique and scoring routine with both COX regression and Kaplan-Mayer. With RStat 1.2, Information Builders is the first to integrate scoring routines for survival models.
Survival analysis is widely used in the pharmaceutical and healthcare industries to measure outcomes. In government, it’s used for matching children with foster parents and in the manufacturing and engineering industries, it is used to model machine failure, maintenance, and replacement of parts.
* Library of scoring routines – expanded to advanced models such as Neural Networks. This widens the scope of models that can be deployed directly with WebFOCUS on any platform.
* Charting capabilities – expanded with new charts that are relevant for survival analysis. Survival charts allow people to quickly assess whether there are significant differences between segments across an organisation or extended network.
For example, with a survival chart, someone can compare how long children stay in foster care from one country to another. If differences are shown, a broken process is indicated and an agency can quickly correct that process based on best practices.
* Testing – with additional testing capabilities, RStat becomes a tool for both data mining as well as for standard statistical analysis. Traditional hypothesis testing methods, such as T and F tests, have also been added.
Integrating RStat into the Developer Studio product enables new and existing customers to quickly begin taking advantage of the power of predictive analytics.
Techniques for predictive analytics vary from statistics and data mining to game theory. Manually analysing current and historical data to make predictions about future events is nearly an impossible task for any person or team.
By integrating RStat software, the widely used open-source statistical library product, with Developer Studio, Information Builders enables data miners and BI developers to quickly and easily work together on the same platform to access and analyse data and develop predictive models as needed across the organisation.
WebFOCUS RStat to pull data out of transaction systems, reformat it, use the analytic tools to develop a predictive model, and then run batch analysis, resulting in more efficient and smarter decision-making.
“By analysing available data through WebFOCUS RStat, managers can get a different, fuller picture of their business and how it is likely to behave. Using predictive BI, they were able to better assess risk,” Jurd concludes.