SAS Enterprise Miner is my favorite modeling software. Does the job well.
SAS Miner is used across the organization. The software is used for two main purposes. First, SAS Enterprise Miner is perhaps the most …
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What is SAS Enterprise Miner?
SAS Enterprise Miner is a data science and statistical modeling solution enabling the creation of predictive and descriptive models on very large data sources across the organization.
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What is SAS Enterprise Miner?
SAS Enterprise Miner is a data science and statistical modeling solution enabling the creation of predictive and descriptive models on very large data sources across the organization.
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What is SAS Enterprise Miner?
SAS Enterprise Miner is a data science and statistical modeling solution enabling the creation of predictive and descriptive models on very large data sources across the organization.
SAS Enterprise Miner Technical Details
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(1-5 of 5)Companies can't remove reviews or game the system. Here's why
SAS Miner is used across the organization. The software is used for two main purposes. First, SAS Enterprise Miner is perhaps the most useful software in the world for ensemble modeling. Second, SAS Enterprise Miner makes producing reproducible models in an efficient structure about as easy as it can get.
- Developing and evaluating ensemble models.
- A very transparent interface.
- SAS Enterprise Miner is far from the fastest software out there
- Integrating SAS Enterprise Miner with other software tools is not easy.
87.5%
8.8
90%
9.0
85%
8.5
92.5%
9.3
80%
8.0
- SAS Enterprise Miner is a positive ROI in the sense that it saves a ton of time coding.
- SAS Enterprise Miner is a negative ROI in that it's expensive, and perhaps makes analysts brainless.
For those that are used to the SAS ecosystem, SAS Enterprise Miner is a massive move in the right direction. It makes doing analytics much more enjoyable. It is more user-friendly than Spotfire or Kinesis and seems to produce better results overall. SAS Enterprise Miner seems to be written by analysts for analysts.
November 13, 2019
I can't live without SAS Enterprise Miner! A+++++
I use SAS Enterprise Miner regularly to conduct data analysis for articles published in academic journals. It is great respondings software that has multiple options. I mostly use Text Miner as a tool to uncover themes in large amounts of unstructured data. It rarely crashes and is able to handle very large files.
- Enterprise Miner has many different data analysis options.
- It is easy to use and very reliable.
- The algorithms behind this statistical software are world class.
- SAS is not as user friendly as other stats software.
67.5%
6.8
70%
7.0
32.5%
3.3
57.5%
5.8
75%
7.5
- It has made me a more productive scholar enabling me to interact with a wider range of academic disciplines.
- SAS is always well respected in the journal I publish in.
- It is very reasonable to get a yearly license through the university where I work.
- PITA to re-install.
I like the algorithms SAS uses better than SPSS. I have been writing SAS code since the mid 1980s and trust their development team. Also offer great refresher class to academics.
SAS Enterprise Miner is used by my department for smaller machine learning models (clustering) and predictive analytics (future churn rate calculation), as well as data exploration and pattern analysis. We use the tool as well as recommend it for clients to help them improve business decision making (e.g loan decision making) and internal efficiencies.
- Very easy to use and intuitive.
- High performance.
- Open source integration with R.
- Amazing data science models.
- Very good data preparation and exploration toolkit.
- Still the same, very old and clunky GUI.
- For smaller organizations, it can be quite pricey.
- For less experienced users, the software can be a little overwhelming.
87.5%
8.8
70%
7.0
90%
9.0
80%
8.0
80%
8.0
- It has a positive ROI to our business, as our sales lead rate increased after we started recommending SAS EM.
- Our business operation numbers improved after we introduced SAS EM and started using predictive analytics for our customer retention and customer chain prediction.
- The statistical modelling for the risk controls in our financial department helped to reduce the related residual risk.
SAS EM has a very great set of machine learning and predictive analytics toolsets, which helped our organization achieve its goals. We used other tools, but for us, SAS EM was the most intuitive and easy to learn the tool and it provides greater data exploration and data preparation capabilities compared to the other tools we used.
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.
- 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.
65%
6.5
70%
7.0
35%
3.5
27.5%
2.8
40%
4.0
- 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).
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).
30
Data scientists and some senior BI analysts that have some statistical knowledge and understanding.
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.
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.
SAS Enterprise Miner is being used across the department for predictive analytics and advanced modeling. I think the flow diagrams make SAS Enterprise Miner the best software out there for producing reproducible forecasts and analytics across multiple users. In addition to predictive modeling, there is also advanced modeling comprising the machine learning, logit, ensemble modeling, and other techniques.
- The flow diagram combined with point-and-click make SAS Enterprise Miner easy to learn and apply in practice.
- The ability to convert the drag-and-drop models into actual SAS code makes customization easier, although not foolproof-easy.
- I think perhaps the most desirable aspect of SAS Enterprise Miner is its ability to create ensemble models, and then the straightforward way of evaluating ensemble modeling.
- With large data sets, SAS Enterprise Miner sometimes takes a long time to run. Sometimes you have to just leave your computer running while Enterprise Miner does its thing.
- If you want complete control over the modeling framework, you have to take what Enterprise Miner does and customize it. SAS seems to be working hard on making things easier to customize, but it's not completely there yet.
- The graphic capabilities of SAS Enterprise Miner leave a lot to be desired, especially in the era of self-service business intelligence software.
87.5%
8.8
80%
8.0
82.5%
8.3
90%
9.0
80%
8.0
- SAS Enterprise Miner improved our forecasting models. I don't think competing software could have accomplished what we did with SAS Enterprise Miner in as little time.
- SAS Enterprise Miner isn't cheap. The high cost makes it harder to justify the return on investment. Luckily for us, SAS Enterprise Miner makes it so easy to perform machine learning and ensemble models that the costs were justified.
- Sometimes it takes time figuring out what SAS Enterprise Miner is doing. If you're a newbie to analytics, SAS Enterprise Miner is a great starting point as long as you're willing to figure out what SAS is doing. Taking Enterprise Miner's word for it didn't work sometimes.
- Tableau Server, QlikView, SAS Advanced Analytics, SAS Enterprise Guide, Sisense, Alteryx Analytics, Microsoft Access, Microsoft Azure, AWS Cloud9, TIBCO Spotfire and TIBCO Spotfire Data Science (previously Alpine Data)
I think SAS Enterprise Miner has three advantages. The software is the easiest of any to implement ensemble modeling and other machine learning models. The second advantage is the ability to connect Enterprise Miner activities to other software. The third advantage is the ease of reproducing results. On the con side, Enterprise Miner is expensive and requires a commitment to figure out SAS black box.