SAS Analytics offers a powerful suite of tools for statistical analysis, predictive analytics, and data handling, making it ideal for industries requiring robust data-driven decisions. Its extensive capabilities cater to professionals familiar with SQL and demand forecasting needs across sectors.

| Product | Mindshare (%) |
|---|---|
| SAS Analytics | 8.1% |
| IBM SPSS Statistics | 16.8% |
| IBM SPSS Modeler | 16.5% |
| Other | 58.6% |
| Type | Title | Date | |
|---|---|---|---|
| Category | Data Mining | May 1, 2026 | Download |
| Product | Reviews, tips, and advice from real users | May 1, 2026 | Download |
| Comparison | SAS Analytics vs KNIME Business Hub | May 1, 2026 | Download |
| Comparison | SAS Analytics vs IBM SPSS Statistics | May 1, 2026 | Download |
| Comparison | SAS Analytics vs IBM SPSS Modeler | May 1, 2026 | Download |
| Title | Rating | Mindshare | Recommending | |
|---|---|---|---|---|
| KNIME Business Hub | 4.1 | 11.4% | 94% | 63 interviewsAdd to research |
| IBM SPSS Statistics | 4.1 | 16.8% | 89% | 40 interviewsAdd to research |
| Company Size | Count |
|---|---|
| Small Business | 2 |
| Midsize Enterprise | 2 |
| Large Enterprise | 5 |
| Company Size | Count |
|---|---|
| Small Business | 34 |
| Midsize Enterprise | 16 |
| Large Enterprise | 53 |
With a strong presence in analytics, SAS Analytics provides a seamless experience for data preparation, exploration, and reporting. Users benefit from its ability to handle large data sets, generate interactive reports, and integrate with multiple platforms. Despite its high costs and need for improved visualization and natural language querying, SAS Analytics remains a favored choice for those requiring comprehensive statistical modeling and risk analytics. Enhancing self-service analytics and accelerating support response times are areas of needed improvement. Companies use it extensively for business intelligence and demand forecasting, particularly in sectors like banking and financial services.
What are the key features of SAS Analytics?SAS Analytics is widely implemented in industries for tasks like national auto insurance pricing, financial replication, and marketing analytics. Teams in banking and financial services apply it for quantitative analyses, risk assessments, and generating detailed operational reports, demonstrating its adaptability and strength in handling complex data scenarios.
| Author info | Rating | Review Summary |
|---|---|---|
| Student at Rochester Institute of Technology | 4.0 | I use SAS Analytics for data cleansing and analytics, appreciating its simple interface and efficiency. However, it's costly and complex for beginners, struggles with large data, and needs better integration, documentation, and faster customer support. I rate it 7-8/10. |
| Senior Manager at Scotiabank | 4.5 | As a SAS Analytics admin, I value its power for data extraction, complex analysis, and direct ODBC connections, though its visualizations are poor compared to Power BI. Customer service is excellent. I prefer Python for ML, but rate SAS Analytics 9/10. |
| Finance Business Intelligence at Banco Santander Mexico SA Institucion de Banca | 5.0 | I use SAS Analytics for data analysis, appreciating its macro automation. It's expensive and setup is complex, but I recommend it for its ease of understanding, though Orange offers better visualization. |
| Solution Consulting, Growth, Analytics at Akinon | 3.5 | I use SAS Analytics for data preparation, exploration, and BI reporting. It excels in embedding interactive reports across platforms. However, the natural language functionality and cost need improvement, with expensive licensing and cloud requirements posing accessibility challenges. |
| Director of Mergers and Acquisitions at Look Alive Investment Pvt. Ltd. | 5.0 | I use SAS Analytics for Business Intelligence, particularly for data analysis and report generation. While it serves its purpose, there's room for improvement in AI capabilities. I've used other solutions like Cobalt and Microsoft Office in previous roles. |
| CTO at a pharma/biotech company with 51-200 employees | 4.5 | I use SAS Analytics daily for data analytics with 50 users. All its analytics features are valuable and it's stable and scalable. However, I think its graphing and visualization capabilities need improvement. I rate it 9/10. |
| Global Data Architecture and Data Science Director at FH | 4.5 | I find SAS Analytics excellent for various data analysis and predictive modeling tasks, praising its ease of use once learned. However, I criticize its complex installation, high cost, and slow basic customer support, recommending it primarily for large organizations. |
| Vice President Commercial IT Systems and Digital Channels at a aerospace/defense firm with 10,001+ employees | 4.5 | I find SAS Analytics a good product for analytics and reporting, recommending it with a 9/10 rating. However, I believe self-service analytics needs simplification, and on-prem to cloud migration should be easier. |