SAS Analytics Reviews

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.

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;

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.

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. 

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.

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.

We use this solution for CRM and retention marketing.

This solution has helped us materially grow multiple business categories.

I use SAS daily to analyze data, produce reports, and other outputs. I also use it to replicate our entire financial system to verify/duplicate calculations.

SAS Business Intelligence is well-suited for our large corporation. We have demand for scalable and reliable insights into information which is housed in our large systems.