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."The quality is very good."
"It helped me in that I didn't need to write them by hand, and I could get a result in one or two minutes. That helped me a lot."
"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."
"We have a local representative who specializes in SPSS. He will help us do the PoC."
"We are using it either for workforce deployment or to improve our operations."
"It scales. I have not run into any challenges where it will not perform."
"We have been able to do some predictive modeling with it"
"It will scale up to anything we need."
"I use it to replicate our entire financial system to verify/duplicate calculations."
"SAS Business Intelligence is well-suited for our large corporation. We have demand for scalable and reliable insights into information which is housed in our large systems."
"It has improved the level of efficacy and validity of our reports."
"All of the data analytics features in SAS Analytics are valuable to us since we're using them daily across our entire analytics team."
"The team immediately resolves the issues."
"SAS Analytics plays a vital role in enhancing our decision-making processes, particularly in areas such as customer segmentation and operational efficiency."
"It has also been around for an extremely long time, has a strong history, and good market penetration."
"I like that it is quickly embedding interactive reports and dashboards into a website, Outlook Mail, or even a mobile app."
"It is not integrated with Qlik, Tableau, and Power BI."
"The forecasting could be a bit easier."
"Requires more development."
"I would like see more programming languages added, like MATLAB. That would be better."
"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."
"I can say the solution is outdated."
"I think mapping for geographic data would also be a really great thing to be able to use."
"It would be helpful if SPSS supported open-source features, for example, embedding R or Python scripts in SPSS Modeler."
"I would like to see their interface to R added to either Base SAS or SAS Analytics."
"There is potential for enhancement, particularly in the virtualized dashboard's capability to generate reports."
"The graphing and visualization features could be enhanced, in my opinion. I would especially stress improving the visualization capabilities."
"They could enhance the AI capabilities of the product."
"The installation could also be easier, and the price could be better."
"This solution should be made more user-friendly."
"The training for SAS Business Intelligence is often difficult to arrange. It is often cancelled due to not enough people being enrolled."
"The natural language querying and automated preparation of dashboards should be improved."
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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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.
