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."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."
"In the solution, I like the virtualization of data flow since it shows what goes where, which is mostly the strength of the tool."
"Our business units' capabilities with SPSS Modeler is high. They no longer waste time on modeling and algorithms, meaning they are not coding any more. For example, segmentation projects now take one to three months, rather than six months to a year, as before."
"We are creating models and putting them into production much faster than we would if we had just gone with a strict, code-based solution, like R or Python."
"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."
"We had an IBM Guardium service contract where we used one of their resources to help us develop our prototype. It was a good experience, but they were helpful and responsive."
"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"
"Extremely easy to use, it offers a generous selection of proprietary machine learning algorithms."
"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."
"Modeling ones and figures, such as PROC LIFETEST, PROC LOGISTICS, PROC GPLOT. PROC FREQ and PROC MEANS, are also among the valuable features."
"It has facilitated timely analysis results with quality work and meaningful output."
"They have provided virtually everything we have needed to accomplish our task, as well as continuously improving our accuracy."
"The technical support is okay."
"I like that it is quickly embedding interactive reports and dashboards into a website, Outlook Mail, or even a mobile app."
"I use it to replicate our entire financial system to verify/duplicate calculations."
"It has improved the level of efficacy and validity of our reports."
"The platform that you can deploy it on needs improvement because I think it is Windows only. I do not think it can run off a Red Hat, like the server products. I am pretty sure it is Windows and AIX only."
"Dimension reduction should be classified separately."
"Customer support is hard to contact."
"The time series should be improved."
"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."
"I would like see more programming languages added, like MATLAB. That would be better."
"I think mapping for geographic data would also be a really great thing to be able to use."
"Formula writing is not straightforward for an Excel user. Totally new set of functions, which takes time to learn and teach."
"This solution should be made more user-friendly."
"They could enhance the AI capabilities of the product."
"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."
"One of the things that can be simplified is self-service analytics, especially for a citizen developer or a citizen data scientist."
"Support at universities used to be limited, but I hear this is changing."
"I would like to see their interface to R added to either Base SAS or SAS Analytics."
"The installation could also be easier, and the price could be better."
"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 KNIME, Microsoft Power BI, RapidMiner, IBM SPSS Statistics and SAP Predictive Analytics, 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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