We performed a comparison between IBM SPSS Modeler and SAS Enterprise Miner based on real PeerSpot user reviews.
Find out in this report how the two Data Mining solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."We are creating models and putting them into production much faster than we would if we had just gone with a strict, code-based solution, like R or Python."
"We have integration where you can write third-party apps. This sort of feature opens it up to being able to do anything you want."
"I think the code modeling features are the most valuable and without the need to write a code back with many different possibilities to choose from. And the second one is linked to the activity of the data preparation."
"In the solution, I like the virtualization of data flow since it shows what goes where, which is mostly the strength of the tool."
"The most valuable features of the IBM SPSS Modeler are visual programming, you don't have to write any code, and it is easy to use. 90 to 95 percent of the use cases, you don't have to fine-tune anything. If you want to do something deeper, for example, create a better neural network, then you have to go into the features and try to fine-tune them. However, the default selection which is made by the tool, it's very practical and works well."
"It is just a lot faster. So you do not have to write a bunch of code, you can throw that stuff on there pretty quickly and do prototyping quickly."
"Very good data aggregation."
"The quality is very good."
"The solution is very good for data mining or any mining issues."
"Most of the features, especially on the data analysis tool pack, are really good. The way they do clustering and output is great. You can do fairly elaborate outputs. The results, the ensembles, all of these, are fantastic."
"The most valuable feature is the decision tree creation."
"The setup is straightforward. Deployment doesn't take more than 30 minutes."
"The solution is able to handle quite large amounts of data beautifully."
"I like the way the product visually shows the data pipeline."
"The technical support is very good."
"he solution is scalable."
"C&DS will not meet our scalability needs."
"We would like to see better visualizations and easier integration with Cognos Analytics for reporting."
"Time Series or forecasting needs to be easier. It is a very important feature, and it should be made easier and more automated to use. For instance, for logistic regression, binary or multinomial is used automatically based on the type of the target variable. I wish they can make Time Series easier to use in a similar way."
"It would be good if IBM added help resources to the interface."
"The platform that you can deploy it on needs improvement because I think it is Windows only. I do not think it can run off a Red Hat, like the server products. I am pretty sure it is Windows and AIX only."
"Customer support is hard to contact."
"I understand that it takes some time to incorporate some of the new algorithms that have come out in the last few months, in the literature. For example, there is an algorithm based on how ants search for food. And there are some algorithms that have now been developed to complement rules. So that's one of the things that we need to have incorporated into it."
"When I used it in the office, back in the day, we did have some stability issues. Sometimes it just randomly crashed and we couldn't get good feedback. But when I use it for my own stuff now I don't have any problems."
"The solution needs an easier interface for the user. The user experience isn't so easy for our clients."
"The solution is much more complex than other options."
"Virtualization could be much better."
"The initial setup is challenging if doing it for the first time."
"The ease of use can be improved. When you are new it seems a bit complex."
"The visualization of the models is not very attractive, so the graphics should be improved."
"Technical support could be improved."
"The user interface of the solution needs improvement. It needs to be more visual."
IBM SPSS Modeler is ranked 4th in Data Mining with 38 reviews while SAS Enterprise Miner is ranked 6th in Data Mining with 13 reviews. IBM SPSS Modeler is rated 8.0, while SAS Enterprise Miner is rated 7.6. The top reviewer of IBM SPSS Modeler writes "Easy to use, quick to learn, and offers many ways to analyze data". On the other hand, the top reviewer of SAS Enterprise Miner writes "A stable product that is easy to deploy and can be used for structured and unstructured data mining". IBM SPSS Modeler is most compared with KNIME, Microsoft Power BI, RapidMiner, IBM SPSS Statistics and Microsoft Azure Machine Learning Studio, whereas SAS Enterprise Miner is most compared with SAS Visual Analytics, RapidMiner, Microsoft Azure Machine Learning Studio, SAS Analytics and Alteryx. See our IBM SPSS Modeler vs. SAS Enterprise Miner report.
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