We performed a comparison between IBM Watson Studio and SAS Enterprise Miner 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 important thing is that it's a multi-faceted solution. It's a kind of specialist, not a generalist. It can produce very specific information for the customer. It's totally different from Google or any search engine that produces generic information. It's specialty is that it's all on video."
"Technical support is great. We have had weekly teleconferences with the technical people at IBM, and they have been fantastic."
"It has a lot of data connectors, which is extremely helpful."
"The scalability of IBM Watson Studio is great."
"Watson Studio is very stable."
"It has greatly improved the performance because it is standardized across the company."
"It is a stable, reliable product."
"It is a very stable and reliable solution."
"Good data management and analytics."
"The solution is able to handle quite large amounts of data beautifully."
"he solution is scalable."
"The setup is straightforward. Deployment doesn't take more than 30 minutes."
"I like the way the product visually shows the data pipeline."
"The most valuable feature is that you can use multiple algorithms for creating models and then you can compare the results between them."
"The solution is very good for data mining or any mining issues."
"The most valuable feature is the decision tree creation."
"Watson Studio would be improved with a clearer path for the deployment of docker images."
"Some of the solutions are really good solutions but they can be a little too costly for many."
"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 less as one big, massive product, but more based on smaller services that we can then roll out to consumers."
"The decision making in their decision making feature is less good than other options."
"I want IBM's technical support team to provide more specific answers to queries."
"Initially, it was quite complex. For us, it was not only a matter of getting it installed, that was just a start. It was also trying to come up with a standard way of implementing it across the entire organization, which had been a challenge."
"The main challenge lies in visibility and ease of use."
"The solution is very stable, but we do have some problems with discrepancies involving SAS not matching with the latest Java versions. It's not stable in cases where SAS tries to run on a different version because SAS doesn't connect with the latest Java update. Once a month we need to restart systems from scratch."
"While I don't personally need tutorials, I can't say that it wouldn't be helpful for others to have some to help them navigate and operate the system."
"The visualization of the models is not very attractive, so the graphics should be improved."
"The solution is much more complex than other options."
"Technical support could be improved."
"The ease of use can be improved. When you are new it seems a bit complex."
"The product must provide better integration with cloud-native technologies."
"Virtualization could be much better."
IBM Watson Studio is ranked 11th in Data Science Platforms with 13 reviews while SAS Enterprise Miner is ranked 15th in Data Science Platforms with 13 reviews. IBM Watson Studio is rated 8.2, while SAS Enterprise Miner is rated 7.6. 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 SAS Enterprise Miner writes "A stable product that is easy to deploy and can be used for structured and unstructured data mining". IBM Watson Studio is most compared with Databricks, Microsoft Azure Machine Learning Studio, Azure OpenAI, Google Vertex AI and Amazon Comprehend, whereas SAS Enterprise Miner is most compared with SAS Visual Analytics, IBM SPSS Modeler, RapidMiner, Microsoft Azure Machine Learning Studio and SAS Analytics. See our IBM Watson Studio vs. SAS Enterprise Miner report.
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