Data analytics and BI software specialist SAS has announced the launch of a new set of tools that promises to bridge the gap between statisticians and business managers at its annual analytics conference in Copenhagen on Thursday.
The toolset, known as Rapid Predictive Modeler (RPM), has been available to customers since 17 August but the company wanted to launch it with a bang at its yearly analytics event.
RPM allows non-statisticians to generate predictive business models in a few simple steps, thereby freeing up resources and improving the efficiency of creating predictive models, according to the company.
"It is targeted at business users or subject matter experts, to allow them to do data mining. With Rapid Predictive Modeler the goal is to make analytics more mainstream, more self-sufficient," Tapan Patel, global marketing manager SAS, told ZDNet UK.
RPM is, essentially, a new task within Enterprise Miner and comes packaged as part of SAS Enterprise Miner 6.2 — running on the data mining platform behind the scenes.
Pricing for the new functionality is included with the licensing of EM 6.2, which varies depending on the installation hardware.
"So what happens is, somebody has created a base table of analytical material and once the table is loaded in Excel or Enterprise Guide [another SAS product] they can select the table, the input variables, and then the models get generated from there — and they get the meaningful results out of it. They get different charts, you can see for example which variables are important and what to do about them. It allows them to take an action, that's where the interplay between the business analyst and the decision makers comes in," added Tapan.
However, he clarifies that, "It's not about removing the role of the statistician, it's about improving collaboration, putting the power of predictive analytics in the hands of the business analyst – they're closer to the decision making cycle and their area of expertise [than statisticians]".
Customers can modify the modeller themselves, adding their own code and still retain the full automated functionality of RPM's built-in tools.
In developing the new task, SAS worked with a select group of its existing customers using an "inter-disciplinary approach," explained Wayne Thompson, analytics product manager, SAS.
"We really followed a rigid, pragmatic marketing plan where we worked with customer data, so we had good data to build these models on – again, these are modelling templates, they're supposed to be broadly applicable, you can tweak them afterwards but they should work pretty well out of the box. We also worked with our education group because they have a lot of expertise with statistics — and the tech support people," he added.