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Users have found SAS to be a versatile tool for a wide range of use cases. One prominent use case is the construction and implementation …
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What is SAS Viya?
An end-to-end platform for AI, data science, and analytics, used for modeling, as well as management and deployment of AI models.
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What is SAS Viya?
An end-to-end platform for AI, data science, and analytics, used for modeling, as well as management and deployment of AI models.
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Model Autotuning, Generative AI | SAS Viya March 2023 Release
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What is SAS Viya?
An end-to-end platform for AI, data science, and analytics, used for modeling, as well as management and deployment of AI models.
Automatically generated insights enable users to identify the most common variables across all models, the most important variables selected across models and assessment results for all models. Natural language generation capabilities are used to create project summaries written in plain language, to generate interpretable reports. Analytics team members can add project notes to the insights report to facilitate communication and collaboration among team members.
Automatically generated insights enable users to identify the most common variables across all models, the most important variables selected across models and assessment results for all models. Natural language generation capabilities are used to create project summaries written in plain language, to generate interpretable reports. Analytics team members can add project notes to the insights report to facilitate communication and collaboration among team members.
SAS Viya Competitors
SAS Viya Technical Details
| Operating Systems | Unspecified |
|---|---|
| Mobile Application | No |
Frequently Asked Questions
An end-to-end platform for AI, data science, and analytics, used for modeling, as well as management and deployment of AI models.
Reviewers rate Support Rating highest, with a score of 10.
The most common users of SAS Viya are from Enterprises (1,001+ employees).
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TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!
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Users have found SAS to be a versatile tool for a wide range of use cases. One prominent use case is the construction and implementation of multiple predictive models across various business units. Users appreciate the software's ability to quickly and easily construct models, regardless of dataset size. SAS Office Analytics is particularly useful for creating an intelligent reporting platform, allowing organizations to share important data with non-SAS users through automated processes.
Another key use case is the utilization of SAS Enterprise Guide, which serves as a point-and-click interface, providing power users with access to the full potential of SAS without requiring coding knowledge. This feature empowers users to explore and analyze data efficiently. Additionally, SAS Office Analytics enables users to access any datasource using the ODBC connection, ensuring easy retrieval of data from servers or databases.
SAS Advanced Analytics is highly valued for its capabilities in data mining, text analyses, support vector machines modeling, ensemble modeling, neural network modeling, and other advanced analytic processes. It proves particularly useful for new modeling projects during the development process. The software is also widely used in departments such as human capital and information management for predictive modeling, data manipulation, clustering, and optimization.
Users have compared SAS Advanced Analytics with other free software tools and found it impressive, especially in terms of visual display. They appreciate that SAS offers built-in models for statistical and events-based forecasting through its Forecasting Studio for Desktop. This allows organizations to analyze customer engagement data and forecast customer engagement patterns accurately.
Furthermore, SAS plays a crucial role in integrating data from across an organization into an end customer marketing and sales database. This facilitates reporting and analysis processes while supporting the needs of multiple departments, such as addressing data quality issues, conducting summary analytics, predictive modeling, and insurance rate setting.
In addition to these applications, users rely on SAS Analytics for various tasks like data visualization to create quick and highly responsive dashboards. Some even leverage SAS Advanced Analytics for analyzing complex survey data, such as the Behavioral Health Risk Factor Surveillance System and the Youth Risk Behavior Survey. The user-friendly nature of SAS, combined with its excellent online support, is highly appreciated by users as well.
The marketing department benefits greatly from SAS Analytics, utilizing it to build predictive models that increase the return on investment for marketing campaigns. Overall, the versatility and ease-of-use of SAS make it a valuable tool for businesses across a wide range of industries and departments.
User-Friendly Interface: Users have consistently praised the user interface of SAS Advanced Analytics, with many stating that it is one of the easiest they have used. This has made it convenient for professionals to self-learn and navigate through the software without much difficulty.
Accessible Web Interface: The web interface of SAS Advanced Analytics has been highly appreciated by reviewers for its accessibility. Users can access and use the software from any computer anywhere in the world, allowing them to produce analyses on-the-go.
Wide Range of Simulation Options: Reviewers have commended SAS Advanced Analytics for offering a wide range of options for simulations. Many users consider these simulation capabilities to be world-class, providing them with ample flexibility and versatility in their analytical work.
Some users have found the startup process of SAS Advanced Analytics to be slow and have suggested that it could be improved. They believe that a faster startup time would greatly enhance their user experience.
Several reviewers have expressed their dislike for the default color schemes in Advanced Analytics, finding them unappealing. They feel that more visually appealing and customizable color options would enhance the overall aesthetics of the software.
Users have mentioned that although the analytics steps in Advanced Analytics are generally simpler than other tools, they still feel that SAS could make the process of learning these steps even simpler. They suggest providing more intuitive tutorials or interactive guides to help new users navigate through the software more easily.
Users highly recommend giving SAS Advanced Analytics a chance and suggest taking a course or finding a user to guide you when using the software. They also emphasize the need for expanding the capacity to analyze huge datasets and providing more coding tutoring on the SAS website or YouTube. Furthermore, users suggest thoroughly reviewing tutorials, training, and information on the best ways to utilize SAS Advanced Analytics in order to maximize its potential. Additionally, they recommend considering SAS Advanced Analytics for companies with large datasets and complicated analyses, but also suggest considering other options.
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January 18, 2020
SAS Advanced Analytics Review
We piloted SAS AA at my organization to see how well it compares with other free software tools such as RStudio and Anaconda. So far what we saw was very impressive especially with the visual display but was a little out of our price range. Nonetheless, we were very impressed with the tool overall and would recommend it to other companies particularly those with limited programming expertise.
- Easy-to-navigate drag and drop display.
- Great visual dashboards.
- Price point was too high given the level of use we would anticipate.
- More comprehensive training.
SAS is just as good as these tools but is pricier. I like that it handles data visualization and modeling together in one platform that's a novel mechanism that is fairly rare. Also, it's forecasting capabilities are nicely integrated with the functionality overall which makes for an easy drag and drop interface that enables a forecast to be present on the graphic in very few steps.
January 19, 2019
SAS Advanced Analytics is good, but generally slow
SAS Advanced Analytics is used only in certain departments that do advanced analytics. The program is used for data mining, text analyses, support vector machines modeling, ensemble modeling, neural network modeling, and various other processes. The program is part of an automation movement for processes that get repeated frequently, although most of the usefulness of the program is in the new modeling projects during the development process.
- The user interface of SAS Advanced Analytics is one of the easiest I have used. The software is easy enough for a professional to self-learn. With that said, a professional should probably have some experience in advanced analytics to get the most use out of the software.
- The web interface of SAS Advanced Analytics makes it easier to produce analyses from any computer anywhere in the world.
- The number of options available for simulations is world-class. Additionally, SAS makes it easier than most other software tools to see exactly what it is doing. Other software tools built for the professional analyst are less straight-forward.
- SAS Advanced Analytics takes a while to get doing. SAS could improve the startup process. Because the software starts so slowly, I have to be completely committed to doing analytics for a good period of time before I will open the software.
- The default color schemes on Advanced Analytics are not very nice to look at. It's almost as if SAS read Tableau's playbook and said, "hey, let's do the complete opposite." Bad decision.
- SAS could make the process of learning the analytics steps of SAS Advanced Analytics simpler. Although it is generally more simple than other tools, that doesn't mean it's perfect.
- SAS Advanced Analytics is not the cheapest software on the market. The overall cost was weighed against free, open-source software tools. The overall return, I think, was quite positive because SAS Advanced Analytics saves enormous amounts of time compared to the open-source software tools.
- At first, adopting SAS Advanced Analytics was a negative return because it took time for individuals to change their analytics habits and adjust to superior tools available at their discretion.
- SAS Advanced Analytics has replaced the need to hire less expensive R or Python programmers. So, although the software requires an initial expensive upfront investment, the ease of use makes it so that other areas of expenditure save money.
- ArcGIS, Arcadia Data, Azure Search, Adobe Analytics, Adobe Business Catalyst, Bitrix24, Google Analytics, Google Analytics Premium, Google Data Studio, Google Drive, Apache Hive, Apache Kafka, Apache Spark, Hadoop, Apache Spark Streaming, Oracle NoSQL Database, Amazon Cloud Drive, Amazon Elastic MapReduce, Amazon Elastic Compute Cloud (EC2), Amazon Kinesis, Amazon Kinesis Analytics, Amazon Redshift, Amazon Relational Database Service, Tableau Public, Tableau Online and SAS Data Integration Studio
SAS Advanced Analytics does a fairly decent job producing good results from user-chosen advanced algorithms. The software includes all of the most-often used advanced machine learning and artificial intelligence algorithms. With that said, although SAS Advanced Analytics has all the popular tools, and many of the less popular tools, the software is often not the first to release cutting-edge models. Other software tools, such as R or Python, typically beat SAS Advanced Analytics to the punch. With that said, for almost every applied, practical application, SAS Advanced Analytics is a much more useful product to put in place than the two aforementioned tools.
May 22, 2018
A good tool on advanced analytics
Currently, in our organization, SAS is used for highly statistical analysis of customer engagement data. We also use for forecasting the customer engagement pattern with SAS. In this capacity, we use a special tool called Forecasting Studio for Desktop as well which has built-in models available for statistical and events based forecasting.
- SAS is can be used as query builder tool which can automate a lot of excel process.
- There are variety of statistical options available that tie up good visual analytics.
- There are inbuilt models available for different operations which doesn't require any coding and are easy to run.
- Query builders have less functionality compared to some tools like alteryx.
- Modifying and customizing outputs are still a cumbersome process.
- Coding for special tasks is difficult and lengthy.
- SAS has provided a good statistical analysis but tool training is required.
- A good POC of tools is required as for our industry it did not become very much effective by not having great level of flexibility.
We had major use of SAS in forecasting where it doesn't require high level of coding knowledge and which has highly efficient models built in which can give good results on forecasts without lot of manual intervention. This tool was designed specifically for forecasting and hence was always a better choice compared to other tools.
September 12, 2017
A Look At a New Way to Analyze Visual Data
I currently use SAS Advanced Analytics to analyze data coming in from the state level Behavioral Health Risk Factor Surveillance System and the Youth Risk Behavior Survey. I focus on tobacco data and SAS is extremely important when looking at this data. I use SAS STAT to analyse the data using survey procedures. Because BRFSS and YRBS are complex surveys I must use the survey procedures in SAS to get weighted frequencies, means and estimates. It is extremely easy to use SAS for these analyses, because the procedures are pretty repetitive. However, I do wish that the strata, weight, and cluster variables did not have to be typed with each procedure done. When using STATA or SPSS these variables are entered once, and the weighted results are automatically given when writing procedure. No need to enter them in for each. However, I am more comfortable with SAS coding and really like that it is user friendly. The online support for SAS is wonderful as well.
- Complex Survey Analysis- SAS is a great resource if you need to analyze complex survey data. One can easily write code for this by inserting (survey) in front of the procedure with the weight, cluster, and strata variables. (ex: surveyfreq)
- Modeling/ Graphing- SAS creates clean and easy to understand graphs and models which take visual data to the next level.
- Support- There is a large SAS Advanced analytics online support in place. It is easy to find help on many procedures that you will use in this software.
- SAS requires a lot of coding compared to other software like STATA or SPSS. However most of the code is repetitive , an easy copy/paste.
- SAS Advanced Analytics is not always easily learned, most people would need some training before getting comfortable with the software.
- The layout of the modeling system may get confusing when too many different windows are open, may need to look at different graphs and models one at a time.
- SAS is pretty expensive, so a company would need to take that into consideration before purchasing a license.
- A lot of companies are now starting to use SAS Advance Analytics, it is great for sharing data between departments.
- Presentations come out better when using SAS, and estimates are more precise to the confidence interval.
SAS is faster then both SPSS and STATA. SAS also has better models and graphs when comparing the three softwares. However, STATA and SPSS are more user friendly. It is easy to use SPSS and STATA, because a lot of it is point-click. SAS requires some training to be able to use it as effectively as possible. SAS is better with large data sets, and it is easier to analyze many data points at the same time.
November 18, 2016
SAS Visual Analytics review
It is being used for data visualization with quick and highly responsive dashboards.
- Good data preparation tools
- Well written code with few critical errors
- Responsive customer support
- More focus on presentation aspects with additional graph objects like donut pie charts
- More focus on production readiness including high availability and automatic monitoring of logs
- Positive ROI and achievement of business objectives
SAS has a much superior and comprehensive data preparation capability with a clear approach on how to handle and scale for a large amount of data and users. However, it can be more expensive to implement.