Companies will combine their data mining and predictive analytics tools.
Angoss Software Corp., a provider of predictive analytics tools, and Datawatch, a provider of data mining solutions, announced on March 1 a partnership that will combine their solutions into one platform.
In a joint release, Datawatch said the new platform would allow customers to use the Angoss tools to gain predictive analytics through all facets of the data mining process, including exploration, segmentation, evaluation and deployment.
“Our partnership with Angoss provides the best of both worlds: access to data sources that provide the most analytic value and the ability to analytically synthesize it through advanced predictive tools,” said Dan Potter, chief marketing officer at Datawatch, in the announcement. “The result is higher predictive and exploratory power for greater return on investment. By analyzing not only the right data, but also all of the data, decision makers will get a better understanding of the reality of their business.”
Datawatch’s managed analytics platform, Monarch, specializes in data acquisition, preparation as well as real-time visualization. It will be merged with Angoss’s Decision Trees solution, according to a release about the partnership. Monarch will acquire and prepare data from almost any data source, including web pages, log files and PDF documents, and Decision Trees will generate insights from it using predictive analytics. The process is automated, eliminating the need for code to be written, and includes a variety of modeling techniques. For those customers who want to write custom code for their projects, Angoss’s solution includes built-in functionality for Python, R and SAS.
Want more? Check out our most-read content:
First Look: Impetus
Finding the Right Recipe for Complex Event Recognition
White Paper: How to ‘Future-Proof’ a Streaming Analytics Platform
E-Book: How to Move to a Fast Data Architecture
The Value of Bringing Analytics to the Edge
Earthquake Early Warning System: There’s an App for That
Liked this article? Share it with your colleagues!