SAS Enterprise Miner Review 1 of 2
Review: "SAS Enterprise Miner brings your "SAS shop" to the next level with customer insight" Enterprise MinerUnspecified9.43101
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Updated January 14, 2019

Review: "SAS Enterprise Miner brings your "SAS shop" to the next level with customer insight"

Score 9 out of 101
Vetted Review
Verified User
Review Source

Overall Satisfaction with SAS Enterprise Miner

Enterprise Miner is being used mostly in the Risk department and marketing department. For Marketing, it is used to develop different marketing decision making algorithms (mostly cross-sell, upsell, churn and acquisition models). These tasks help answer the following questions:
  • What are my customers next best action (what product would they consider)
  • Who is more likely to leave for a competitor
  • Who is more likely to increase their volume of savings and by how much.
For Risk, the use mostly measures the exposure to loss of the bank regarding loans. It will help answer the following questions:
  • How much money can I safely lend customers based on several metrics such as credit score rating, assets, ... (credit scorecards)
  • In case of recessions or the collapse of a specific industry, what is the change of global exposure at default based on the change in risk (IFRS9 stresstesting)
  • What is the risk of customers with overdue payment to never pay what they owe (Bad debt)
  • Is a customer doing money laundering or terrorist financing (Anti-money laundering)
  • Enterprise Miner is really visual and lets you do a whole lot without actually going into the detailed options. For decent results, you should really explore the different advanced options though.
  • The recent versions of Miner allow users to use R code in Miner. You can then compare several models and approach to get the best performing model.
  • The resulting data is really well displayed and easy to understand (ex: the lift graph, score ranking, etc.)
  • Miner has the ability to integrate custom SAS code which allows the user to add functionalities that are specific to the project.
  • One of the major flaws is that the tool is basically an interface to SAS/STAT code. It generates code in the background and runs it. Because of that, some errors are warning might be a little difficult to understand for users who aren't proficient with SAS code.
  • R integration is nice but I would like to see the possibility to integrate even more statistical models different than SAS. That would allow for better performance optimization when really required.
  • The light client is java based and a little heavy on the OS. It would be nice to get a web-based version of the tool instead of the java one.
  • In our organization, users were using SAS already so the learning curve was really low. Within a few weeks after the implementation, the users were already delivering models developed with SAS Enterprise Miner. It is difficult to talk about ROI as models were already being developed before. It was mostly a change of technology and it was a smooth transition.
  • Going with Enterprise Miner came with migration from desktop use of SAS to a server use of SAS. This created a new role of SAS administrator. This was obviously a cost but as the use of SAS increased greatly, it was expected.
  • From a methodology standpoint, Enterprise Miner helped greatly in the documentation of the model development which was a requirement in a few groups such as the risk groups. Having a visual "GUI-like" approach to development, the flowchart or diagram of the project in Miner was able to give users a good understanding of the approach the analyst took to develop the model.
SPSS was used for model development before SAS in my organization. SAS brought a bigger more complete integrated solution than SPSS had.
It allowed users to easily prepare their data with SAS/Enterprise Guide and then use it with Enterprise Miner. The data preparation tools of SAS are really well integrated and I don't feel it was the case with SPSS (or at least from what I've heard).
This product might be suited where SAS is already present and widely used by analysts, where source data (for model development) is in SAS format (SAS table), or where you want to develop good models but maybe not the most optimal one. This product might not be a good fit if you barely have any SAS knowledge in the organization and don't wish to develop it, have other modeling software that you would like to integrate with it (other than R), or want a cheap and quick to implement solution for a small organization.

SAS Enterprise Miner Feature Ratings

Connect to Multiple Data Sources
Extend Existing Data Sources
Automatic Data Format Detection
MDM Integration
Not Rated
Interactive Data Analysis
Interactive Data Cleaning and Enrichment
Data Transformations
Data Encryption
Not Rated
Built-in Processors
Not Rated
Multiple Model Development Languages and Tools
Automated Machine Learning
Not Rated
Single platform for multiple model development
Self-Service Model Delivery
Not Rated
Flexible Model Publishing Options
Security, Governance, and Cost Controls

Using SAS Enterprise Miner

Users and Roles

30 - Data scientists and some senior BI analysts that have some statistical knowledge and understanding.

Support Headcount Required

4 - The support for Enterprise Miner is mostly a technical support. Software support is covered by co-workers and online queries.
For the technical support, it doesn't really require an additional person to cover that. In our case, the SAS admins support all SAS softwares and Miner is just one of them.