We performed a comparison between IBM SPSS Modeler and KNIME 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."It's a very organized product. It's easy to use."
"Very good data aggregation."
"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"
"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."
"We have full control of the data handling process."
"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."
"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."
"What I like the most is that it works almost out of the box with Random Forest and other Forest nodes."
"Since KNIME is a no-code platform, it is easy to work with."
"One of the greatest advantages of KNIME is that it can be used by those without any coding experience. those with no coding background can use it."
"This solution is easy to use and it can be used to create any kind of model."
"It has allowed us to easily implement advanced analytics into various processes."
"The most valuable feature is the data wrangling, which is what I mainly use it for."
"It allows for a user-friendly approach where you can simply drag and drop elements to create your model, which is a convenient and effective idea."
"Stability is excellent. I would give it a nine out of ten."
"Customer support is hard to contact."
"I can say the solution is outdated."
"The challenge for the very technical data scientists: It is constraining for them."
"Regarding visual modeling, it is not the biggest strength of the product, although from what I hear in the latest release it's going to be a lot stronger. That, to me, has always been the biggest flaw in using this. It's very difficult to get good visualization."
"I think mapping for geographic data would also be a really great thing to be able to use."
"Expensive to deploy solutions. You need to buy an extra deployment unit."
"The biggest issue with the visual modeling capability is that we can't extract the SQL code under the hood."
"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 license is quite expensive for us."
"I would like it to have data visualitation capabilities. Today I'm still creating my own data visualtions tools to present my reports."
"KNIME's licensing and data management aren't as straightforward relative to Alteryx. Alteryx's tools are more sophisticated, so you need fewer to use it compared to KNIME. I think tab implementation could be easier, too."
"The dynamic column name feature could be improved. When attempting to automate processes involving columns, such as with companies, it becomes difficult to achieve the same result when we make changes."
"I would like to see better web scraping because every time I tried, it was not up to par, although you can use Python script."
"Though I can use KNIME in a 64-bit platform in the lab, it's missing some features. For example, from my laptop, I can use the image reader feature of KNIME. However, in the lab, the image reader node is missing."
"It's very general in terms of architecture, and as a result, it doesn't support efficient running. That is, the speed needs to be improved."
"The visualization functionalities are not good (cannot be compared to, for instance, the possibilities in R)."
IBM SPSS Modeler is ranked 4th in Data Mining with 38 reviews while KNIME is ranked 1st in Data Mining with 50 reviews. IBM SPSS Modeler is rated 8.0, while KNIME is rated 8.2. 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 KNIME writes "A low-code platform that reduces data mining time by linking script". IBM SPSS Modeler is most compared with Microsoft Power BI, IBM SPSS Statistics, RapidMiner, Alteryx and SAS Visual Analytics, whereas KNIME is most compared with RapidMiner, Microsoft Power BI, Alteryx, Dataiku and Databricks. See our IBM SPSS Modeler vs. KNIME report.
See our list of best Data Mining vendors and best Data Science Platforms vendors.
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.
KNIME. It free, open-source, and you can plug in Java, Python, R, and Matlab. The community is awesome.
I used IBM Modeler several years ago and found it to be effective, but expensive. Fortunately, it was for a commercially funded contract.
For KNIME I have only used it for experimental purposes and found it rather cumbersome but powerful. It is also more cost-effective.
I found RapidMiner more intuitive to learn. However, there is so much choice nowadays that it is difficult to be definitive. In my experience, it largely depended on the quality of the add-on extensions. Clearly, though, at least in universities, the cost is a significant factor.
I am not familiar with KNIME, but the main difference is KNIME is open-source and free.