Find the value in your data – big or small – and use it to your advantage. Now you can tackle your most complex challenges, and get precise answers – instantaneously. Only SAS combines industry-leading analytics with a powerful in-memory engine and an intuitive interface, so you can build predictive models faster than you ever imagined.
Beat the competition with precise insights.
Discover, analyze and evaluate new opportunities so you can quickly surface insights hidden in vast stores of data. We've combined powerful, predictive analytics with visual data exploration capabilities in a single, interactive environment. The result? Business analysts and statisticians can do more with data than ever before.
Boost data scientist and statistician productivity.
Multiple users can quickly and interactively customize their models. Add or change variables, remove outliers, etc., and instantaneously see how those changes affect model outcomes. Which model provides the most predictive power? It’s now easy to find out. So you can get more value from your big data analytics and your employees.
Develop and run more models faster.
How long does it take to run your models? Hours? Our multicore processing environment reduces that to minutes. Build models to target specific groups or segments, and run numerous scenarios simultaneously. Analytical professionals can ask more what-if questions and get fast answers. Refined models produce better results.
Stay agile with in-memory computing.
SAS Visual Statistics performs complex analytic computations using an in-memory engine. Modelers can quickly test new ideas, try different sophisticated modeling techniques and refine models on the fly – all using data volumes never before possible.
- Data visualization and exploration. Quickly identify predictive drivers among thousands of explanatory variables, and interactively discover outliers and data discrepancies through integration with SAS Visual Analytics.
- Descriptive modeling. Visually explore and evaluate segments for further analysis using k-means clustering.
- Predictive modeling. Build predictive models using techniques like linear regression, generalized linear modeling, logistic regression and classification trees.
- Dynamic group-by processing. Concurrently build models and process results for each group or segment without having to sort or index data each time.
- In-memory analytical processing. Build models faster. There's no need to write data to disk or perform data shuffling, and you can instantly see the impact of changes (e.g., adding new variables or removing outliers).
- Model comparison and assessment. Generate model comparisons, including lift charts and ROC charts for one or more models.
- Model scoring. Generate SAS DATA step code, and apply it to new data.
- Platform support. Supports Hadoop distributed file system (Cloudera or Hortonworks distributions), as well as Teradata and Pivotal databases.