We performed a comparison between IBM SPSS Statistics 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."The most valuable feature is the user interface because you don't need to write code."
"You can quickly build models because it does the work for you."
"The best part is that they have an algorithm handbook, so you can open it up and understand how it works, and if it is useful, this is very important."
"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."
"It has the ability to easily change any variable in our research."
"I've found the descriptive statistics and cross-tabs valuable. The very simple correlations and regressions are as well."
"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."
"Easy to connect with every database: We use queries from SQL, Redshift, Oracle."
"Overall KNIME serves its purpose and does a good job."
"What I like the most is that it works almost out of the box with Random Forest and other Forest nodes."
"We can deploy the solution in a cluster as well."
"The most valuable features of KNIME are its ability to convert your sub-workflow into a node. For example, the workflow has many individual native nodes that can be converted into a single node. This representation has simplified my workflow to a great extent. I can present my workflow in a very compact way."
"I've tried to utilize KNIME to the fullest extent possible to replace Excel."
"The solution allows for sharing model designs and model operations with other data analysts."
"The product is open-source and therefore free to use."
"If there is any self-generation data collection plan (DCP), it would be helpful in gathering data. It would also be useful if there is a function to scale it up to, let's say, UiPath and have it consolidate and integrate into a UiPath solution."
"It would be helpful if there was better documentation on how to properly use the solution. A beginner's guide on how to use the various programming functions within the product would be so useful to a lot of people. I found that everything was very confusing at first. Having clear documentation would help alleviate that."
"SPSS slows down the computer or the laptop if the data is huge; then you need a faster computer."
"Perhaps in terms of visualization. It's not really easy to do some data visualization, just simple, descriptive analysis in SPSS. I think that could be an area for improvement."
"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."
"I know that SPSS is a statistical tool but it should also include a little bit of analytical behavior. You can call it augmented analysis or predictive analysis. The bottom line is it should have more graphical and analytical capabilities."
"Technical support needs some improvement, as they do not respond as quickly as we would like."
"Improvements are needed in the user interface, particularly in terms of user-friendliness."
"It needs more examples, use cases, and MOOC to learn, especially with respect to the algorithms and how to practically create a flow from end-to-end."
"The data visualization part is the area most in need of improvement."
"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."
"The main issue with KNIME is that it sometimes uses too much CPU and RAM when working with large amounts of data."
"It's pretty straightforward to understand. So, if you understand what the pipeline is, you can use the drag-and-drop functionality without much training. Doing the same thing in Python requires so much more training. That's why I use KNIME."
"Data visualization needs improvement."
"KNIME can improve by adding more automation tools in the query, similar to UiPath or Blue Prism. It would make the data collection and cleanup duties more versatile."
IBM SPSS Statistics is ranked 3rd in Data Mining with 36 reviews while KNIME is ranked 1st in Data Mining with 50 reviews. IBM SPSS Statistics is rated 8.0, while KNIME is rated 8.2. The top reviewer of IBM SPSS Statistics writes "Enhancing survey analysis that provides valued insightfulness". On the other hand, the top reviewer of KNIME writes "A low-code platform that reduces data mining time by linking script". IBM SPSS Statistics is most compared with Alteryx, TIBCO Statistica, Microsoft Azure Machine Learning Studio and IBM SPSS Modeler, whereas KNIME is most compared with RapidMiner, Microsoft Power BI, Alteryx, Weka and Dataiku Data Science Studio. See our IBM SPSS Statistics vs. KNIME report.
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