SAS/ACCESS is a must for the every day SAS data analyst.
February 22, 2018
SAS/ACCESS is a must for the every day SAS data analyst.
Score 8 out of 10
Vetted Review
Verified User
Overall Satisfaction with SAS/Access
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.
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.
- SAS supports the main database connection options that allow you to optimize the performance of your extracts and loads.
- Simplicity of the syntax for a basic connection.
- Ability to configure by an administrator in a BI environment so that all users can benefit from the connection without having to establish it by themselves.
- Easier management in the administration platform. Connecting these can be a challenge.
- Hard to say because it's a part of a global solution. Definetaly help reduce cost by allowing users to use only one tool and not 2.
- Datastage
Datastage might be the closest one. Being a full ETL tool, it's weird to compare both. Datastage might be more robust for extraction but it lacks the simplicity that the end users need for everyday data extract and analysis.