SAS® Business Analytics Features

Data access

  • Provides access to data from more than 60 data sources, including relational and nonrelational databases, PC files, Hadoop, Amazon Redshift and data warehouse appliances with a single SAS Business Analytics license.
  • Provides direct, secure data access with native interfaces and integration standards.
  • Supports business decisions with complete, consistent, up-to-date and accurate data.

View more data access features

Seamless, transparent data access

  • Broad access to data through an intuitive interface, regardless of where it’s stored.
  • Support for a wide range of databases and platforms, including big data databases, relational stores, data warehouses, mainframe sources and PC files.
  • Easy integration with popular platforms without detailed knowledge of the database or SQL.
  • Option of working in SAS or SQL with automatic generation of the appropriate SQL statements, passed through to the database for execution.
  • Support for integration standards outside of the dedicated SAS/ACCESS module, including SAS/ACCESS Interface to ODBC, SAS/ACCESS Interface to JDBC and SAS/ACCESS Interface to OLE DB.
  • Ability to execute procedural language, complementing SQL-based statement logic.
  • High level of security with native database security mechanisms.
  • Option to import or export data from a PC file to a SAS data set, as well as the ability to read and write directly to PC files.
  • DBMS metadata can be accurately maintained within the SAS Metadata Repository for metadata reuse.

Flexible query language support

  • Seamlessly access data with minimal knowledge of the data or the SQL required to surface it.
  • Take more control by using your own customer SQL statements to modify or maintain automatically generated SQL.
  • Map SAS specific statements or functions to database-specific statements or functions and process SQL statements directly inside the database for optimal performance.
  • Enlist a SAS extension to process the appropriate query logic using the SAS or database engine.

Performance tuning options

  • Take advantage of a multithreaded read interface, as well as threaded kernel technology and native APIs to Oracle, DB2 Spark and Teradata.
  • Enable federated views through optimized read and write, including buffering, compression, threading, chunking, and sort and join performance.
  • Join processing is automatically pushed into the database.
  • Boost performance with temporary table support.
  • Work effortlessly with seamless interfaces to loaders and utilities without an in-depth understanding of each loader.
  • Use PROC TRANSPOSE pushdown capability for Teradata and Hadoop.
  • Use PROC pushdown capability for Amazon Redshift, Postgres, Microsoft SQL Server and metadata integration.
  • Maintain DBMS metadata within the SAS metadata repository, and reuse data jobs.
  • Use jobs across a variety of SAS solutions, including SAS® Enterprise Guide® and SAS Data Management.
  • Use native storage options, including support for temporary tables, materialized views and partitioned tables.
  • Make use of native database types that translate the source database to the appropriate SAS data type.
  • Increase database performance with processor threads, placing data into a memory buffer between reads.
  • National language support.

Optimization features for better performance

  • Use pipeline read to increase database read performance by up to 30 percent via a processor thread that reads data from a database and places it into a memory buffer. Pipeline read is available in SAS/ACCESS Interface to DB2 (non-z/OS), SAS/ACCESS Interface to Greenplum, SAS/ACCESS Interface to Oracle and SAS/ACCESS Interface to Teradata.
  • Improve efficiency with native storage options, including temporary tables, materialized views and partitioned tables.
  • Speed processing with native database types, which are automatically translated to the appropriate SAS data type.

SAS/ACCESS for databases

SAS/ACCESS interfaces to relational databases and database appliances include:

  • Teradata
  • Aster Data
  • Cloudera Impala
  • Datacom
  • Greenplum
  • Netezza
  • Vertica
  • Informix
  • SAP HANA, SAP R3, SAP ASE, SAP IQ
  • DB2
  • Oracle and Oracle RDB
  • PostgreSQL
  • Microsoft SQL Server
  • MySQL
  • Amazon Redshift
  • JDBC
  • Spark SQL – SAS Viya
  • ODBC
  • OLE DB 

SAS/ACCESS® for mainframes

Supported mainframe sources include:

  • SAS/ACCESS® Interface to ADABAS
  • SAS/ACCESS® Interface to DATACOM/DB
  • SAS/ACCESS® Interface to CA IDMS
  • SAS/ACCESS® Interface to IMS-DL/I

SAS/ACCESS® for NoSQL data platforms

  • SAS/ACCESS® Interface to the PI System

SAS/ACCESS® for distributed file systems

One supported distributed file system source is:

  • SAS/ACCESS® Interface to Hadoop

SAS/ACCESS® Interface to PC Files 

SAS/ACCESS Interface to PC Files includes access to:

  • DBF
  • DIF (Unix)
  • XLS (Windows)
  • WK1
  • WK3
  • WK4
  • XLSX

SAS/Access® for Non-Relational Sources

  • MongoDB
  • Salesforce

Self-service data preparation

  • Provides an interactive, self-service, easy-to-use interface for profiling, cleansing and blending data using a GUI.
  • Enables full integration with your analytics pipeline.
  • Provides access to data lineage with network diagrams.
  • Enables you to reuse, schedule and monitor jobs.

View more self-service data preparation features

Data and metadata access

  • Use any authorized internal source, accessible external data sources and data held in-memory in SAS Viya.
    • View a sample of a table or file loaded in the in-memory engine of SAS Viya, or from data sources registered with SAS/ACCESS, to visualize the data you want to work with.
    • Quickly create connections to and between external data sources.
    • Access physical metadata information like column names, data types, encoding, column count and row count to gain further insight into the data.
  • Data sources and types include:
    • Amazon S3.
    • Amazon Redshift.
    • DNFS, HDFS, PATH-based files (CSV, SAS, Excel, delimited).
    • DB2.
    • Hive.
    • Impala.
    • SAS® LASR.
    • ODBC.
    • Oracle.
    • Postgres.
    • Teradata.
    • Feeds from Twitter, YouTube, Facebook, Google Analytics, Google Drive, Esri and local files.
    • SAS® Cloud Analytic Services (CAS).

Data provisioning

  • Parallel load data from desired data sources into memory simply by selecting them – no need to write code or have experience with an ETL tool. (Data cannot be sent back to the following data sources: Twitter, YouTube, Facebook, Google Analytics, Esri; it can only be sourced form these sites).
    • Reduce the amount of data being copied by performing row filtering or column filtering before the data is provisioned.
    • Retain big data in situ, and push processing to the source system by including SAS In-Database optional add-ons.

    Guided, interactive data preparation

    • Transform, blend, shape, cleanse and standardize data in an interactive, visual environment that guides you through data preparation processes.
    • Easily understand how a transformation affected results, getting visual feedback in near-real-time through the distributed, in-memory processing of SAS Viya.

    Column-based transformations

    • Use column-based transformations to standardize, remediate and shape data without doing configurations. You can:
      • Change case.
      • Convert column.
      • Rename.
      • Remove.
      • Split.
      • Trim whitespace.
      • Custom calculation.
    • Support for wide tables allows for the saving of data plans for quick data preparation jobs.

    Row-based transformations

    • Use row-based transformations to filter and shape data.
    • Create analytical-based tables using the transpose transformation to prepare the data for analytics and reporting tasks.
    • Create simple or complex filters to remove unnecessary data.

    Code-based transformations

    • Write custom code to transform, shape, blend, remediate and standardize data.
    • Write simple expressions to create calculated columns, write advanced code or reuse code snippets for greater transformational flexibility.
    • Import custom code defined by others, sharing best practices and collaborative productivity.

    Multiple-input-based transformations

    • Use multiple-input-based transformations to blend and shape data.
    • Blend or shape one or more sets of data together using the guided interface – there’s no requirement to know SQL or SAS. You can:
      • Append data.
      • Join data.
      • Transpose data.

    Data profiling

    • Profile data to generate column-based and table-based basic and advanced profile metrics.
    • Use the table-level profile metrics to uncover data quality issues and get further insight into the data itself.
    • Drill into each column for column-level profile metrics and to see visual graphs of pattern distribution and frequency distribution results that help uncover hidden insights.
    • Use a variety of data types/sources (listed previously). To profile data from Twitter, Facebook, Google Analytics or YouTube, you must first explicitly import the data into the SAS Viya in-memory environment.

    Data quality processing

    (SAS® Data Quality on SAS® Viya® is included in SAS Data Preparation)

    Data cleansing

    • Use locale- and context-specific parsing and field extraction definitions to reshape data and uncover additional insights.
    • Use the extraction transformation to identify and extract contact information (e.g., name, gender, field, pattern, identify, email and phone number) in a specified column.
    • Use parsing when data in a specified column needs to be tokenized into substrings (e.g., a full name tokenized into prefix, given name, middle name and family name).
    • Derive unique identifiers from match codes that link disparate data sources.
    • Standardize data with locale- and context-specific definitions to transform data into a common format, like casing.

    Identity definition

    • Analyze column data using locale-specific rules to determine gender or context.
    • Use identification analysis to analyze the data and determine its context, which is particularly valuable if the data or source of data is unfamiliar.
    • Use gender analysis to determine the gender of a name using locale-specific rules so the data can be easily filtered or segmented.
    • Create a unique ID for each row with unique ID generator.
    • Identify the subject data in each column with identification analysis.
    • Identify, find and sort data by tagging data with columns and tables.

    Data matching

    • Determine matching records based upon locale- and context-specific definitions.
    • Easily identify matching records using more than 25 context-specific rules such as date, address, name, email, etc.
    • Use the results of the match code transformation to remove duplicates, perform a fuzzy search or a fuzzy join.
    • Find like records and logically group together.

    System and job monitoring

    • Use integrated monitoring capabilities for system- and job-level processes.
    • Gain insight into how many processes are running, how long they’re taking and who is running them.
    • Easily filter through all system jobs based on job status (running, successful, failed, pending and cancelled).
    • Access job error logs to help with root-cause analysis and troubleshooting. (Note: Monitoring is available using SAS Environment Manager and the job monitor application.)

    Data import and data preparation job scheduling

    • Create a data import job from automatically generated code to perform a data refresh using the integrated scheduler.
    • Schedule data explorer imports as jobs so they will become an automatic, repeatable process.
    • Specify a time, date, frequency and/or interval for the jobs.

    Data lineage

    • Explore relationships between accessible data sources, data objects and jobs.
    • Use the relationship graph to visually show the relationships that exist between objects, making it easier to understand the origin of data and trace its processing.
    • Create multiple views with different tabs, and save the organization of those views.

    Plan templates and project collaboration

    • Use data preparation plans (templates), which consist of a set of transformation rules that get applied to one or more sources of data, to improve productivity (spend less time preparing data).
    • Reuse the templates by applying them to different sets of data to ensure that data is transformed consistently to adhere to enterprise data standards and policies.
    • Rely on team-based collaboration through a project hub used with SAS Viya projects. The project’s activity feed shows who did what and when, and can be used to communicate with other team members.

    Batch text analysis

    • Quickly extract contents of documents, and perform text identification and extraction.

    Cloud data exchange

    • Securely copy data from on-site repositories to a cloud-based SAS Viya instance running in a private or public cloud for use in SAS Viya applications – as well as sending data back to on-site locations.
    • Preprocess data locally, which reduces the amount of data that needs to be moved to remote locations.
    • Use a Command Line Input (CLI) interface for administration and control.
    • Securely and responsibly negotiates your on-site firewall.  

    Visual data exploration & insights deployment

    • Provides an integrated environment for self-service data discovery, reporting and world class analytics.
    • Delivers easy-to-use predictive analytics with “smart algorithms.”
    • Enables data exploration and information sharing via email, web browser, Microsoft Office or mobile devices.
    • Provides web-based administration, monitoring and governance of a single platform.

    View more data exploration & insights deployment features

    Data

    • Import data from a variety of sources: databases, Hadoop, Excel spreadsheets, social media, etc.
    • Drag an Excel file, CSV or SAS data set onto your workspace, and quickly start building reports or dashboards.
    • Use standard data quality functions like change case; convert, rename, remove and split columns; and create calculated columns and transformations using custom code.
    • Prep data using append, join, filter and transpose functions.
    • Reuse, schedule and monitor jobs.
    • View lineage with network diagrams.
    • Quickly view descriptive statistics on measures to help you see the characteristics of your data.
    • Create calculated, aggregated or derived data items.
    • Create drillable hierarchies in a self-service manner without the need to predefine user paths.

    Discovery

    • Interactive data discovery enables business users and analysts to easily identify relationships, trends, outliers, etc.
    • Precise and responsive layout capabilities give you flexible layout and design options. You can stack or group items, and more.
    • A variety of graph objects or charts are included:
      • Bar.
      • Pie.
      • Donut.
      • Line.
      • Scatter.
      • Heat map.
      • Bubble.
      • Animated bubble.
      • Treemap.
      • Dot.
      • Needle.
      • Numeric series.
      • Schedule chart.
      • Vector.
      • Key value infographics.
      • And many more with flexible graph building capabilities.
    • Add content from the web (e.g., YouTube videos, web apps) and images (e.g., logos) to your report.
    • Custom sort allows you to rank order category data items in a table or graph by characteristics (e.g., products, customers). The characteristics that are most important to your organization will be displayed first.
    • One-click filtering (e.g., one way, bidirectional) and linked selections will allow you to spend less time manually linking content (e.g., visualizations, reports).
    • Text objects include date-driven or system-generated text for relevant context.
    • Synchronize selection and filters across visualizations in a report or dashboard.
    • Link different reports (e.g., link a sales report to an inventory report).
    • Report consumers can change calculation parameters and display rules using controls, filters, etc. to see information that is most relevant to them.
    • Report consumers can switch measures and change chart type and formatting all on the fly allowing them to make critical business decisions instantly.
    • Set refresh rates for individual objects, pages or your entire report.
    • Analytical visualizations include:
      • Box plot.
      • Heat map.
      • Animated bubble chart.
      • Network diagram.
      • Correlation matrix.
      • Forecasting.
      • Parallel coordinates plot.
      • Decision tree.
      • And many more with flexible graph building capabilities.
    • Geographical map views provide a quick understanding of geospatial data, including travel time and travel distance, demographics data enrichment with Esri integration.
    • Network diagrams enable you to display networks across a map.
    • Bring your own custom interactive visualizations (e.g., D3.js graphs, C3 visualizations or Google charts) into SAS Visual Analytics, so they’re all driven by the same data.
    • Key value visualization allows you to display important metrics (numeric or categorical value) in an infographic style for quick reference.
    • Perform path analysis (Sankey diagrams) to visualize relationships between a distinct sequence of events.
    • Add cell visualizations, like bars and heat maps, to your tables to quickly identify problem points and see trends in your data.
    • Generate forecasts on the fly with forecasting confidence intervals included.
    • The most appropriate forecasting model is automatically selected after running multiple models against data.
    • Scenario analysis lets you see how changes in different variables would affect forecasts.
    • Goal seeking enables you to specify a target value for your forecast, and then determines the values of underlying factors that would be required to achieve the target value.
    • Decision tree graphically depicts likely outcomes.
    • Custom binning moves continuous data into a small number of groups for better interpretation and presentation of results.
    • Text analysis capabilities enable you to automatically find topics and understand sentiment from text sources, including Facebook, Twitter, Google Analytics, YouTube comments and more.
    • Recover reports you are editing when your session ends unexpectedly. Reports are automatically saved every five seconds after an edit is made.
    • Pick up where you left off from a prior session on all your devices.

    Augmented analytics

    • Autocharting automatically chooses the graph best-suited to display selected data.
    • Automated Explanation determines which variables contribute to an outcome and provides a simple natural language explanation that is easy to understand.
    • Quickly detect and highlight patterns and outliers in your data with Automated Explanation.
    • Automated Explanation determines the key difference between the top and bottom cases in data. For example, what best differentiates the lowest risk and the highest risk cases?
    • The steps taken to automatically explain your data are displayed for transparency.
    • Use Automated Explanation to identify interesting groups based on factors you select.
    • Automatically builds an interactive analytical story based on all your data, ready to be published.
    • Suggested insights automatically derived from your data allows you to quickly build informative reports and dashboards.
    • Related measures highlighted within the measure list so users can quickly identify potential interactions.

    Sharing & collaboration

    • Reuse and share report modifications, such as filters, calculations, hierarchies and report element formatting.
    • Collaborate across mobile devices and the web by adding comments to a report.
    • Create alerts for a report object so that subscribers are notified via email or a text message when the threshold condition is met.
    • Distribute reports as PDFs or email in a secure manner. Distribute reports once or at recurring intervals, such as daily, weekly or monthly.
    • Playable dashboards let you put your report in slideshow mode.
    • Administrators can configure support for guest access to view report or visualization.
    • Guest users can view the insights that are available to the public.
    • Users can see, organize and collaborate on their work using SAS Drive:
      • Users can favorite, share, preview and tag their content from one place.
      • Create projects that share data, content and other resources with project members.

    SAS® Visual Analytics Apps

    • Available for free from:
      • App Store for iOS iPhone and iPads.
      • Google Play for Android phones and tablets.
      • Microsoft Store for Windows 10 devices.
    • The app lets you connect and interact with your SAS Visual Analytics reports and dashboards using gestures native to your devices.
    • Interact with your SAS Visual Analytics app for iOS using voice commands.
    • Reports created once in SAS Visual Analytics can be viewed anywhere.
    • Gain secure access to content on mobile devices, both online and offline.
    • Annotate, comment, share and email reports to others for increased collaboration.
    • Screenshots can be captured and comments shared with others.
    • Notifications alert business users when a report is updated, data is changed or the application is updated.

    Embedded insights

    • Create your own mobile apps using the SAS SDK for iOS and SAS SDK for Android to create embedded insights:
      • Personalize your mobile app with embedded SAS Visual Analytics content, your corporate logo and name of your choosing.
      • Preconfigure your mobile app to connect to SAS servers and subscribe to specified reports.
      • Develop completely customized mobile apps that embed SAS Visual Analytics content (e.g., GatherIQ).
      • Manage and secure your mobile app and data by integrating with mobile device management (MDM) service (via new APIs).       
    • Embed full reports or individual objects in websites and web apps using the SAS Visual Analytics SDK:
      • Combine insights from multiple reports in one location.
      • User selections within an embedded SAS Visual Analytics object can drive other elements anywhere on the webpage.

    Location analytics

    • Geographical maps are enabled through Esri ArcGIS Online or OpenStreetMap. 
    • You can lasso data points on geographical maps to select specific data for further analysis.
    • Geographical maps make it easy to visualize measurement variances over a geographical area.
    • Access to all Esri basemaps and geosearch is available through Esri ArcGIS Online at no additional charge.
    • Custom polygons (e.g., sales territories, voting districts, floor plans, seating charts) will let you see the world just as your business demands for it. These polygons can be animated to show how key metrics change over time.
    • Geographic point clustering makes it easier to visualize high-volume location data and identify areas of interest. Get more or less details at different zoom levels.
    • Add map pins to mark points of interest and insights on a map.
    • With Esri ArcGIS Online license, you can enrich your data with Esri demographics data:
      • Start from a pin, and select the area that can be traveled based on travel distance or provided travel time.
      • Create travel routes between points.
      • Understand how location affects outcomes by geocoding your data – adding latitude and longitude columns to your data based on location information in your data (country, state, zip code, city, street).

    Security & administration

    • SAS Environment Manager provides easy-to-use, web-based centralized administration and monitoring of your BI and analytics environment, including users, data, content, servers, services and security.
    • User authentication and content authorization support governance.
    • Object-level security (folders, reports, etc.) and data security (table and row level) support governance.
    • Seamless integration with corporate identity directories such as LDAP.
    • Rules-mapping application capabilities for users and groups support governance.
    • Whitelist or blacklist mobile devices to determine authorization for SAS Visual Analytics apps.
    • Near-real-time dashboard for monitoring system health and key activities.
    • Distributed processing node addition and deletion.
    • Scriptable APIs perform administrative tasks in batch, including management of security, libraries, users groups and configurations.
    • Customizable monitoring and performance reports.
    • Environmentwide log exploration, job scheduling and monitoring.

    SAS® Viya® in-memory engine

    • CAS (SAS Cloud Analytic Services) performs processing in memory and distributes processing across nodes in a cluster.
    • User requests (expressed in a procedural language) are translated into actions with the parameters needed to process in a distributed environment. The result set and messages are passed back to the procedure for further action by the user.
    • Data is managed in blocks and can be loaded in memory and on demand.
    • If tables exceed memory capacity, the server caches the blocks on disk. Data and intermediate results are held in memory as long as required, across jobs and users.
    • Includes highly efficient node-to-node communication. An algorithm determines the optimal number of nodes for a given job.
    • Communication layer supports fault tolerance and lets you remove or add nodes from a server while it is running. All components can be replicated for high availability.
    • Support for legacy SAS code and direct interoperability with SAS 9.4M5 clients.
    • Supports multitenancy deployment, allowing for a shared software stack to support isolated tenants in a secure manner.

    Deployment flexibility

    • On-site deployments:
      • Single-machine server to support the needs of small to midsized organizations.
      • Distributed server to meet growing data, increasing workloads and scalability requirements.
    • Cloud deployments:
      • Enterprise hosting.
      • Private or public cloud (e.g., BYOL in Amazon) infrastructure.
      • SAS managed software as a service (SaaS).
      • Cloud Foundry platform as a service (PaaS) to support multiple cloud providers.

    Approachable analytics

    • Provides access to advanced analytic capabilities without coding, including:                                        
      • Correlations.
      • Forecasting.
      • Scenario analysis.
      • Decision trees.
      • Text analysis.
      • Automated goal seeking (an advanced SAS Forecasting feature).

    Back to Top