We performed a comparison between IBM SPSS Modeler and SAP Predictive Analytics based on real PeerSpot user reviews.
Find out in this report how the two Data Science Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."It is a great product for running statistical analysis."
"Some basic form of feature engineering for classification models. This really quickens the model development process."
"New algorithms are added into every version of Modeler, e.g., SMOTE, random forest, etc. The Derive node is used for the syntax code to derive the data."
"Compared to other tools, the product works much easier to analyze data without coding."
"It's a very organized product. It's easy to use."
"The most valuable features of the IBM SPSS Modeler are visual programming, you don't have to write any code, and it is easy to use. 90 to 95 percent of the use cases, you don't have to fine-tune anything. If you want to do something deeper, for example, create a better neural network, then you have to go into the features and try to fine-tune them. However, the default selection which is made by the tool, it's very practical and works well."
"So far, the stability has been rock solid."
"We are using it either for workforce deployment or to improve our operations."
"I think the features of the actual ability to forecast and pull trends and correlations has been really good."
"The most valuable features are the analytics and reporting."
"It is very good, but slow. The slowness may be because we have not finalized all the background information in SPSS. It still needs some tweaking."
"C&DS will not meet our scalability needs."
"Unstructured data is not appropriate for SPSS Modeler."
"I can say the solution is outdated."
"Neural networks are quite simple, and now neural networks are evolving to these architecture related to deep learning, etc. They didn't incorporate this in IBM SPSS Modeler."
"We would like to see better visualizations and easier integration with Cognos Analytics for reporting."
"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."
"Formula writing is not straightforward for an Excel user. Totally new set of functions, which takes time to learn and teach."
"This solution works for acquired data but not live, real-time data."
Earn 20 points
IBM SPSS Modeler is ranked 12th in Data Science Platforms with 38 reviews while SAP Predictive Analytics is ranked 24th in Data Science Platforms. IBM SPSS Modeler is rated 8.0, while SAP Predictive Analytics is rated 8.6. 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 SAP Predictive Analytics writes "Easy to implement, good data forecasting and reporting". IBM SPSS Modeler is most compared with KNIME, Microsoft Power BI, RapidMiner and IBM SPSS Statistics, whereas SAP Predictive Analytics is most compared with IBM Watson Studio, Microsoft Azure Machine Learning Studio, Domino Data Science Platform and Alteryx. See our IBM SPSS Modeler vs. SAP Predictive Analytics report.
See our list of best Data Science Platforms vendors.
We monitor all Data Science Platforms 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.
