SAS Visual Analytics

Basic Information

SAS Visual Analytics is a comprehensive business intelligence and analytics software designed for visual data exploration, interactive reporting, and self-service analytics. It is available in various versions, often tied to either the SAS 9.4 platform or the cloud-native SAS Viya platform. Key versions include SAS Visual Analytics 7.5 (compatible with SAS 9.4M6 through 9.4M9) and versions integrated with SAS Viya (e.g., 8.1, 8.3, 8.4, 8.5, 2020.1, and later).

• Model/Version: SAS Visual Analytics (various versions, including 7.5.x on SAS 9.4 and 8.x/202x.x on SAS Viya).
• Release Date: Initial release of SAS Visual Analytics 5.1 on SAS 9.3 was March 27, 2012. SAS Visual Analytics 8.1 on Viya 3.1 was released March 21, 2017. The SAS Viya platform itself was initially released in 2016. The latest stable version of the mobile app, 2025.09.1, was listed on September 18, 2025.
• Minimum Requirements:
  • Server (Distributed LASR): Minimum 4-server, 16-core environment (4 servers x 4 cores) with Intel Xeon architecture and 16 GB RAM per core (256 GB total).
  • Server (Non-distributed LASR): Minimum 4 cores with Intel Xeon architecture and 16 GB RAM per core (64 GB total).
  • Client: Minimum two CPU cores, 8 GB RAM, and a screen resolution of 1280 x 1024.
• Supported Operating Systems:
  • Server (Linux): Oracle Linux (e.g., 8.10, 9.1), Red Hat Enterprise Linux (e.g., 8.10, 9.1), SUSE Linux Enterprise Server (e.g., 15 SP3). Specific versions depend on the SAS 9.4 maintenance release or SAS Viya version.
  • Client (Windows): Windows 7, 8, 10, and 11 (for 7.5M1 and later). Windows Server versions are also supported.
  • Mobile (App): iOS, Android, and Windows 10.
• Latest Stable Version: For the mobile application, version 2025.09.1 is available. For the platform, it aligns with SAS Viya Long Term Support (LTS) releases, such as 2025.03.
• End of Support Date: Information regarding specific end-of-support dates for various versions requires consulting official SAS documentation and support policies.
• End of Life Date: Information regarding specific end-of-life dates requires consulting official SAS documentation and support policies.
• Auto-update Expiration Date: This information is not publicly available and requires consulting official SAS documentation.
• License Type: Server-based licensing, typically determined by the number of cores on the SAS application server. Flexible pricing options are available, including Basic, Professional, and Premium plans, based on the number of users and customization level. Licenses are provided as JSON web token files.
• Deployment Model: Supports on-premise deployments, cloud-native deployments on the SAS Viya platform, and can be deployed in distributed or non-distributed SAS LASR Analytic Server modes. It is available as SaaS modules and can be deployed on major cloud platforms like Amazon AWS, Google Cloud, Red Hat OpenShift, and Microsoft Azure.

Technical Requirements

SAS Visual Analytics demands enterprise-grade hardware for server deployments to handle analytical workloads, while client requirements are more modest.

• RAM:
  • Server: Minimum 16 GB per core. This translates to 64 GB for a 4-core non-distributed deployment and 256 GB for a 16-core distributed deployment.
  • Client: Minimum 8 GB.
• Processor:
  • Server: Intel Xeon architecture is required.
  • Client: Minimum two CPU cores. SAS Viya supports 64-bit AMD chipsets; 32-bit chipsets are not supported.
• Storage: Client machines require swap space of 1.5 times physical RAM or 250 GB, whichever is less. Support for Hadoop distributions is available for co-located data storage.
• Display: Client machines require a minimum screen resolution of 1280 x 1024.
• Ports: Deployment involves designating specific ports for communication between components.
• Operating System: Server deployments primarily target 64-bit Linux distributions such as Oracle Linux, Red Hat Enterprise Linux, and SUSE Linux Enterprise Server. Client access is supported on 64-bit Windows operating systems (Windows 7, 8, 10, 11) and mobile platforms (iOS, Android, Windows 10 for the app).

Analysis of Technical Requirements

The technical requirements for SAS Visual Analytics highlight its enterprise focus and scalability. Server-side deployments necessitate substantial processing power and memory, particularly for distributed analytics with SAS LASR Analytic Server, emphasizing the need for robust, multi-core Intel Xeon-based systems. This ensures efficient in-memory processing of large datasets. Client-side requirements are comparatively light, reflecting its web-based and mobile application access model, where much of the heavy lifting occurs on the server. The support for various Linux distributions on the server and Windows/mobile on the client demonstrates broad compatibility within typical enterprise IT environments. The explicit mention of 64-bit architecture underscores its modern design and capability to handle large data volumes and complex computations.

Support & Compatibility

SAS Visual Analytics offers broad compatibility and support across various platforms and data sources, ensuring integration within diverse enterprise ecosystems.

• Latest Version: The latest stable version for the mobile app is 2025.09.1. Platform versions align with SAS Viya LTS releases, such as 2025.03.
• OS Support:
  • Server: Primarily 64-bit Linux distributions including Oracle Linux, Red Hat Enterprise Linux, and SUSE Linux Enterprise Server.
  • Client: Windows 7, 8, 10, and 11.
  • Mobile: iOS, Android, and Windows 10 for the SAS Visual Analytics App.
• End of Support Date: Specific end-of-support dates are subject to SAS's official support policies and documentation.
• Localization: Supports report localization, allowing content to be translated without altering the report design. A command-line interface (CLI) is available for managing report translations.
• Available Drivers: Utilizes SAS/ACCESS engines for parallel loading and connectivity to various data sources, including Teradata, Greenplum, and Hadoop. It also requires Python for SAS Information Retrieval Studio and ArcGIS Server 10.x or later for map services.

Analysis of Overall Support & Compatibility Status

SAS Visual Analytics demonstrates a strong commitment to support and compatibility, crucial for enterprise deployments. Its extensive OS support, particularly across major Linux distributions for servers and Windows for clients, ensures flexibility in deployment environments. The availability of mobile applications for iOS, Android, and Windows 10 extends accessibility to a wide range of devices. The robust localization features are a significant advantage for global organizations, enabling reports to be consumed in multiple languages. Furthermore, the reliance on SAS/ACCESS engines and support for third-party tools like ArcGIS and Hadoop underscores its capability to integrate with diverse data landscapes, making it a versatile tool for complex analytical needs. The continuous release of updates and versions, especially within the SAS Viya platform, indicates ongoing development and support.

Security Status

SAS Visual Analytics incorporates a multi-layered security framework to protect data and access, aligning with enterprise security requirements.

• Security Features:
  • Authentication: Handled by SAS Logon Manager, supporting direct LDAP, Kerberos, SAML, OAuth, OpenID Connect, Host Authentication, and Pluggable Authentication Modules (PAM).
  • Encryption: Default encryption for data in motion using Transport Layer Security (TLS). Data at rest encryption is configurable. Mobile apps encrypt reports and data using the device's operating system encryption. SAS Viya provides self-signed certificates for HTTP/HTTPS access, but CA-signed certificates are recommended for secure SSL connections with mobile apps.
  • Access Control: SAS metadata security is enforced on all reports, and access is based on user roles and permissions.
  • Network Security: Supports allowlists and denylists to control server access.
  • Mobile Security: Features include optional passcode protection for the app, control over remote data persistence, and integration with Mobile Device Management (MDM) solutions via SAS SDK.
• Known Vulnerabilities: Information on specific known vulnerabilities is typically managed through official SAS security advisories and patches, not generally published in broad public searches.
• Blacklist Status: Not publicly available.
• Certifications: Complies with SAS security standards. For secure SSL connections with mobile applications, it requires certificates signed by a Certificate Authority (CA), as self-signed certificates are not supported.
• Encryption Support: TLS is used for data in motion by default. Encryption for data at rest is configurable. Mobile devices encrypt SAS Visual Analytics reports and data along with other device content.
• Authentication Methods: Direct LDAP, Kerberos, Security Assertion Markup Language (SAML), OAuth, OpenID Connect, Host Authentication, and Pluggable Authentication Modules (PAM).
• General Recommendations: Enforce allowlists to manage access, utilize MDM technologies for comprehensive mobile security, and ensure SSL connections use CA-signed certificates for enhanced trust and security.

Analysis on the Overall Security Rating

SAS Visual Analytics offers a robust and comprehensive security posture, essential for handling sensitive enterprise data. The platform provides multiple authentication mechanisms, catering to various organizational security policies and integration needs. Strong encryption support, particularly TLS for data in motion, ensures data confidentiality during transmission. While data at rest encryption is configurable, it allows organizations to meet their specific compliance requirements. The enforcement of metadata security and role-based access controls ensures that users only access authorized information. Mobile app security features, including device-level encryption and MDM integration, address the unique challenges of mobile data access. The requirement for CA-signed certificates for mobile SSL connections further strengthens trust. Overall, SAS Visual Analytics provides a high level of security, with features designed to protect against common threats and support enterprise security best practices.

Performance & Benchmarks

Performance of SAS Visual Analytics is generally optimized for data visualization and analysis, though experiences can vary with dataset size and deployment configuration.

• Benchmark Scores: Specific, publicly available benchmark scores are not readily detailed in general searches.
• Real-world Performance Metrics: Users praise the tool for its speed and efficiency in presenting data and quickly analyzing large volumes of data. However, some users report slow performance when loading exceptionally large datasets, which can impact responsiveness.
• Power Consumption: Information regarding specific power consumption metrics is not publicly available.
• Carbon Footprint: Information regarding the carbon footprint of SAS Visual Analytics operations is not publicly available.
• Comparison with Similar Assets: SAS Visual Analytics is often compared with other business intelligence and data visualization tools such as Microsoft Power BI, QlikView, and Tableau Server. Pricing models are noted as a key differentiator, with SAS Visual Analytics offering flexible plans based on user count and customization, while competitors like Tableau have more straightforward structures.

Analysis of the Overall Performance Status

SAS Visual Analytics is designed for high performance in visual data exploration and interactive reporting, leveraging in-memory processing capabilities, especially with SAS LASR Analytic Server and SAS Cloud Analytic Services (CAS) on Viya. This architecture contributes to its praised speed and efficiency in presenting and analyzing data. However, like many data-intensive applications, its performance can be sensitive to the scale of datasets, with some users experiencing slowdowns when dealing with extremely large volumes. The scalability of the underlying SAS Viya platform, which supports multi-server and distributed deployments, suggests that performance can be optimized through appropriate infrastructure sizing and configuration. While specific benchmark figures are not widely publicized, the focus on advanced analytics and real-time insights implies a design for demanding computational tasks. Comparisons with competitors often highlight its comprehensive feature set, though cost and occasional performance issues with massive datasets are noted areas for consideration.

User Reviews & Feedback

User feedback for SAS Visual Analytics generally highlights its strengths in usability and analytical depth, while also pointing out areas for improvement.

• Strengths:
  • Ease of Use: Users frequently commend its intuitive interface, drag-and-drop functionality, and ease of implementation, making it accessible for quick data exploration and visualization without deep technical skills.
  • Integrations: Valued for seamless integration with various data sources, facilitating efficient data management.
  • Charting and Data Analysis: Appreciated for versatile charting features, enabling easy visual exploration, and intuitive data analysis capabilities that are highly beneficial for insights.
  • Comprehensive Package: Reviewers note its comprehensive suite of features, including ETL capabilities, dashboards, scorecards, query and report writing, OLAP, data warehousing, data mining, and predictive analytics.
  • Fast Data Presentation: Praised for speed and efficiency in presenting data and generating insights from large volumes.
• Weaknesses:
  • Performance with Large Datasets: Users report slow performance when loading very large datasets, impacting responsiveness.
  • Cost: The cost of SAS Visual Analytics is often cited as a significant barrier for businesses.
  • Complexity/Bugs: Some users find the tool complex, wishing for a more user-friendly interface, and report occasional bugs or challenges with integration with other programming languages.
  • Feature Gaps: Limitations include a lack of automated anomaly alerting, absence of slideshows (requiring workarounds), and no direct write-back capabilities (designed for read-only reporting). It also has limited connectivity to some third-party web map services (e.g., ArcGIS) and cannot scan barcodes/QR codes.
• Recommended Use Cases:
  • Automating the distribution of high-level reports.
  • Visual data exploration, interactive reporting, and self-service analytics.
  • Predictive analytics, forecasting, and advanced data visualization.
  • Data warehousing, data mining, and comprehensive business intelligence.
  • Suitable for small, medium, and large enterprises across various sectors, particularly those in IT, data, and web hosting services.

Summary

SAS Visual Analytics stands as a powerful and comprehensive enterprise asset for business intelligence and advanced analytics. Its core strength lies in its intuitive visual data exploration, interactive reporting, and self-service analytics capabilities, making it accessible for users across various skill levels. The platform offers a rich set of features, including ETL, dashboards, predictive modeling, and data mining, all designed to facilitate quick insights from complex data. Its broad compatibility with diverse operating systems and data sources, coupled with robust security features like multi-factor authentication and encryption, makes it a reliable choice for enterprise environments. The support for localization further enhances its global applicability.

However, the asset is not without its weaknesses. Users occasionally report performance slowdowns when dealing with exceptionally large datasets, suggesting that optimal performance is highly dependent on proper infrastructure scaling. The cost of SAS Visual Analytics is frequently cited as a significant barrier, particularly for smaller organizations, and some users note a degree of complexity for newcomers. Feature gaps, such as the absence of automated anomaly alerting, native slideshows, or write-back functionalities, are also mentioned.

Despite these limitations, SAS Visual Analytics is highly recommended for organizations seeking a powerful, scalable, and secure platform for in-depth data analysis, interactive reporting, and predictive modeling. It is particularly well-suited for enterprises that require extensive integration with existing data ecosystems and demand robust security controls. Organizations with very large datasets should carefully consider their infrastructure to ensure optimal performance. For those prioritizing ease of use and comprehensive analytical features, and who have the budget for an enterprise-grade solution, SAS Visual Analytics provides significant value. Its continuous development, especially within the cloud-native SAS Viya platform, ensures it remains a competitive tool in the evolving analytics landscape.

The information provided is based on publicly available data and may vary depending on specific device configurations. For up-to-date information, please consult official manufacturer resources.