Develop predictive models using complete data, not just a subset, to get accurate insights in minutes or seconds. Use sophisticated analytics. Perform frequent modeling iterations. And gain competitive advantage.
Seize new opportunities.
Blazingly fast performance shrinks analytical processing time and produces rapid insights. Finer and more accurate results enable you to confidently make well-informed decisions. Derive new revenue opportunities. Gain significant business value.
Answer your most difficult questions.
Use the best, most sophisticated analytical techniques to tackle difficult or unresolved problems and test new ideas. Combined with high-performance text mining, you can uncover relationships in text data and gain even more predictive power.
Bring precision to important decisions.
Improve the accuracy of your data mining results and make more precise decisions. How? Use sophisticated techniques against all data – both structured and unstructured – instead of just a subset. Handle more variables. Perform modeling iterations more frequently.
Optimize model performance.
Analytic professionals can take advantage of the massive parallel processing, in-memory environment to solve difficult business questions without infrastructure limitations. Quickly build and run complex models. And easily retrain models using different parameters.
- High-performance neural networks.
- High-performance random forests.
- High-performance variable reduction.
- High-performance Bayesian networks.
- High-performance clustering.
- High-performance data summarization.
- High-performance correlation.
- High-performance binning.
- High-performance imputation.
- High-performance support vector machine.
- Available for Greenplum, Teradata and Oracle Exadata, as well as Hadoop (Cloudera and Hortonworks distributions).
Analysts are really excited as they can now see what’s possible. They can run multiple nodes not only faster but in parallel, which will help us to increase productivity and deliver more accurate, collaborative outcomes.