We performed a comparison between IBM Watson Studio and KNIME 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."For me, the valuable feature of the solution is the one that I used, which was Jupyter notebooks."
"Technical support is great. We have had weekly teleconferences with the technical people at IBM, and they have been fantastic."
"The main benefit is the ease of use. We see a lot of engineers in our site and customers that really like the way the tools are able to work with the people."
"It stands out for its substantial AI capabilities, offering a broad spectrum of features for crafting solutions that meet specific requirements."
"Stability-wise, it is a great tool."
"It is a very stable and reliable solution."
"It has a lot of data connectors, which is extremely helpful."
"The system's ability to take a look at data, segment it and then use that data very differently."
"It provides very fast problem solving and I don't need to do much coding in it. I just drag and drop."
"This solution is easy to use and it can be used to create any kind of model."
"The product is open-source and therefore free to use."
"I've tried to utilize KNIME to the fullest extent possible to replace Excel."
"There are a lot of connectors available in KNIME."
"The most valuable feature is the data wrangling, which is what I mainly use it for."
"From a user-friendliness perspective, it's a great tool."
"It has allowed us to easily implement advanced analytics into various processes."
"More features in data virtualization would be helpful. The solution could use an interactive dashboard that could make exploration easier."
"We would like to see it more web-based with more functionality."
"I want IBM's technical support team to provide more specific answers to queries."
"Some of the solutions are really good solutions but they can be a little too costly for many."
"The initial setup was complex."
"It's sometimes easy to get lost given the number of images the solution opens up when you click on the mouse and the amount of different tabs."
"I think maybe the support is an area where it lacks."
"We would like to see it less as one big, massive product, but more based on smaller services that we can then roll out to consumers."
"The predefined workflows could use a bit of improvement."
"The data visualization part is the area most in need of improvement."
"If they had a more structured training model it would be very helpful."
"There should be better documentation and the steps should be easier."
"KNIME is not scalable."
"Data visualization needs improvement."
"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."
"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."
IBM Watson Studio is ranked 10th in Data Science Platforms with 13 reviews while KNIME is ranked 4th in Data Science Platforms with 50 reviews. IBM Watson Studio is rated 8.2, while KNIME is rated 8.2. The top reviewer of IBM Watson Studio writes "A highly robust and well-documented platform that simplifies the complex world of AI". On the other hand, the top reviewer of KNIME writes "A low-code platform that reduces data mining time by linking script". IBM Watson Studio is most compared with Databricks, Azure OpenAI, Microsoft Azure Machine Learning Studio, Google Vertex AI and Amazon Comprehend, whereas KNIME is most compared with RapidMiner, Microsoft Power BI, Alteryx, Dataiku Data Science Studio and Weka. See our IBM Watson Studio vs. KNIME report.
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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.
