Anonymous UserAssistant Director-Data Analytics at a financial services firm
Mike TurekVice President, Business Analysis & Performance at Starboard Cruise Services
We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
"Most of the product features are good but I particularly like the linear regression analysis."
"Some of the most valuable features that we are using with some business models are machine learning algorithms, statistical models given to us by the business, and getting data from the database or text files."
"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."
"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."
"They have many existing algorithms that we can use and use effectively to analyze and understand how to put our data to work to improve what we do."
"It has the ability to easily change any variable in our research."
"The most valuable feature is the user interface because you don't need to write code."
"In terms of the features I've found most valuable, I'd say the duration, the correlation, and of course the nonparametric statistics. I use it for reliability and survival analysis, time series, regression models in different solutions, and different types of solutions."
"The most valuable feature is the ability to handle large data sets."
"The technical support is okay."
"It's very easy to use once you learn it."
"I think the visualization and charting should be changed and made easier and more effective."
"Technical support needs some improvement, as they do not respond as quickly as we would like."
"The statistics should be more self-explanatory with detailed automated reports."
"Each algorithm could be more adaptable to some industry-specific areas, or, in some cases, adapted for maintenance."
"The product should provide more ways to import data and export results that are user-friendly for high-level executives."
"The design of the experience can be improved."
"This solution is not suitable for use with Big Data."
"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."
"This solution should be made more user-friendly."
"One of the things that can be simplified is self-service analytics, especially for a citizen developer or a citizen data scientist."
"The installation could also be easier, and the price could be better."
"We think that IBM SPSS is expensive for this function."
"The price of this solution is a little bit high, which was a problem for my company."
"The pricing of the modeler is high and can reduce the utility of the product for those who can not afford to adopt it."
"I think that the cost-benefit ratio is okay."
"SAS is very expensive."
IBM SPSS Statistics is ranked 2nd in Data Mining with 15 reviews while SAS Analytics is ranked 6th in Data Mining with 3 reviews. IBM SPSS Statistics is rated 8.0, while SAS Analytics is rated 9.0. The top reviewer of IBM SPSS Statistics writes "Offers good Bayesian and descriptive statistics". On the other hand, the top reviewer of SAS Analytics writes "A user-friendly, easy coding analytics solution that is good for typical predictive analytics". IBM SPSS Statistics is most compared with IBM SPSS Modeler, TIBCO Statistica, Weka, MathWorks Matlab and Alteryx, whereas SAS Analytics is most compared with KNIME, IBM SPSS Modeler, Oracle Advanced Analytics, IBM Watson Explorer and Weka. See our IBM SPSS Statistics vs. SAS Analytics report.
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