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."Very good data aggregation."
"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."
"We use analytics with the visual modeling capability to leverage productivity improvements."
"You take two quarters and compare them and this tool is ideal because it gives you a lot of visibility on the before and after."
"It works fine. I have not had any stability issues; it is always up."
"It is very scalable for non-technical people."
"It is pretty scalable."
"Automated modelling, classification, or clustering are very useful."
"The setup is straightforward. Deployment doesn't take more than 30 minutes."
"he solution is scalable."
"Good data management and analytics."
"The most valuable feature is that you can use multiple algorithms for creating models and then you can compare the results between them."
"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 found the ease of use of the solution the most valuable. Additionally, other valuable features include: the user interface, power to extract data, compatibility with other technologies (specifically with PS400), and automation of several tasks."
"I like the way the product visually shows the data pipeline."
"The technical support is very good."
"It would be good if IBM added help resources to the interface."
"Dimension reduction should be classified separately."
"The challenge for the very technical data scientists: It is constraining for them."
"It's not as user friendly as it could be."
"I can say the solution is outdated."
"The biggest issue with the visual modeling capability is that we can't extract the SQL code under the hood."
"The standard package (personal) is not supported for database connection."
"The forecasting could be a bit easier."
"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."
"The ease of use can be improved. When you are new it seems a bit complex."
"The initial setup is challenging if doing it for the first time."
"Technical support could be improved."
"Virtualization could be much better."
"The visualization of the models is not very attractive, so the graphics should be improved."
"The product must provide better integration with cloud-native technologies."
"The solution is very stable, but we do have some problems with discrepancies involving SAS not matching with the latest Java versions. It's not stable in cases where SAS tries to run on a different version because SAS doesn't connect with the latest Java update. Once a month we need to restart systems from scratch."
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, KNIME and SAS Analytics. See our IBM SPSS Modeler vs. SAS Enterprise Miner report.
See our list of best Data Mining vendors and best Data Science Platforms vendors.
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.
