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 have full control of the data handling process."
"Very good data aggregation."
"It’s definitely scalable, it’s all on the same platform, it’s well integrated. I think the integration is important in terms of scalablility because essentially, having the entire suite helps a lot to scale it"
"In the solution, I like the virtualization of data flow since it shows what goes where, which is mostly the strength of the tool."
"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."
"Stability is good."
"I think it is the point and drag features that are the most valuable. You can simply click at the windows, and then pull up the functions."
"It continues to be a very flexible platform, so that it handles R and Python and other types of technology. It seems to be growing with additional open-source movement out there on different platforms."
"The technical support is very good."
"The most valuable feature is the decision tree creation."
"he solution is scalable."
"The solution is able to handle quite large amounts of data beautifully."
"The setup is straightforward. Deployment doesn't take more than 30 minutes."
"Good data management and analytics."
"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."
"I like the way the product visually shows the data pipeline."
"The product does not have a search function for tags."
"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."
"Unstructured data is not appropriate for SPSS Modeler."
"Requires more development."
"Regarding visual modeling, it is not the biggest strength of the product, although from what I hear in the latest release it's going to be a lot stronger. That, to me, has always been the biggest flaw in using this. It's very difficult to get good visualization."
"The standard package (personal) is not supported for database connection."
"It would be helpful if SPSS supported open-source features, for example, embedding R or Python scripts in SPSS Modeler."
"The challenge for the very technical data scientists: It is constraining for them."
"The ease of use can be improved. When you are new it seems a bit complex."
"While I don't personally need tutorials, I can't say that it wouldn't be helpful for others to have some to help them navigate and operate the system."
"Virtualization could be much better."
"Technical support could be improved."
"The solution needs an easier interface for the user. The user experience isn't so easy for our clients."
"The user interface of the solution needs improvement. It needs to be more visual."
"The solution is much more complex than other options."
"The product must provide better integration with cloud-native technologies."
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, Alteryx 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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We monitor all Data Mining reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
