Find the value in your data, no matter the size. Multiple users can explore and visualize data, then interactively create and refine descriptive and predictive models. Distributed, in-memory processing slashes model development time so you can run complex analytic computations – and get precise results – in minutes.
Stay agile with in-memory computing.
An in-memory engine performs complex analytic computations fast. Modelers can test new ideas quickly, compare different modeling techniques and refine models on the fly to get the best results – using data volumes never before possible.
Run more models faster. With precision.
Our multicore processing environment reduces model run times to minutes. You can build models to target specific groups or segments, and run numerous scenarios simultaneously. Ask more what-if questions, and get fast answers. Refined models produce better results.
Beat the competition with savvy insights.
Quickly surface insights hidden in vast data stores. Uncover opportunities your competitors miss. Find new ways to grow revenue. Powerful, predictive analytics and visual data discovery let business analysts and statisticians do more with data than ever before.
Boost your analytical productivity.
Multiple users can customize models quickly – and interactively. Add or change variables, remove outliers, etc., and instantly see the effect on model outcomes. Now it's easy to find out which model provides the most predictive power – and get more value from your big data analytics.
- Interactive data exploration and modeling environment. Use SAS Visual Analytics to quickly identify predictive drivers and interactively discover outliers across multiple variables. Then create powerful descriptive and predictive models with a simple drag-and-drop interface.
- Descriptive modeling. Visually explore and evaluate segments for further analysis using k-means clustering, scatter plots and detailed summary statistics.
- Predictive modeling. Build predictive models using a variety of techniques – linear regression, generalized linear modeling, logistic regression, classification trees, etc.
- 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 on diverse data sets, including Hadoop. Instantly see the impact of changes. And there's no need to shuffle data or write data to disk.
- Model comparison and assessment. Generate model comparison summaries (e.g., lift charts, ROC charts, concordance statistics and misclassification tables) on one or more models.
- Model scoring. Generate SAS DATA step code, and apply it to new data.
- Data visualization and reporting via SAS Visual Analytics. Design and distribute BI reports and dashboards. Explore relevant data through interactive data discovery. And provide easy-to-use, self-service analytics to more users.
- Platform support. Supports Hadoop distributed file system (Cloudera or Hortonworks distributions), as well as Teradata, Greenplum (Pivotal), SAP HANA and Oracle databases.