SAS Analytics Reviews

My main use case for SAS Analytics is data management and cleansing, and also a little analytics.

I worked on a project where we received student data, and we needed to cleanse and manage the data by transforming and organizing it so everybody would understand it. We also obtained analytics of the data, such as how many students are in the class and how many students are interested in specific things that we were transforming. Data management and cleansing are the most important use cases for me.

Those are the main two tasks I performed, but sometimes I also use more advanced analytics in SAS Analytics, such as accessing statistical analytics.

In my opinion, the best features SAS Analytics offers are the data steps, a simple UI, a very good interface, and features that are easy to access and understand.

What I appreciate most about the data steps and the interface is the simplicity and easily memorable steps such as DATA and PROC steps.

I do not use many features other than these data steps, and I appreciate SAS Analytics integration with environments such as cloud environments and Microsoft environments. This is also a good use case for me and for my college computers as well when we are using it.

SAS Analytics has positively impacted our organization, as we have noticed that our time is saving much more than using other complex tools for small things, and we have also noticed good efficiency in our work. In the end, I tried to improve my faculty by doing it very fast and showing them perfect results with good efficiency.

We have noticed at least a thirty to forty percent saving in time and a thirty to forty percent increase in the efficiency of our work.

SAS Analytics needs to improve for entry-level people in my college because buying licenses is somewhat costly for small businesses. SAS Analytics is also somewhat complex for students who are entering the data field. Additionally, SAS Analytics is not good at integration with other environments, so it should improve on that, and some syntaxes are not good. SAS Analytics has an older architecture in some areas, so it should enhance the UX side and performance in some aspects.

SAS Analytics documentation should improve to be clearer for students who are currently using or trying to use SAS Analytics, so the documentation should be simplified. Customer service is pretty good as of now, as they respond in two to four hours on business days, but faster response times may solve many problems and attract more users.

I rate SAS Analytics seven or eight out of ten. SAS Analytics should improve in massive data handling, as I mentioned earlier. SAS Analytics struggles when it handles massive data, and it should also get top-tier support. The support should improve from two to four hours down to one hour or even thirty minutes. SAS Analytics should also offer more low-code options for those who do not know how to code or to save more time, and they should consider reducing their pricing. Currently, SAS Analytics has limited flexibility and very limited integration, so improvements in those areas are necessary.

SAS Analytics student version does not handle long or lengthy data well right now, so it should improve on that point. As I have mentioned, it struggles with larger data sets, but for current small or medium data sets, it is very good.

Customer support should improve to fifteen to thirty minutes or immediate live agents during business hours. The two to four hours wait can sometimes take more than one day, and that does not make sense. We have to stop all our work until they reply if we encounter critical problems, so they should improve for certain.

Customer service is pretty good as of now, as they respond in two to four hours on business days, but faster response times may solve many problems and attract more users.

Customer support should improve to fifteen to thirty minutes or immediate live agents during business hours. The two to four hours wait can sometimes take more than one day, and that does not make sense. We have to stop all our work until they reply if we encounter critical problems, so they should improve for certain.

For beginners considering using SAS Analytics, they may struggle for one or two weeks at the start because of its complexity. However, it is very good, very simple, and features easy syntax in some cases, so I find it a good tool for data management, data processing, or analytical solutions. I rate SAS Analytics seven out of ten overall.

I work with SAS Analytics solutions, specifically with SAS Data Mining, because I extract information from the core system of the bank via ODBC connections and use SAS Base on the servers. I am the admin of the servers of SAS Analytics for all risk and I use SAS Base and SAS Enterprise Guide, the client version, and Miner. In fact, I do the maintenance of Miner. For example, in some cases the server was reset and all services on the server were not running correctly. Then I manually put the reset of Miner in order. With this, Miner is available for all the teams of risk. For a model, for example, the area, in particular model build, in risk, uses SAS Miner many times. For the strategy of the areas of collection, for example, they make a train, Random Forest, and some types of segmentation models for the strategies.

I appreciate SAS Analytics because it has many options compared to SPSS. For example, I can save the outputs for logistic regression in a table. With this information, I can make many analyses as well. For example, I can do 100 logistic regressions and make an average for the parameters. With this exercise, I avoid bootstrapping or the estimation of the parameters. With other software, I cannot do that. SAS Analytics has a lot of help on the web, and it is easy to understand the code, the options, the tool blocks of the proc, and the data. It is a powerful tool for connecting to many data sources.

By ODBC connections, I can access several sources of data. For example, with SQL, it is not possible because I need to first import the data in SQL Server. I can do it with an ETL in Visual Studio, but SAS Analytics allows a connection directly to the data. If a third person changes information in the Excel file, for example, I do not need to do anything else. I only run my program, and SAS Analytics loads the new information from the Excel file and other sources of data. SAS Analytics is a very versatile and comfortable tool and it is powerful because I can work with data from many millions of records, big data with SAS Analytics in a short time versus other software.

With SAS Analytics, I find that the graphs are very poor, and since I use Power BI, I think SAS Analytics needs to enhance the visualization options to do better visualizations. Many other software have advanced in this area, but I feel SAS Analytics's graphs are not the same. In my experience, many people do not use the graphs of SAS Analytics; instead, they use Power BI or Excel to create graphs because Excel provides more options that SAS Analytics does not. I appreciate that in the last versions, with SAS Viya, there was a significant enhancement in the graphs, but not in SAS Enterprise Guide.

The graphic interface of SAS Analytics could be better.

The data visualization tools in SAS Analytics should be improved, which is why I use Power BI.

I think this is an opportunity area for SAS Analytics.

For visualization, I would rate SAS Analytics a nine.

I consider SAS Analytics a wonderful tool, but they need to enhance the visualizations.

The technical support from SAS Analytics is very good. When I need support, SAS Mexico attends to us promptly and provides more context and explanations about the issues.

For their technical support, I raise a ticket via the web service and later communicate through email. In some cases, we have a call with the technical area of SAS Analytics, and via remote connection, they make settings on the server. I remember one issue where the people from SAS USA connected with SAS in Mexico at about 10:00 p.m. Mexico time to fix the issue on the SAS Analytics server, and their help was greatly appreciated by our team, especially since it was on a Friday night.

In terms of my experience with SAS Analytics, I have some experience with it.

For machine learning, I use more Python. With SAS Viya, I can use SAS Analytics or open source tools like Python or R, but for me, it is easier to use Python for machine learning.

I would rate this review a 9 overall.

This is an application I use for data prep, data exploration, BI reporting, and some basic automated analytics.

You need to constantly jump from data prep to visualization, then back to data for fixing quality, enriching, and visualizing again. Then you start modeling and need to switch back and forth again. Eventually, you have to democratize the results so that anyone can look and interactively question the underlying insights.

For all of these purposes - data to BI or data to AI to BI - it is never a linear one way relationship; it's rather a closed loop for continuous analysis monitoring and sharing. If used this way, SAS Visual analytics can enable continuous improvement and innovation in a corporation.

I like that it is quickly embedding interactive reports and dashboards into a website, Outlook Mail, or even a mobile app. It was very important to democratize results amongst everybody in the company.

The natural language querying and automated preparation of dashboards should be improved.

The cost and accessibility should be improved. For a $900 USD/user/annual license you have to set up a $12,000 USD/annual cloud with a minimum of 16 cores, etc.

I've been using the solution since 2020.

We use SAS Analytics in the area of Business Intelligence (BI), particularly for analyzing data and generating reports.

They could enhance the AI capabilities of the product as the landscape is evolving with AI playing a significant role in analytics.

We have been using SAS Analytics for 12 to 15 years. At present, we use the latest version.

I rate the platform’s stability a ten out of ten.

At the moment, I use SAS Analytics in our organization. However, there were around 1000 users in large corporate organizations.

I rate the scalability a ten out of ten.

The technical support services are good. The team immediately resolves the issues. However, the services are more expensive than Microsoft.

Positive

I have experience working with various solutions in the past, including Cobalt and Microsoft Office products, while working in different corporate business roles such as audit and employee benefits.

The initial setup was easy as we had experienced technical executives working on the process.

I rate the product’s pricing a one out of ten.

I recommend SAS Analytics to others and rate it a ten out of ten.

Our number one use case for SAS Analytics is data analytics. We have about 50 data analysts on our team who are currently using it day in and day out.

All of the data analytics features in SAS Analytics are valuable to us since we're using them daily across our entire analytics team. 

The graphing and visualization features could be enhanced, in my opinion. I would especially stress improving the visualization capabilities.

I have been using SAS Analytics for seven years.

I think that SAS Analytics is stable. 

Given that we have a team of 50 analysts, I would say SAS Analytics is scalable in this sense. It's probable that in the future we will add more users.

We haven't really used the technical support much, but we are satisfied with them so far.

We have only ever used SAS Analytics before because it is one of the industry standards and it's the solution that we started with.

The setup was straightforward. 

Our initial deployment only took about one day. We did it ourselves, and we currently employ two engineers to take care of the maintenance and any extra deployments. One is a system engineer and the other is the analytics lead on our team.

I have not yet seen ROI on SAS Analytics. 

Our licensing covers the usage for around 50 data analysts.

The competing data analytics solutions we considered were R, Python, and other open source solutions.

If you are in need of extensive visualizations, then I would recommend that you first check with SAS Analytics first, to find out whether they are able to offer the right visualization packages that you need. But for analytics and statistics, it's pretty good.

I would rate SAS Analytics a nine out of ten. 

We use SAS Analytics for all data analysis, quick exploratory data analysis, statistical analysis, predictive modeling, and rapid data management in risk analytics. Predominantly, we're using risk analytics, but I plan to extend it to marketing and other departments as well.

The primary focus here is risk analytics and portfolio management in this organization. But in the past, I have used it in various industries for numerous use cases like demand forecasting, inventory management, customer relationship management, strategic analytics, M&A, HR analytics, business intelligence, and analytical data marts.

I have also used SAS extensively for developing risk scoring models, model risk management, collection analytics, and marketing analytics very effectively for banking and financial services.

It's a very good tool for typical data analytics and reporting, data management. It's very easy to use once you learn. The coding is super easy. If you know SQL, you can start using SAS immediately. SAS E-Miner also a great tool for predictive analytics.

Example codes:

Libname mylib “A:\data”;

Data tempdata;

Set mylib.mydata; /* reading data set from the library mylib*/

Or 

Proc SQL:

Create table temp as 

Select * from mylib.mydata;

quit;

They should provide technical support quickly and for free. I would also like them to offer the integration of various Base SAS modules into one like SAS Studio to make it more cost effective for small organization. The installation could also be easier, and the price could be better.

I have been using SAS Analytics and SAS overall for almost 19 years.

SAS is a stable analytics tool.

It's scalable because it has a SAS server version as well as Cloud version. For scalability, you need to get SAS server. They are also in the process to launch on Microsoft Azure Cloud.

If you don't buy a special SAS support plan, they take time to respond. It takes at least three to four days to get a response back from them. You have to buy a premium support plan for a quick response. They should make technical support free or maybe make it much faster.

I have also used Databricks, Azure ML Studio, SPSS, R Studio, Python, C++, Perl, SQL,  Power BI and Tableau for data management, data visualization and advanced analytics.

The installation is little complex.  I think it's a ten-gigabyte file you have to download and install. SAS is not easy to install because it's dependencies and not simple like open-source. The last time my team installed it, I think it took two to three hours from end-to-end.

Though there are SAS cloud versions available, which are easy to use.

Our IT team installed it. Typically, anyone who can install applications at an enterprise level can do it. You just need a SAS administrator in your organization to install and maintain it. But if it's a personal computer, you can do it on your own with the lightweight version of the product. SAS also provides support if you buy it.

SAS is very expensive. The challenge with them is that you have to pay for a license with every module you buy. They should probably combine everything and make it cheaper and light weight in Base Version.

I would recommend SAS for large organizations because of the cost of this enterprise-level solution. For a small organization, I would recommend a cheaper solution.

On a scale from one to ten, I would give SAS a nine rating.

Our primary use case is analytics and reporting in our production area.

One of the things that can be simplified is self-service analytics, especially for a citizen developer or a citizen data scientist. It should be easier to use.

It should be easier to migrate from the on-premises deployment to the cloud version.

We have been using SAS Analytics for at least 15 years.

We have approximately 1,000 users in our organization.

The technical support is okay. If we have problems, they can fix them.

We also have SAP Analytics in place.

Being an on-premises installation, it is somewhat complex.

We have an in-house team of six people who take care of deployment and maintenance.

I think that the cost-benefit ratio is okay. It is the right balance.

At the time, we looked over some of the other products on the market and ultimately decided on SAS.

In summary, this is a good product and I can recommend it to others.

I would rate this solution a nine out of ten.

We use this solution for CRM and retention marketing.

This solution has helped us materially grow multiple business categories.

The most valuable feature is the ability to handle large data sets.

This solution should be made more user-friendly. It needs a better visual output to explain logic to senior executives.

The primary use of SAS Analytics in my area is mostly of statistical modeling, such as logistic regression, survival analysis, and longitudinal analysis as well as producing tabular summary results.

It has facilitated timely analysis results with quality work, meaningful output, and consistent data use.

Modeling ones and figures, such as PROC LIFETEST, PROC LOGISTICS, PROC GPLOT. PROC FREQ and PROC MEANS, are also among the valuable features.

In the graphical area much more improvement is still needed. Once a SAS figure is produced one would like to modify things, such as titles, legends, and incorporate risk sets as a footer on the plots.