Components of the SAS Intelligence Platform

Components Overview

The SAS Intelligence Platform includes components in the following categories:

Data Management

The data management components enable you to consolidate and manage enterprise data from a variety of source systems, applications, and technologies. Components are provided to help you cleanse, migrate, synchronize, replicate, and promote your data. In addition, SAS offers data storage options that are optimized for analytical processing, enabling you to quickly analyze and report on large volumes of data. Metadata for all of your intelligence resources is stored centrally and controlled through a single management interface.

Business Intelligence

The business intelligence components enable users with various needs and skill levels to create, produce, and share their own reports and analyses. Through easy-to-use interfaces, users can obtain their own answers to business questions. Meanwhile, the information technology staff retains control over the quality and consistency of the data.

Analytics

SAS offers the richest and widest portfolio of analytic products in the software industry. The portfolio includes products for predictive and descriptive modeling, data mining, text analytics, forecasting, optimization, simulation, data visualization, model management, and experimental design. You can use any combination of these tools with the SAS Intelligence Platform to add extraordinary precision and insight to your reports and analyses.

The following sections describe in more detail the data management, business intelligence, and analytics components, as well as key supporting components.

Data Management Overview

Data Management

The software tools in the data management category enable you to consolidate and manage enterprise data from a variety of source systems, applications, and technologies. SAS provides access engines and interfaces to a wide variety of data sources, including the following:

delimited files, SAS data sets, and relational database management system (RDMS) tables
application data from enterprise resource planning (ERP) and customer relationship management (CRM) systems
message queuing platforms
Web services
unstructured and semi-structured data

Data storage options include simple relational databases, a threaded multidimensional database that supports online analytical processing (OLAP), and relational storage with a threaded multiple input/output (I/O) subsystem for intensive use by focused applications.

Each of the data management solutions is described briefly in the following sections.

SAS Data Integration Server and SAS Data Integration Studio

SAS Data Integration Studio, which is part of the SAS Data Integration Server offering, is a visual design tool that enables you to consolidate and manage enterprise data from a variety of source systems, applications, and technologies. The software enables you to create jobs and process flows that extract, transform, and load data for use in data warehouses and data marts. You can also create processes that cleanse, migrate, synchronize, replicate, and promote data for applications and business services.

For more information, see Clients in the SAS Intelligence Platform.

DataFlux Data Management Platform

The DataFlux Data Management Platform works with SAS Data Integration Studio and enables you to discover, design, deploy, and maintain data across your enterprise in a centralized way. The DataFlux Data Management Platform combines data quality, data integration, and master data management under a unified user interface.

SAS Data Surveyor

The SAS Data Surveyor applications enable you to build SAS Data Integration Studio jobs to read data directly from these ERP vendors: SAP, Oracle, Salesforce.com, and Siebel.

SAS Data Quality Server

The SAS Data Quality Server works with software from SAS and DataFlux (a SAS company) to analyze, cleanse, transform, and standardize your data. The language elements that make up the SAS Data Quality Server software form the basis of the data quality transformations in SAS Data Integration Studio.

Data Storage Options

The data storage options that can be used with the SAS Intelligence Platform include SAS data tables, parallel storage, multidimensional databases, and third-party databases. These storage options can be used alone or in any combination. Metadata for your intelligence resources is stored centrally in the SAS Metadata Repository for use by all components of the intelligence platform.

Relational Storage: SAS Data Sets

You can use SAS data sets, the default SAS storage format, to store data of any granularity. The data values in a SAS data set are organized as a table of observations (rows) and variables (columns). A SAS data set also contains descriptor information such as the data types and lengths of the columns, as well as which SAS engine was used to create the data.

Access to Third-Party Databases: SAS/ACCESS

SAS/ACCESS provides interfaces to a wide range of relational, hierarchical, and network model databases. Examples include DB2, Oracle, SQL Server, Teradata, IBM Information Management System (IMS), and Computer Associates Integrated Database Management System (CA-IDMS). With SAS/ACCESS, SAS Data Integration Studio and other SAS applications can read, write, and update data regardless of which database and platform the data is stored on. SAS/ACCESS interfaces provide fast, efficient data loading and enable SAS applications to work directly from your data sources without making a copy.

Several SAS/ACCESS engines support bulk load of data files and threaded reads. Threaded reads let you read blocks of data on multiple threads instead of a record at a time. Several engines can use multiple threads to the parallel database management system (DBMS) server to access DBMS data. Both features significantly improve performance so that you can read and load data more rapidly without changing your original data.

High-Performance Computing: SAS In-Database

To support high-performance computing for complex, high-volume analytics, SAS In-Database enables certain data management, analytic, and reporting tasks to be performed inside the database. In-database technology minimizes the movement of data across the network, while enabling more sophisticated queries and producing results more quickly. This technology is available for several types of databases.

Multidimensional Storage: SAS OLAP Server

The SAS OLAP Server provides dedicated storage for data that has been summarized along multiple business dimensions. The server uses a threaded, scalable, and open technology and is especially designed for fast-turnaround processing and reporting.

A simplified ETL process enables you to build consistent OLAP cubes from disparate systems. A threaded query engine and parallel storage enable data to be spread across multiple-disk systems. Support is provided for multidimensional (MOLAP) and hybrid (HOLAP) data stores, as well as for open industry standards.

Parallel Storage: SAS Scalable Performance Data Engine and SAS Scalable Performance Data Server

The SAS SPD Engine and SAS SPD Server provide a high-speed data storage alternative for processing very large SAS data sets. They read and write tables that contain millions of observations, including tables that exceed the 2-GB size limit imposed by some operating systems. In addition, they provide the rapid data access that is needed to support intensive processing by SAS analytic software and procedures.

These facilities work by organizing data into a streamlined file format and then using threads to read blocks of data very rapidly and in parallel. The software tasks are performed in conjunction with an operating system that enables threads to execute on any of the CPUs that are available on a machine.

The SAS SPD Engine, which is included with Base SAS software, is a single-user data storage solution. The SAS SPD Server, which is available as a separate product, is a multi-user solution that includes a comprehensive security infrastructure, backup and restore utilities, and sophisticated administrative and tuning options.

Business Intelligence

The software tools in the business intelligence category address two main functional areas: information design, and self-service reporting and analysis.

The information design tools enable business analysts and information architects to organize data in ways that are meaningful to business users, while shielding the end users from the complexities of underlying data structures. These tools include the following products:

SAS Information Map Studio enables analysts and information architects to create and manage information maps that contain business metadata about your data.
SAS OLAP Cube Studio enables information architects to create cube definitions that organize summary data along multiple business dimensions.

The self-service reporting and analysis tools enable business users to query, view, and explore centrally stored information. Users can create their own reports, graphs, and analyses in the desired format and level of detail. In addition, they can find, view, and share previously created reports and analyses. The tools feature intuitive interfaces that enable business users to perform these tasks with minimal training and without the involvement of information technology staff.

The self-service reporting and analysis tools include the following products:

SAS Web Report Studio is a Web-based query and reporting tool that enables users at any skill level to create, view, and organize reports.
SAS Information Delivery Portal provides a Web-based, personalized workplace to help decision makers easily find the information that they need.
SAS BI Portlets includes portlets, such as the SAS Stored Process Portlet and the SAS Report Portlet, that add value to the SAS Information Delivery Portal.
SAS BI Dashboard enables SAS Information Delivery Portal users to create, maintain, and view dashboards to monitor key performance indicators that convey how well an organization is performing.
SAS Add-In for Microsoft Office enables users to access SAS functionality from within Microsoft Office products.
SAS Enterprise Guide is a project-oriented Windows application that enables users to create processes that include complex computations, business logic, and algorithms.

As users create information maps, cubes, report definitions, portal content definitions, and stored processes, information about them is stored in the SAS Metadata Repository. Client applications and users can access these information assets on a need-to-know basis. Access is controlled through multilayered security that is enforced through the metadata.

For a description of each of the business intelligence tools, see Clients in the SAS Intelligence Platform.

Analytics

SAS offers the richest and widest portfolio of analytic products in the software industry. The portfolio includes products for predictive and descriptive modeling, data mining, text analytics, forecasting, optimization, simulation, and experimental design. You can use any combination of these tools with the SAS Intelligence Platform to add precision and insight to your reports and analyses.

SAS software provides the following types of analytical capabilities:

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 analytic models
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 procedures.
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 applications.
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.

Supporting Components

SAS Metadata Repository

Your information assets are managed in a common metadata layer called the SAS Metadata Repository.

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.

SAS Management Console

SAS Management Console provides a single interface through which system administrators can manage and monitor SAS servers, explore and manage metadata repositories, manage user and group accounts, and administer security.

Scheduling in SAS

Platform Suite for SAS is an optional product that provides enterprise-level scheduling capabilities in a single-server environment. Platform Suite for SAS is also included as part of the SAS Grid Manager product to enable distributed enterprise scheduling, workload balancing, and parallelized workload balancing. The components of Platform Suite for SAS include Process Manager, Load Sharing Facility (LSF), and Grid Management Services.

As an alternative, operating system services can be used to provide a basic level of scheduling for SAS jobs, and SAS in-process scheduling enables you to schedule jobs from certain Web-based SAS applications.