RapidMiner takes predictive analytics out of the hands of data scientists by combining a recommendation engine with collective intelligence. “We’re taking those traditional data science tasks,” explained RapidMiner CEO and Founder Dr. Ingo Mierswa. He and RapidMiner COO and President Michele Chambers sat down with John Furrier inside theCUBE at the BigDataSV conference last week to shine a light on RapidMiner and its goals going forward.

The RapidMiner Product

Backed by funding from top firms in the United States and Europe, RapidMiner provides analytics that takes a team-approach. “We facilitate collaboration,” said Chambers, that “accelerates the time to value for organizations.” In addition to guiding clients through self-service analytics, Chambers stated “we provide accelerators.” She explained that each are “pre-defined use cases like customer churn, direct marketing, sentiment analysis, and predictive asset maintenance.” The accelerators, she continued, provide a “60 to 80 percent fit that [clients] can customize” to fit their specific needs.

RapidMiner can handle all types of data, “including binary, text, images, we run in-memory, we run in Hadoop.” Chambers said. This functionality makes RapidMiner an effective tool; it also offers push-down in-stream processing, elastic compute in Amazon Web Services, inc.’s AWS product and in standard databases. “Whatever your environment is,” she said, “we work inside that kind of context.”

This Boston-based company provides their products using a “business source model,” said Chambers. They offer a commercial version that starts out with a freemium model. Once a new version has come out, the previous release is open sourced. In the commercial package, RapidMiner clients have the option to “extend [their] package” through accelerators, but, Chambers added, “the community also contributes back in the form of accelerators as well.”

When Recommendation Engine Meets Collective Intelligence

In fact, RapidMiner uses a “community based model” to offer insights, new techniques, and recommendations as clients use the platform, said Mierswa. These suggestions are gleaned from the 250,000 RapidMiner users that “give something back [to RapidMiner] other than just money.” Machine learning based on this customer intelligence has received a “warm reception from business analysts,” added Chambers, because it allows them to “immediately add value to a line of business.”

Focusing on the Ecosystem in 2015

In the coming year, the RapidMiner CEO and COO said they plan to use their most recent round of funding to continue “investing into the products” in order to maintain their lead in the marketplace. He added that they intend to begin “building out our ecosystem.” This focus on the ecosystem will benefit both the industry and RapidMiner itself, remarked Mierswa, because “predictive analytics is stronger when it’s embedded.” Chambers explained that in order to accomplish their goals, RapidMiner is beginning “a very aggressive community plan this year,” which, she pointed out, is one of their first in the United States.

Watch the full interview below, and be sure to check out more of SiliconANGLE and theCUBE’s coverage of BigDataSV 2015.