We performed a comparison between Google Cloud Datalab and IBM SPSS Statistics 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 MLOps, when we are designing the data pipeline, the designing of the data pipeline is easy in Google Cloud."
"Google Cloud Datalab is very customizable."
"All of the features of this product are quite good."
"The APIs are valuable."
"The infrastructure is highly reliable and efficient, contributing to a positive experience."
"Custom tables and macros: They allow us to create useful reports quickly for a broad audience."
"The most valuable features are the solution is easy to use, training new users is not difficult, and our usage is comprehensive because the whole service is beneficial."
"Since we are using the software as a statistical tool, I would say the best aspects of it are the regression and segmentation capabilities. That said, I've used it for all sorts of things."
"The learning curve to using this product is not steep. The program is appropriate for those who do not have a lot of background in programming, yet have to perform basic statistical analysis."
"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."
"The most valuable feature of IBM SPSS Statistics is all the functionality it provides. Additionally, it is simple to do the five-way analysis that you can into multidimensional setup space. It's the multidimensional space facility that is most useful."
"The solution has numerous valuable features. We particularly like custom tabs. It's very useful. We end up analyzing a lot of software data, so features related to custom tabs are really helpful."
"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 interface should be more user-friendly."
"We have also encountered challenges during our transition period in terms of data control and segmentation. The management of each channel and data structure as it has its own unique characteristics requires very detailed and precise control. The allocation should be appropriate and the complexity increases due to the different time zones and geographic locations of our clients. The process usually involves migrating the existing database sets to gcp and ensure data integrity is maintained. This is the only challenge that we faced while navigating the integers of the solution and honestly it was an interesting and unique experience."
"The product must be made more user-friendly."
"There is room for improvement in the graphical user interface. So that the initial user would use it properly, that would be a good option."
"Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience."
"It could provide even more in the way of automation as there are many opportunities."
"The reports could be better."
"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."
"In some cases, the product takes time to load a large dataset. They could improve this particular area."
"Needs more statistical modelling functions."
"IBM SPSS Statistics could improve the visual outputs where you are producing, for example, a graph for a company board of directors, or an advert."
"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."
"Better documentation on how to use macros."
Google Cloud Datalab is ranked 15th in Data Science Platforms with 5 reviews while IBM SPSS Statistics is ranked 7th in Data Science Platforms with 36 reviews. Google Cloud Datalab is rated 7.6, while IBM SPSS Statistics is rated 8.0. The top reviewer of Google Cloud Datalab writes "Easy to setup, stable and easy to design data pipelines". On the other hand, the top reviewer of IBM SPSS Statistics writes "Enhancing survey analysis that provides valued insightfulness". Google Cloud Datalab is most compared with Databricks, Cloudera Data Science Workbench, IBM SPSS Modeler, KNIME and Qlik Sense, whereas IBM SPSS Statistics is most compared with Alteryx, TIBCO Statistica, Microsoft Azure Machine Learning Studio, IBM SPSS Modeler and Oracle Advanced Analytics. See our Google Cloud Datalab vs. IBM SPSS Statistics report.
See our list of best Data Science Platforms vendors.
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.