IBM SPSS Modeler vs. SAS Enterprise Guide

Fast NLP analytics are very easy in SPSS Modeler because there is a built-in interface for classifying concepts and themes and several pre-built models to match the incoming text source. The visualizations all match and help present NLP information without substantial coding, typically required for word clouds and such. SPSS Modeler is good at attaining results faster in general, and the visual nature of the code makes a good tool to have in the data science team's repository. For younger data scientists, and those just interested, it is a good tool to allow for exploring data science techniques.

Incentivized

SAS Enterprise Guide is good at taking various datasets and giving analyst/user ability to do some transformations without substantial amounts of code. Once the data is inside SAS, the memory of it is very efficient. Using SAS for data analysis can be helpful. It will give good statistics for you, and it has a robust set of functions that aid analysis.

Incentivized

• Combine text and data
• Provide facilities for all phases of the data mining process.
• Use a node and stream paradigm to easily and quickly create models.

Incentivized

• I think the most useful aspect of SAS Enterprise Guide is the ability to use a point-and-click interface to create graphics, transform data, and perform statistics. The best part is that SAS Enterprise Guide creates base SAS code from the process, making it easy to reproduce analyses.
• SAS Enterprise Guide makes creating summary statistics about as easy as it gets. If one doesn't know proc means or proc tabulate, one can use SAS Enterprise Guide instead.
• The time-series forecasting procedures within SAS Enterprise Guide produce fairly good results. SAS Enterprise Guide makes time-series model comparisons relatively straight-forward.

Incentivized

• Has very old style graphs, with lots of limitations.
• Some advanced statistical functions cannot be done through the menu.
• The data connectivity is not that extensive.
• It's an expensive tool.

Incentivized

• SAS Studio has some great examples that can be implemented. Adding a filter to the output datasets for one.
• Some issues around having to enter my password every time I open it up. Some people are having this issue and others aren't. SAS admin is at a loss to work out why it's occurring.

Incentivized

On account of current user experience and the organization-wide acceptance.

It's not all bad, but I don't believe that an enterprise purchase of SAS is worth the expense considering the widely available set of tools in the data analytics space at the moment. In my company, it's a good tool because others use it. Otherwise, I wouldn't purchase a new set of it because it doesn't have some of the better analytical functions in it.

Incentivized

The online support board is helpful and the free add ons are incredibly appreciated.

Incentivized

I did not use the technical support of SAS EG. I can say that I have had hard time to find online tutorials or projects for SAS EG. For instance, it is hard to find completed researches or designed algorithms used with SAS EG. Sometimes it just depends on user's skill set and experience with databases and programming.

Incentivized

I've not worked hands-on with the implementation team, but there were no escalations barring a few hiccups in the deployment due to change in requirement & adoption to our company's remote servers.

When it comes to investigation and descriptive we have found SPSS Statistics to be the tool of choice, but when it comes to projects with large and several datasets SPSS Modeler has been picked from our customers.

Incentivized

Why I prefer SAS EG: Data processing speed is much faster than that R Studio. It can load any amount of data and any type of data like structured or unstructured or semi-structured. Its output delivery system by which we have the output in PDF file makes it very comfortable to use and share that file to clients very easily. Inbuilt functions are very powerful and plentiful. Facility of writing macros makes it far away from its competitors.

• Positive - Ease of decision making and reduction in product life cycle time.
• Positive - Gives entirely new perspective with the help of right team. Helps expanding the portfolio.
• Negative - Needs to have good understanding about mathematical modelling, of which talent is rare and expensive. Hence, increase the costs for R&D and manpower.

Incentivized

• Positive (cost): SAS made a bundle that include unlimited usage of SAS/Enterprise Guide with a server solution. That by itself made the company save a lot of money by not having to pay individual licences anymore.
• Positive (insight): Data analysts in business units often need to crunch data and they don't have access to ETL tools to do it. Having access to SAS/EG gives them that power.
• Positive (time to market): Having the users develop components with SAS/EG allows for easier integration in a production environment (SAS batch job) as no code rework is required.