We performed a comparison between IBM SPSS Modeler and SAS Analytics 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."It handles large data better than the previous system that we were using, which was basically Excel and Access. We serve upwards of 300,000 parts over a 150 regions and we need to crunch a lot of numbers."
"We are using it either for workforce deployment or to improve our operations."
"A lot of jobs that are stuck in Excel due to the huge numbers of rows are tackled pretty quickly."
"The visual modeling capability is one of its attractive features."
"Extremely easy to use, it offers a generous selection of proprietary machine learning algorithms."
"It works fine. I have not had any stability issues; it is always up."
"The ease of use in the user interface is the best part of it. The ability to customize some of my streams with R and Python has been very useful to me, I've automated a few things with that."
"It is a great product for running statistical analysis."
"It has improved the level of efficacy and validity of our reports."
"It is able to connect to all major platforms, and all the smaller platforms that I have come across."
"It's very easy to use once you learn it."
"All of the data analytics features in SAS Analytics are valuable to us since we're using them daily across our entire analytics team."
"I like that it is quickly embedding interactive reports and dashboards into a website, Outlook Mail, or even a mobile app."
"They have provided virtually everything we have needed to accomplish our task, as well as continuously improving our accuracy."
"The team immediately resolves the issues."
"I use SAS daily to analyze data, produce reports, and other outputs."
"The biggest issue with the visual modeling capability is that we can't extract the SQL code under the hood."
"Unstructured data is not appropriate for SPSS Modeler."
"I think mapping for geographic data would also be a really great thing to be able to use."
"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."
"The integration with sources and visualisation needs some improvement. The scalability needs improvement."
"The forecasting could be a bit easier."
"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."
"Requires more development."
"Support at universities used to be limited, but I hear this is changing."
"The graphing and visualization features could be enhanced, in my opinion. I would especially stress improving the visualization capabilities."
"This solution should be made more user-friendly."
"One of the things that can be simplified is self-service analytics, especially for a citizen developer or a citizen data scientist."
"There is potential for enhancement, particularly in the virtualized dashboard's capability to generate reports."
"The installation could also be easier, and the price could be better."
"The natural language querying and automated preparation of dashboards should be improved."
"I would like to see their interface to R added to either Base SAS or SAS Analytics."
IBM SPSS Modeler is ranked 4th in Data Mining with 38 reviews while SAS Analytics is ranked 5th in Data Mining with 11 reviews. IBM SPSS Modeler is rated 8.0, while SAS Analytics is rated 9.0. 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 Analytics writes "Provides comprehensive data analysis tools and functionalities, but its higher pricing and potential stability issues may present drawbacks". IBM SPSS Modeler is most compared with Microsoft Power BI, KNIME, IBM SPSS Statistics and RapidMiner, whereas SAS Analytics is most compared with KNIME, IBM SPSS Statistics, Weka and SAS Enterprise Miner. See our IBM SPSS Modeler vs. SAS Analytics report.
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