SAS Enterprise Guide features a user-friendly visual interface that simplifies data exploration and management. With its compatibility with other software, it supports data manipulation, analysis, and preparation across industries.
| Product | Mindshare (%) |
|---|---|
| SAS Enterprise Guide | 7.7% |
| Alteryx Designer Cloud | 9.0% |
| Alteryx | 8.7% |
| Other | 74.6% |
| Type | Title | Date | |
|---|---|---|---|
| Category | Data Preparation Tools | Apr 28, 2026 | Download |
| Product | Reviews, tips, and advice from real users | Apr 28, 2026 | Download |
| Comparison | SAS Enterprise Guide vs Alteryx | Apr 28, 2026 | Download |
| Comparison | SAS Enterprise Guide vs Bright Data | Apr 28, 2026 | Download |
| Comparison | SAS Enterprise Guide vs Toad Data Point | Apr 28, 2026 | Download |
| Title | Rating | Mindshare | Recommending | |
|---|---|---|---|---|
| Alteryx | 4.2 | 8.7% | 89% | 85 interviewsAdd to research |
| Unifi | 4.0 | 6.0% | 100% | 2 interviewsAdd to research |
| Company Size | Count |
|---|---|
| Small Business | 3 |
| Midsize Enterprise | 4 |
| Large Enterprise | 12 |
| Company Size | Count |
|---|---|
| Small Business | 26 |
| Midsize Enterprise | 22 |
| Large Enterprise | 66 |
SAS Enterprise Guide offers a range of functionalities ideal for both novice and advanced users. Its visual interface, combined with features like the query builder and drag-and-drop tools, enables data exploration without coding. The tool shines in data management, predictive analytics, and cleaning, providing a platform compatible with other software and facilitating large dataset handling. The reporting and ETL capability further support comprehensive data strategies. Despite strengths, it could benefit from better machine learning integration and enhanced visualization without coding. Challenges also include stability issues, limited community support, and performance faltering with large datasets, especially in environments like Netezza.
What are the key features of SAS Enterprise Guide?SAS Enterprise Guide finds application in industries like banking, pharmaceuticals, and healthcare, mainly for tasks involving data manipulation, statistical analysis, and quality assurance. Its ability to handle large datasets while providing an interface for non-programmers makes it a choice for data modeling and ETL processes. Organizations leverage its integration capabilities for data strategies, enabling agility in data processing and insights generation.
Canary Islands Statistics Institute
| Author info | Rating | Review Summary |
|---|---|---|
| Data Analyst at a financial services firm with 10,001+ employees | 3.5 | I use SAS Enterprise Guide primarily for data analysis and modeling. While it offers valuable server access, its stability issues and limited visualization features need improvement. Despite using similar products, I appreciate not needing extensive Python for data handling. |
| Data Engineer at Ministry of Health New Zealand | 4.5 | At my government health agency, I use SAS Enterprise Guide for querying databases and generating reports. It excels at reporting and has excellent support, though initial setup is complex. Overall, I rate it 9/10. |
| IT Administrator with 1-10 employees | 4.0 | I find SAS Enterprise Guide easy to use with its drag-and-drop interface, leveraging standard SQL. However, it frequently crashes and loops when data fields change, which is frustrating. I wish it provided better error messages instead of terminating the application. |
| Head of Customer Intelligence & Research at a financial services firm with 5,001-10,000 employees | 3.5 | I use this powerful, scalable tool for data cleaning and lookups across various sources. While effective, it's expensive and complex to set up, requiring expert maintenance. I rate it 7/10. |
| Data Scientist at a tech services company with 1,001-5,000 employees | 3.0 | In the banking sector, I use SAS Enterprise Guide for data analytics and modeling, finding it valuable for data engineering and predictive analytics, though it lacks advanced modeling support and community resources compared to Python's extensive open-source solutions. |
| Senior Manager, Data Science and AI / ML Manager at a tech vendor with 10,001+ employees | 3.5 | I use SAS Enterprise Guide for ETL and data processing, finding its stability and scalability excellent. Though it handles our specific needs well, I note its limitations in advanced machine learning features, which require separate SAS products. |
| Advisor at KPMG | 4.0 | I use SAS Enterprise Guide for data tasks. It's user-friendly with good exploration, but lacks a community and support is slow. It's stable and scalable. I recommend it for specific uses, rating it 8/10. |
| SAS Application Architect at a computer software company | 4.0 | I recommend SAS Enterprise Guide for its versatility in ETL, testing, and data science, being user-friendly for business users. Performance with Netezza large datasets is a concern, and it needs better integration/deployment features, though it's stable. |