Apply advanced statistics to huge volumes of data. Quickly retrain many models. Use all available processing power. And get the answers you need to make the best decisions.
Chart the best course of action.
Not all in-memory approaches are created equal. Now you can apply world-class statistical techniques to big data for more accurate results. Faster than ever. From summarization and exploration to model building and scoring results. It's not just about using query, reporting and descriptive statistics within an in-memory environment. It's about predicting and prescribing the best course of action so you stay ahead of your competition.
Address your most difficult problems.
Do your analytical professionals feel they have to compromise by using suboptimal analysis techniques? Or that there's not enough time to experiment with different models and data types? Those days are over. Now they can quickly run iterative models on big data, experiment with sophisticated algorithms and solve even the most complex problems – all within ever-shrinking windows of opportunity.
Don't be stymied by big data.
Don't let big data get the best of you. With our solution, you can use huge volumes of data in your statistical analyses. And run analytic processes at blazingly fast speeds. Repeatedly fine-tune models to produce more accurate results. And realize significant value from your big data.
Process statistical models in record time.
Take advantage of the massive parallel processing (MPP) in-memory computing environment so that complete data – not just a subset – can be readily analyzed and evaluated. Hundreds or thousands of variables? No problem. You can process all the data you need to without infrastructure limitations.
- High-performance logistic, linear and nonlinear regression models.
- High-performance mixed linear models.
- High-performance generalized linear modeling.
- High-performance decision trees.
- High-performance partial least squares.
- High-performance quantile regression analysis.
- High-performance finite mixture models.
- High-performance principal components analysis.
- High-performance canonical discriminant analysis.
- High-performance data summarization.
- High-performance data mining database.
- High-performance correlation.
- High-performance sampling.
- High-performance binning.
- High-performance imputation.