Descriptive and predictive modeling provide insights that drive better decision making. Now you can streamline the data mining process to develop models quickly. Understand key relationships. And find the patterns that matter most.
Build better models with better tools.
Dramatically shorten model development time for your data miners and statisticians. An interactive, self-documenting process flow diagram environment efficiently maps the entire data mining process to produce the best results. And it has more predictive modeling techniques than any other commercial data mining package. Why not use the best?
Empower business users.
Business users and subject-matter experts with limited statistical skills can generate their own models using SAS Rapid Predictive Modeler. An easy-to-use GUI steps them through a workflow of data mining tasks. Analytics results are displayed in easy-to-understand charts that provide the insights needed for better decision making.
Improve prediction accuracy. Share reliable results.
Create better-performing models using innovative algorithms and industry-specific methods. Verify results with visual assessment and validation metrics. Easily compare predictions and assessment statistics from models built with different approaches by displaying them side by side. The resulting diagrams also serve as self-documenting templates that you can update or apply to new problems without starting over.
Automate model deployment and scoring.
Scoring code is automatically generated for all stages of model development, which eliminates potentially costly errors stemming from manually rewriting and converting code. You can embed the scoring code in your business processes. Or deploy it in real-time or batch environments within SAS, on the web and directly into relational databases. This saves time and produces more accurate results.
Combine the power of SAS®9 and SAS® Viya™.
Take full advantage of the two most powerful analytics platforms in a single user environment using the SAS Viya Code node and SAS/CONNECT®. The SAS Viya Code node enables SAS Enterprise Miner users to call powerful SAS Viya actions within a SAS Enterprise Miner process flow. By incorporating SAS Viya models into their process flows, data scientists can compare or combine SAS Viya models and SAS®9 models – giving them the best of both worlds.
- Easy-to-use GUI and batch processing. Build more – and better – models faster.
- Sophisticated data preparation, summarization and exploration. Address missing values, filter outliers, develop segmentation rules, etc., with a powerful set of interactive data preparation tools .
- Advanced predictive and descriptive modeling. Gain superior analytical depth with a suite of statistical, data mining and machine-learning algorithms.
- Open source integration with R. Perform data transformation and exploration, as well as train and score supervised and unsupervised models in R.
- High-performance capabilities. Boost performance with an included set of high-performance data mining nodes.
- Fast, easy and self-sufficient way for business users to generate models. SAS Rapid Predictive Modeler automatically steps nontechnical users through a workflow of data mining tasks.
- Model comparisons, reporting and management. Quickly identify which models produce the best lift and overall ROI with easy-to-use assessment features.
- Automated scoring. automatically generate score code in SAS, C, Java and PMML, then deploy the scoring code in a variety of real-time or batch environments within SAS, on the web, or directly in relational databases or Hadoop.
- Ability to call SAS Viya actions within a process flow. Use the new SAS Viya Code node to submit and execute SAS Viya code directly in a SAS Enterprise Miner process flow.
- Scalable processing. Scale from a single-user system to very large enterprise solutions with the Java client and SAS server architecture.
For more information, read the SAS Enterprise Miner fact sheet.
Learn about the modern applications of machine learning.
Learn why Gartner names SAS a Leader in the the Magic Quadrant for Data Science Platforms.