KXEN fills the need for BA speed
Analytic Framework 3.0.2 rips through large data sets, but its ease of use could use a boostFollow @infoworld
Despite the fact that BI gives data-mining pros the opportunity to answer strategic questions, it’s not a very interactive or iterative process. That’s the domain of BA (business analytics).
But even BA tools require you to know the right questions in advance and to be able to see patterns in presented information so you can ask the right follow-up questions.
One of the factors that limits analytics software is that it tends to be aimed either at a large swath of domain experts or at a small cadre of statistics gearheads. This leaves a gap that BA vendors are trying to bridge by building tools that have sophisticated analytical capabilities wrapped into interfaces that people without professional statistical knowledge can steer skillfully.
KXEN’s Analytic Framework 3.0.2 is a valiant effort at filling that gap. It supports iterative, interactive data exploration, bringing together data mining and BA with a high-powered engine that rips through large data sets surprisingly fast.
Analytic Framework will kick some serious grass in analyzing targeted applications and big data stores, but its user interface for midrange analytics professionals has room for improvement. Nevertheless, Analytic Framework’s significant data-crunching capabilities and speed can crush BA challenges that would swallow most analytics software as easily as a petit four.
Analytic Framework’s mojo resides in its foundation: the statistical learning theory of mathematician Vladimir Vapnik and the senior mathematicians on KXEN’s board. The product’s architecture is built on a set of eight analysis modules — ranging from data cleaning to time-series data analysis — accessible through an API or the Modeling Assistant wizard.
The Modeling Assistant guides you through problem specification, including defining a sampling strategy, loading data from standard file formats, and specifying one or more target variables (such as “profitable customer” or “failed circuit board”) and variables to omit from the run. The software then uses those settings to train and build a model and report your results.
Although it’s hard to spin out of control with the wizard, the system has many prescribed constraints about how imported data must look. It’s not a truly muddy interface, but users will need serious understanding of what this powerhouse uses for fuel, which requires rigorous user training.
Pluses and Minuses
The two main analyses modules are K2R (Robust Regression) and K2S (Smart Segmenter). K2R creates predictive models, devouring historical data to calculate future probabilities.
K2S creates cluster analyses for market-segmentation efforts — critical for classic market analysis and partitioning the total universe of customers into trait-sharing groups. Many tools expect the analyst to dictate the definition of a cluster, but K2S defines a model for the analyst, saving time and effort. Instead of guessing who your prime clusters are, the software maps rows of data into clusters for fast, true analysis.