SAS Analytics Reviews

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 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. 

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.