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Do I Really Get Value from My SAS Language Environment?

Many organizations running SAS language-based analytics want to modernize for a variety of reasons. You might be facing commercial and pricing pressures and want to reduce your costs; maybe you don’t want to be tied to a particular software vendor — or you want to incorporate open-source technologies and new visual tools; or perhaps you want to move to a new cloud platform.

The decision to change isn’t simple. Many businesses depend on their analytics infrastructure for mission-critical processing –and downtime generates big risks and expenses.

Moving to different technologies poses the greatest risk in terms of cost, project failure, operational performance, but there can be benefits. You might consider:

  • Programming language
  • Visual and graphic data science and analytics tools
  • Compute and storage platforms
  • Private or public cloud infrastructure
  • Manages services or outsourcing

When weighing the best course of action, factors to consider include:

  • Cost and complexity of code and data migration
  • Retesting, quality assurance, and business user involvement
  • Robustness of open-source package management and support
  • Reliability of "free" software
  • Interoperability
  • Performance and scalability
  • Platform and hosting cost control
  • Data security
  • Regulation

Balancing risks, costs, and benefits is complex. The practicalities of maintaining 40+ years of SAS language applications can be daunting. Modernize your data science and analytics environment with these three following aspects in mind.

1. One Development Tool for Visual Workflows and Coding

Consider a full lifecycle analytics scenario: You want to build a complex analytical process (workflow) using a high productivity GUI tool using your own logic for data acquisition from Microsoft® Excel®, databases, Snowflake®, and Amazon S3. Altair technology lets you develop data management and ETL processes for processing large amounts of data robustly across a hybrid enterprise infrastructure on premises and in the cloud. Additionally, you can deploy finished data processes into a secure pipeline for scheduled execution and prepare data to shape and make it ready for analysis, build advanced predictive models, and explain those models with decision trees. Seamlessly share data between R, Python, and SAS code, publish results to Power BI, Tableau, or Qlik, and make results available for external processing via web APIs. Altair SLC consolidates these capabilities into a single toolset. With its own fully integrated SAS language complier, you can use Altair SLC to combine R and Python with the SAS language and with other commercial and non-commercial tools and technologies to create executable programs and workflows.

Altair’s approach avoids the complexity and inefficiency of having to use multiple products from multiple vendors. Our open-source technology makes users more effective and our easy-to-use tools make teams more productive without needing to upskill. 

2. One Platform for Deployment, Integration, and Management

To get real value from developing an analytics program or predictive model in your business, you must use it widely and effectively. To do this, deploy it in a managed production environment to get maximum exposure and use. This should likely involve publishing web APIs so other systems, business users, and external services can easily make use of the program or model to support the business. The Altair Hub platform supports clear and easy deployment, operation, and governance of programs and models, with version control and clear separation of development, test, and production environments. Users can deploy workload pipelines according to calendars of condition triggers. Scheduled and interactive user analytics workloads are effectively balanced across a managed analytics cluster on premises, in the cloud, or both together.

3. Pricing that Minimizes Cost and Maximizes Value

Modern public and private cloud infrastructures enable businesses to scale analytics processes on demand for development, testing, and production. This can be a difficult fit with traditional licensing models that typically involve an annual subscription for a fixed compute environment or number of “seats” (users). 

Cloud deployments also create internal governance challenges, meaning IT or other administrators need to control who within an organization can use a cloud environment. Without a governance initiative, businesses risk racking up unexpected cloud platform and/or software licensing bills or falling out of licensing compliance.

Altair’s patented, flexible enterprise cloud pricing model accommodates elastic computing and storage infrastructures.  

Modern Analytics Environment

Modernizing your existing analytics environment can bring immense benefits. Migrate your existing SAS language programs to a modern platform easily in a simple three-step approach:

  1. Run the Altair SLC code analyzer on your existing SAS language programs to generate a detailed compatibility report.
  2. Try running your SAS language programs at zero cost with a free trial version of Altair SLC.
  3. Migrate your applications, programs, and data. We can provide any support you need, though many customers need little or no assistance.

Turbocharge your investment in SAS language programs by migrating to Altair SLCAltair Analytics Workbench, and Altair Hub. Your data science team and analytics applications keep your business competitive. To discover how to lead the pack, visit