We performed a comparison between IBM SPSS Statistics and RapidMiner 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."The most valuable features are the small learning curve and its ability to hold a lot of data."
"It has the ability to easily change any variable in our research."
"Capability analysis is one of the main and valuable functions. We also do some hypothesis testing in Minitab and summary stats. These are the functions that we find very useful."
"The features that I have found most valuable are the Bayesian statistics and descriptive statistics."
"They have many existing algorithms that we can use and use effectively to analyze and understand how to put our data to work to improve what we do."
"Some of the most valuable features that we are using with some business models are machine learning algorithms, statistical models given to us by the business, and getting data from the database or text files."
"You can find a complete algorithm in the solution and use it. You don't need to write your own algorithms for predictive analytics. That's the most valuable feature and the main one we use."
"I've found the descriptive statistics and cross-tabs valuable. The very simple correlations and regressions are as well."
"It's helpful if you want to make informed decisions using data. We can take the information, tease out the attributes, and label everything. It's suitable for profiling and forecasting in any industry."
"The documentation for this solution is very good, where each operator is explained with how to use it."
"I've been using a lot of components from the Strategic Extension and Python Extension."
"Using the GUI, I can have models and algorithms drag and drop nodes."
"Scalability is not really a concern with RapidMiner. It scales very well and can be used in global implementations."
"The data science, collaboration, and IDN are very, very strong."
"The most valuable features are the Binary classification and Auto Model."
"It is easy to use and has a huge community that I can rely on for help. Moreover, it is interactive."
"I'd like to see them use more artificial intelligence. It should be smart enough to do predictions and everything based on what you input."
"The technical support should be improved."
"The design of the experience can be improved."
"There is a learning curve; it's not very steep, but there is one."
"The solution needs to improve forecasting using time series analysis."
"Improvements are needed in the user interface, particularly in terms of user-friendliness."
"I think the visualization and charting should be changed and made easier and more effective."
"Technical support needs some improvement, as they do not respond as quickly as we would like."
"RapidMiner would be improved with the inclusion of more machine learning algorithms for generating time-series forecasting models."
"Improve the online data services."
"In terms of the UI and SaaS, the user interface with KNIME is more appealing than RapidMiner."
"It would be helpful to have some tutorials on communicating with Python."
"The price of this solution should be improved."
"I would appreciate improvements in automation and customization options to further streamline processes."
"The visual interface could use something like the-drag-and-drop features which other products already support. Some additional features can make RapidMiner a better tool and maybe more competitive."
"The biggest problem, not from a platform process, but from an avoidance process, is when you work in a heavily regulated environment, like banking and finance. Whenever you make a decision or there is an output, you need to bill it as an avoidance to the investigator or to the bank audit team. If you made decisions within this machine learning model, you need to explain why you did so. It would better if you could explain your decision in terms of delivery. However, this is an issue with all ML platforms. Many companies are working heavily in this area to help figure out how to make it more explainable to the business team or the regulator."
IBM SPSS Statistics is ranked 8th in Data Science Platforms with 36 reviews while RapidMiner is ranked 7th in Data Science Platforms with 19 reviews. IBM SPSS Statistics is rated 8.0, while RapidMiner is rated 8.6. The top reviewer of IBM SPSS Statistics writes "Enhancing survey analysis that provides valued insightfulness". On the other hand, the top reviewer of RapidMiner writes "Offers good tutorials that make it easy to learn and use, with a powerful feature to compare machine learning algorithms". IBM SPSS Statistics is most compared with Alteryx, TIBCO Statistica, Microsoft Azure Machine Learning Studio, IBM SPSS Modeler and Weka, whereas RapidMiner is most compared with KNIME, Alteryx, Dataiku Data Science Studio, Tableau and Microsoft Azure Machine Learning Studio. See our IBM SPSS Statistics vs. RapidMiner report.
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