We performed a comparison between IBM SPSS Modeler and SAS Analytics based on real PeerSpot user reviews.
Find out what your peers are saying about Knime, Weka, IBM and others in Data Mining."Our go live process has been slightly enhanced compared to the previous programmatic process. There is now a faster time to production from the business end."
"It makes pretty good use of memory. There are algorithms take a long time to run in R, and somehow they run more efficiently in Modeler."
"A lot of jobs that are stuck in Excel due to the huge numbers of rows are tackled pretty quickly."
"So far, the stability has been rock solid."
"The supervised models are valuable. It is also very organized and easy to use."
"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 been able to do some predictive modeling with it"
"It's very easy to use. The drag and drop feature makes it very easy when you are building and testing the streams. That's very useful."
"It has improved the level of efficacy and validity of our reports."
"The team immediately resolves the issues."
"I like that it is quickly embedding interactive reports and dashboards into a website, Outlook Mail, or even a mobile app."
"SAS Analytics plays a vital role in enhancing our decision-making processes, particularly in areas such as customer segmentation and operational efficiency."
"The most valuable feature is the ability to handle large data sets."
"Modeling ones and figures, such as PROC LIFETEST, PROC LOGISTICS, PROC GPLOT. PROC FREQ and PROC MEANS, are also among the valuable features."
"They have provided virtually everything we have needed to accomplish our task, as well as continuously improving our accuracy."
"It has facilitated timely analysis results with quality work and meaningful output."
"I think mapping for geographic data would also be a really great thing to be able to use."
"We have run into a few problems doing some entity matching/analytics."
"It would be beneficial if the tool would include more well-known machine learning algorithms."
"I would like better integration into the Weather Company solution. I have raised a couple of concerns about this integration and having more time series capabilities."
"Expensive to deploy solutions. You need to buy an extra deployment unit."
"I would not rate the technical support very well. The technicians have accents. When you do find someone, it is very hard to get somebody able to answer the technical questions."
"The platform's cloud version needs improvements."
"The product does not have a search function for tags."
"The training for SAS Business Intelligence is often difficult to arrange. It is often cancelled due to not enough people being enrolled."
"The installation could also be easier, and the price could be better."
"The natural language querying and automated preparation of dashboards should be improved."
"They could enhance the AI capabilities of the product."
"One of the things that can be simplified is self-service analytics, especially for a citizen developer or a citizen data scientist."
"This solution should be made more user-friendly."
"There is potential for enhancement, particularly in the virtualized dashboard's capability to generate reports."
"Once a SAS figure is produced one would like to modify things, such as titles, legends, and incorporate risk sets as a footer on the plots."
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 KNIME, Microsoft Power BI, RapidMiner and IBM SPSS Statistics, whereas SAS Analytics is most compared with KNIME, IBM SPSS Statistics, Weka and SAS Enterprise Miner.
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