SAS Data Management Reviews and Ratings

Use Cases and Deployment Scope

The product is used by our company to meet specific needs in the analytics field, for our CRM. In our case, it's used to manage customer segmentation logics and CRM targeting. I think it is the ideal solution, but may not find it's place in other areas or projects.

Likelihood to Recommend

I believe that in the field of Data Integration, the SAS product is very suitable if the company uses SAS products for all the steps of the data. It is less suitable if used as a stand-alone tool. In our case, to manage customer segmentation logics and CRM targeting, it is the ideal solution.

Use Cases and Deployment Scope

SAS/Access is used in our department and university-wide to provide a connection to various databases to our SAS Viya and SAS 9.4 server setups as well as local based research servers. SAS/Access allows for greater, concerted efforts to connect multiple databases.

Likelihood to Recommend

SAS/Access is best suited for large numbers of databases but can be easily used for single databases as well.

Use Cases and Deployment Scope

My organization is using the SAS Data Management Platform at an enterprise-level i.e., across multiple companies. The business problem was to have a single source of data for reporting and analysis as well as creating a 360-degree customer view to identify common customers across companies to improve cross-sell and up-sell. As there are multiple systems across multiple systems, a common data management platform was required to create an EDW for the same.

Likelihood to Recommend

SAS Data Management Platform is very well suited if you have common data sources like Oracle, SAP, MS SQL, etc.. Still, if there are a bit more obscure, then you need to purchase additional connectors to connect to those data sources. It is well suited when you don't have a business team with strong technical/coding background as the drag and drop interface makes creating jobs and scheduling them quite easy.

Use Cases and Deployment Scope

The Healthy Nevada Project (healthynv.org) uses SAS/Access to bind the SAS 9.4 analytics platform to electronic healthcare records stored in Microsoft SQL Server. SAS/Access is used across the entire project to extract research data. Without the capabilities of SAS/Access, we would be unable to deliver phenotypic data to geneticists so they can perform genome-wide association studies (GWAS). The product is incredibly robust. In the millions of extracts we have performed, we have never encountered a software defect with SAS/Access. As a result, SAS/Access is "invisible" to the team. It just works. Every. Single. Time. The ACCESS product line from SAS is rich in that there are twenty-five different "adapters" that let SAS connect to data sources as disparate Oracle, DB2, Sybase, Hadoop, Impala, SQL Server, MySQL, Greenplum, HAWQ, Netezza....and the list goes on. If your analytics platform is SAS and you have disparate data sources then SAS/Access is your best friend.

Likelihood to Recommend

SAS/Access is incredibly well-suited for the enterprise-wide data access (read and write) to any data source in your enterprise, no matter the complexity of the schema.

Use Cases and Deployment Scope

This is used department-wide for different clients. There are very limited people who are using it because for handling this tool some experience is required. We use it to make data lakes for fetching data and using it for analyzing patterns.

Likelihood to Recommend

It is suited for taking data from multiple sources simultaneously like for example the streaming data of transactions in retail stores. It processes the data at a pretty high pace and gives high results every time.

It is less appropriate sometimes because it hangs sometimes and does not have a large user community available over the internet.

Use Cases and Deployment Scope

SAS has been used by many users in our organization, mainly business and retail users for running various modeling using SAS optimizer. It allows the user a wider range of capabilities to cleanse and manipulate the data.

Likelihood to Recommend

SAS Enterprise Data Integration allows the user a wider range of capabilities to cleanse and manipulate the data. Not only can the data be pulled directly into SAS, but before it is finalized it can be transposed, graphed, or altered. Depending upon the number of use cases and business requirements, think before investing.

Use Cases and Deployment Scope

SAS Data Integration Studio is being used for one view of all our data across the organization. Pulling from our Oracle and SQL Servers the data is described in a fashion that users can know what they are looking at with only a little bit of effort. It also pulls from our major vendors' platforms, pulling both the data or metadata from their systems.

Likelihood to Recommend

When data is in a system that needs a complex transformation to be usable for an average user. Such tasks as data residing in systems that have very different connection speeds. It can be integrated and used together after passing through the SAS Data Integration Studio removing timing issues from the users' worries. A part that is perhaps less appropriate is getting users who are not familiar with the source data to set up the load processes.

Use Cases and Deployment Scope

SAS/Access is used by my main customer as an efficient way of bringing data from the enterprise data warehouse into SAS for crunching and general customer insight work. It's also used in some cases to reinject data into the warehouse so that people using other BI tools can access it. Using SAS for statistical analysis as well as an ETL tool allows the users to kill two birds with one stone. They can extract the data themselves and then transform it like they want.

The SAS users are located in several business units with the main ones being risk and marketing. For marketing, it's a question of getting data from several different platforms in order to get a clear customer picture. For risk, it's also about getting data from several platforms but the purpose is to size the risk associated with actual and future loans.

Likelihood to Recommend

Really best suited for tasks where some statistical analysis is needed.

Purely ETL work should be done with a different tool.

Use Cases and Deployment Scope

We are using SAS/Access to extract and join data from a variety of platforms to create new datasets in a form usable by business partners.

Likelihood to Recommend

Extracting from large databases in order to subset and filter for specific purposes is a suitable use for SAS/Access.

Use Cases and Deployment Scope

I currently use SAS/Access 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.

Likelihood to Recommend

SAS/Access is well suited for companies who need to manipulate and analyze large databases and data-sets. It does the same thing as SQL, and if you already know basic SAS coding it is easier to pick up. SAS/Access works well with analyzing data from multiple data-sources at once, including large databases stored in external and virtual environments like Hadoop. Data can be easily reassembled from relational databases for use by the user. SAS/Access is not necessary if you are only pulling data from one database that you have the physical file for.