Predictive Analytics and Data Mining

Every organization accumulates huge volumes of data from a variety of sources on a daily basis. Data mining is an iterative process of creating predictive and descriptive models, by uncovering previously unknown trends and patterns in vast amounts of data from across the enterprise, in order to support decision making. Text mining applies the same analysis techniques to text-based documents. The knowledge gleaned from data and text mining can be used to fuel strategic decision making.

Components of Predictive Analytics and Data Mining

• Exploratory Data Analysis – Get dynamic visualization, advanced statistical techniques and core data mining capabilities to quickly identify relationships and opportunities.
• Model Development and Deployment – Go from raw data to accurate, business-driven analytical models with a seamless, efficient process.
• Analytics Acceleration – Generate faster results and improve data governance with in-database analytics.
• Scoring Acceleration – Maximize the performance and accuracy of your analytic models.

For customers who are interested in in-database analytics with a Teradata data warehouse, be sure to learn about SAS Analytic Advantage for Teradata. 

How SAS^® Is Different

Only SAS offers a rich suite of integrated predictive modeling processes so you can:

• Grow and standardize on a common platform with multiple entry points for different types of users.
• Use flexible data preparation and management capabilities to build models that generalize well and produce superior outcomes.
• Take advantage of a rich, interactive visualization and data exploration environment to quickly identify the optimal opportunities.
• Achieve better response times and fast results with in-memory, in-database and grid capabilities.
• Facilitate continuous enhancement, refinement and maintenance of your analytic models.