In today’s fast-evolving data landscape, many aspiring analysts and data scientists are asking- “Is SAS dying?” With the explosive growth of Python, R, and cloud technologies, there’s a growing perception that SAS, once the leader in analytics, is losing its relevance. But is that really the case?

Let’s explore the facts and future of SAS — especially for those considering careers in clinical research, financial analytics, or data science.

What is SAS? and Why Was It So Dominant?

SAS (Statistical Analysis System) has been a trusted platform for statistical analysis, data management, and predictive modeling for more than four decades. It became the industry standard in sectors like pharmaceuticals, banking, insurance, and healthcare, where regulatory compliance and data integrity are critical.

Key reasons for SAS’s long-standing dominance include:

• High reliability and performance for large-scale analytics
• Regulatory acceptance by global bodies like the FDA, EMA, and financial regulators
• User-friendly interface with strong customer support
• Powerful data manipulation and reporting capabilities

For many years, SAS certification was considered a passport to a high-paying analytics career.

Why Do People Say SAS Is Dying?

The perception of SAS losing popularity stems from several global trends:

1. High Licensing Cost

Unlike open-source tools like Python and R, SAS requires expensive licensing. Startups, academic institutions, and even some enterprises are shifting toward cost-effective, open-source alternatives.

2. Rise of Python and R

Python has become the most widely adopted programming language in data science, AI, and machine learning. R is the go-to tool for academic research and statistical modeling. These languages offer flexibility, community support, and integration with modern technologies.

3. Shift in Educational Curriculum

Today, most universities and online platforms teach Python and R by default. As a result, the new generation of data professionals enters the workforce without SAS exposure.

4. Broader Skill Demands in the Job Market

Modern data science roles often require knowledge of:

• Python or R for programming
• SQL for data querying
• Cloud computing (AWS, Azure, GCP)
• Data visualization tools like Power BI or Tableau

This makes SAS insufficient on its own for many roles outside its traditional industries.

Where SAS Remains Strong in 2025?

Despite competition, SAS remains essential and highly valued in specific industries.

Clinical Research and Pharmaceuticals:

SAS is still the industry standard for clinical trial data analysis and submission. Organizations like the FDA and EMA prefer SAS datasets. Career roles such as Clinical SAS Programmer, Biostatistician, and CDISC Specialist remain in demand across India, the US, Canada, and Europe.

Banking and Credit Risk:

Many large banks continue to use SAS for building and validating risk models. Credit scoring, fraud detection, and regulatory compliance in banking rely on SAS’s secure, auditable environment.

Insurance and Healthcare Analytics:

Actuarial modeling, claims analysis, and patient outcome studies often use SAS due to its stability and compliance features.

Is SAS Evolving?

Yes. SAS has modernized its platform with:

• SAS Viya – A cloud-native platform that supports integration with open-source tools
• Support for Python and R within the SAS environment
• Machine learning and AI capabilities
• Better scalability and cloud deployment

This transition shows SAS’s commitment to staying relevant in a competitive ecosystem.

Should You Still Learn SAS in 2025?

It depends on your career path. Here’s a practical guide:

If you're already skilled in SAS, expanding your toolkit with Python or cloud tools can make you highly competitive.

SAS is not dying. It is transitioning.

While it may no longer be the only or dominant player in all areas of data science, it remains critical in regulated, high-risk industries such as pharmaceuticals, banking, and insurance. SAS is evolving through platforms like Viya and integration with Python and R.

The most future-ready professionals will be those who combine SAS knowledge with modern data science tools and cloud technologies. Instead of asking whether SAS is dying, ask how you can position yourself to be relevant across both legacy and modern platforms.