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First Look: Rapid Insight 3.0 Update


I last got an update from Rapid Insight in 2014 and caught up again with them recently to discuss their 3.0 release. Rapid Insight was founded in 2003 and has over 200 client sites across education, healthcare and other companies. The product set is focused on predictive analytics, ad-hoc analysis and self-service data preparation.

Rapid Insight automates the process of building predictive models, making it easy to explore data and to quickly build models. The tool is designed to deliver automation in a flexible way, allowing users to automate as much or as little of the process as they prefer. The resulting models are tied back to the original data preparation processes so that they can easily be implemented and re-developed in the future.

They launched Rapid Insight Analytics 3.0 this year. The 3.0 product supports in-database and in-hadoop optimization using SQL pushback and has been re-written to exploit multicore processing. The products remains Windows clients with a wide range of access to data on any server or environment. The core user interface models the project workflow with data access, data preparation and modeling steps laid out. New steps can be added from a palette and each can be configured as you would expect. Multiple streams can be defined and R can easily be integrated. New in 3.0 is the ability to define sub routines so that pieces of projects can be reused. Jobs can be scheduled and managed as you would expect.

The Analytics 3.0 environment begins in a new data viewing environment that consumes the data processed with the flow. This automatically classifies the variables, identifies relationships between them and allows a wide range of (updated and refreshed) visualizations of this data. A report can be built incrementally as useful visualizations are identified. These reports can now be immediately shared with others via the cloud. 3.0 allows new clustering approaches and decision trees to be applied to the data.

Models can still be developed automatically from the data and the performance of this has been significantly improved in 3.0. The model can be built completely automated or the user can pick specific variables and techniques or take a hybrid approach, specifying some variables and letting the tool pick the others. The new environment makes it easy to transform and filter data in situ, new model analyses are available (and can be added to the reporting environment) and more information about distributions etc is available instantly.

All the jobs are invokable from a command line or other code environment and can execute for any number of records. Models can also be exported to PMML and deployed to a deployment environment.

More information is available on Rapid Insight here.


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