We performed a comparison between IBM SPSS Modeler and IBM SPSS Statistics 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."
"Some basic form of feature engineering for classification models. This really quickens the model development process."
"It is very scalable for non-technical people."
"We have full control of the data handling process."
"Very good data aggregation."
"You take two quarters and compare them and this tool is ideal because it gives you a lot of visibility on the before and after."
"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."
"Automated modelling, classification, or clustering are very useful."
"The SPSS interface is very accessible and user-friendly. It's really easy to get information in it. I've shared it with experts and beginners, and everyone can navigate it."
"The solution is very comprehensive, especially compared to Minitabs, which is considered more for manufacturing. However, whatever data you want to analyze can be handled with SPSS."
"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 most valuable features are the small learning curve and its ability to hold a lot of data."
"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."
"The most valuable feature is its robust statistical analysis capabilities."
"I've found the descriptive statistics and cross-tabs valuable. The very simple correlations and regressions are as well."
"The most valuable feature of IBM SPSS Statistics is all the functionality it provides. Additionally, it is simple to do the five-way analysis that you can into multidimensional setup space. It's the multidimensional space facility that is most useful."
"I understand that it takes some time to incorporate some of the new algorithms that have come out in the last few months, in the literature. For example, there is an algorithm based on how ants search for food. And there are some algorithms that have now been developed to complement rules. So that's one of the things that we need to have incorporated into it."
"The product does not have a search function for tags."
"Dimension reduction should be classified separately."
"We have run into a few problems doing some entity matching/analytics."
"Expensive to deploy solutions. You need to buy an extra deployment unit."
"It would be helpful if SPSS supported open-source features, for example, embedding R or Python scripts in SPSS Modeler."
"The integration with sources and visualisation needs some improvement. The scalability needs improvement."
"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."
"It could provide even more in the way of automation as there are many opportunities."
"Better documentation on how to use macros."
"Improvements are needed in the user interface, particularly in terms of user-friendliness."
"I feel that when it comes to conducting multiple analyses, there could be more detailed information provided. Currently, the software gives a summary and an overview, but it would be beneficial to have specific details for each product or variable."
"One of the areas that should be similar to Minitabs is the use of blogs. The Minitabs blog helps users understand the tools and gives lots of practical examples. Following the SPSS manual is cumbersome. It's a good, exhaustive manual, but it's not practical to use. With Minitabs, you can go to the blogs and find specific articles written about various components and it's very helpful. Without blogs, we find SPSS more complicated."
"The solution needs more planning tools and capabilities."
"The design of the experience can be improved."
"The solution could improve by providing a visual network for predictions and a self-organizing map for clustering."
IBM SPSS Modeler is ranked 4th in Data Mining with 38 reviews while IBM SPSS Statistics is ranked 3rd in Data Mining with 36 reviews. IBM SPSS Modeler is rated 8.0, while IBM SPSS Statistics is rated 8.0. 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 IBM SPSS Statistics writes "Enhancing survey analysis that provides valued insightfulness". IBM SPSS Modeler is most compared with Microsoft Power BI, KNIME, RapidMiner, Alteryx and SAS Visual Analytics, whereas IBM SPSS Statistics is most compared with Alteryx, TIBCO Statistica, Microsoft Azure Machine Learning Studio, Weka and Oracle Advanced Analytics. See our IBM SPSS Modeler vs. IBM SPSS Statistics 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.
