• predictive analytics and data mining, to build descriptive and predictive models and
deploy the results throughout the enterprise
• text analytics, to maximize the value of unstructured data assets
• dynamic data visualization, to enhance the effectiveness of analytics
• forecasting, to analyze and predict outcomes based on historical patterns
• model management, to streamline the process of creating, managing, and deploying
• operations research, to apply techniques such as optimization, scheduling, and
simulation to achieve the best result
• quality improvement, to identify, monitor, and measure quality processes over time
• statistical data analysis, to drive fact-based decisions
The following are examples of analytic products:
• SAS Enterprise Miner enables analysts to create and manage data mining process
flows. These flows include steps to examine, transform, and process data to create
models that predict complex behaviors of economic interest. The SAS Intelligence
Platform enables SAS Enterprise Miner users to centrally store and share the
metadata for models and projects. In addition, SAS Data Integration Studio provides
the ability to schedule data mining jobs.
• SAS Forecast Server enables organizations to plan more effectively for the future by
generating large quantities of high-quality forecasts quickly and automatically. This
solution includes the SAS High-Performance Forecasting engine, which selects the
time series models, business drivers, and events that best explain your historical data,
optimizes all model parameters, and generates high-quality forecasts. SAS Forecast
Studio provides a graphical interface to these high-performance forecasting
• SAS Model Manager supports the deployment of analytical models into your
operational environments. It enables registration, modification, tracking, scoring, and
reporting on analytical models that have been developed for BI and operational
• JMP is interactive, exploratory data analysis and modeling software for the desktop.
JMP makes data analysis—and the resulting discoveries—visual and helps to
communicate those discoveries to others. JMP presents results both graphically and
numerically. By linking graphs to each other and to the data, JMP makes it easier to
see the trends, outliers, and other patterns that are hidden in your data.
SAS Metadata Repository
Your information assets are managed in a common metadata layer called the SAS
This repository stores logical data representations of items such as libraries, tables,
information maps, and cubes, thus ensuring central control over the quality and
consistency of data definitions and business rules. The repository also stores information
about system resources such as servers, the users who access data and metadata, and the
rules that govern who can access what.
All of the data management and business intelligence tools read and use metadata from
the repository and create new metadata as needed.
Components of the SAS Intelligence Platform 7