We performed a comparison between IBM SPSS Statistics and Microsoft Azure Machine Learning Studio 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."in terms of the simplicity, I think the SPSS basic can handle it."
"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."
"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 software offers consistency across multiple research projects helping us with predictive analytics capabilities."
"SPSS is quite robust and quicker in terms of providing you the output."
"You can quickly build models because it does the work for you."
"IBM SPSS Statistics depends on AI."
"It has helped our analyst unit deliver work with more transparency and confidence, given that we can always view the dataset in totality, after each step of data transformation."
"The solution is integrated with our Microsoft Azure tenant, and we don't have to go anywhere else outside the tenant."
"Their support is helpful."
"The graphical nature of the output makes it very easy to create PowerPoint reports as well."
"Visualisation, and the possibility of sharing functions are key features."
"Its ability to publish a predictive model as a web based solution and integrate R and python codes are amazing."
"The product's standout feature is a robust multi-file network with limited availability."
"It's easy to use."
"The solution is very fast and simple for a data science solution."
"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."
"The design of the experience can be improved."
"SPSS slows down the computer or the laptop if the data is huge; then you need a faster computer."
"In some cases, the product takes time to load a large dataset. They could improve this particular area."
"Needs more statistical modelling functions."
"Most of the package will give you the fixed value, or the p-value, without an explanation as to whether it it significant or not. Some beginners might need not just the results, but also some explanation for them."
"In developing countries, it would be beneficial to provide certain features to users at no cost initially, while also customizing pricing options."
"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."
"The product must improve its documentation."
"Stability-wise, you may face certain problems when you fail to refresh the data in the solution."
"The price of the solution has room for improvement."
"Operability with R could be improved."
"In the Machine Learning Studio, particularly the Designer part, which is essentially Azure's demo designer, there is room for improvement. Many customers and users tend to switch to Microsoft Azure Multi-Joiners, which is a more basic version, but they do so internally. One area that could use enhancement is the process of connecting components. Currently, every time you want to connect a component, such as linking it to your storage or an instance like EC2, you have to input your username and password repeatedly. This can be quite cumbersome. Google, for instance, has made it more user-friendly by allowing easy access for connecting services within a workspace. In a workspace, you can set up various resources like storage, a database cluster, machine learning studio, and more. When connecting these services, there's no need to enter your username and password each time, making it a more efficient process. Another aspect to consider is the role of the designer, and they were to integrate a large language model to handle various tasks, it could significantly enhance the overall scalability and usability of the platform."
"The solution should be more customizable. There should be more algorithms."
"In terms of data capabilities, if we compare it to Google Cloud's BigQuery, we find a difference. When fetching data from web traffic, Google can do a lot of processing with small queries or functions."
"This solution could be improved if they could integrate the data pipeline scheduling part for their interface."
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IBM SPSS Statistics is ranked 7th in Data Science Platforms with 36 reviews while Microsoft Azure Machine Learning Studio is ranked 2nd in Data Science Platforms with 53 reviews. IBM SPSS Statistics is rated 8.0, while Microsoft Azure Machine Learning Studio is rated 7.6. The top reviewer of IBM SPSS Statistics writes "Enhancing survey analysis that provides valued insightfulness". On the other hand, the top reviewer of Microsoft Azure Machine Learning Studio writes "Good support for Azure services in pipelines, but deploying outside of Azure is difficult". IBM SPSS Statistics is most compared with Alteryx, TIBCO Statistica, IBM SPSS Modeler, Weka and Oracle Advanced Analytics, whereas Microsoft Azure Machine Learning Studio is most compared with Google Vertex AI, Databricks, Azure OpenAI, TensorFlow and SAS Visual Analytics. See our IBM SPSS Statistics vs. Microsoft Azure Machine Learning Studio report.
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