Data mining with SAS® Enterprise MinerTM
Unearth valuable insight and gain profitable data mining results with less time and effort
SAS Enterprise Miner streamlines the data mining process to create highly accurate predictive and descriptive models based on analysis of vast amounts of data from across the enterprise. Forward-thinking organizations today are using SAS data mining software to detect fraud, anticipate resource demands, increase acquisitions and curb customer attrition.
- Support the entire data mining process.
- Build more models faster.
- Enhance accuracy of predictions and easily surface reliable business information.
- Multiple interfaces.
- Scalable processing.
- Data preparation, summarization and exploration.
- Advanced predictive and descriptive modeling.
- Business-based model comparisons, reporting and management.
- Automated scoring process.
- Open, extensible design.
Our profitability around marketing interventions programs is much higher because of the precision of understanding that SAS provides.
— David Norton,
Senior Vice President of Relationship Marketing,
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How SAS Is Different
- Data access, management and cleansing are seamlessly integrated, making it easier to prepare data for analysis.
- With multithreaded algorithms and support for multiprocessing and grid computing, execution time is reduced and hardware resources are used more efficiently.
- Smart defaults allow business users to produce models quickly, while advanced statisticians have the option to tweak details and embed their own algorithms into their model flows.
- The rich Java client interface enables fast, maintenance-free distribution throughout large organizations, and data mining projects can be shared among analysts across different units and regions.
- Our advanced analytic algorithms are organized under the core tasks that are performed in any successful data mining endeavor: Sampling, Exploration, Modification, Modeling and Assessment (SEMMA). In each step, you are guided through an array of actions to perform as the data mining project develops.
- Robust variable selection and data modification tools improve the quality of your data, which leads to better modeling and more reliable results.
- Scoring code is delivered in SAS, C, Java and PMML for scoring in batch and real-time in both SAS and non-SAS environments.