Overall Satisfaction with IBM SPSS Modeler
IBM SPSS Modeler is the on-premise version of Modeler Flow, which we use in the Cloud Pak for Data as a Service application in IBM's cloud. Mainly, it is an interface based application with nodes that replaces the need to write code. In several instances, it can be a quicker method to designing a data science project. We use it for the Text Analytics node to assist with ad hoc NLP requests to turn over NLP in a quicker fashion than typical.
- Fast code builder.
- No need to maintain software versioning.
- Visual code, easy to see what's happening.
- Details of models and nodes requires some "digging", "clicking".
- Customization of versions is hard to implement.
- Latest open source code packages take time to integrate into the application.
- Several projects completed quickly and without substantial coding.
- Saves some time by allowing younger experienced employees to jump right into data science.
- Comes with the package for Cloud Pak for Data, so its available that way as a toolkit, saves money.
We additionally use SAS Data Miner as a toolkit. Compared to SAS Data Miner, the SPSS Modeler is a good competitor. SAS probably is more integrated in the market for a visual-based code for data science activities. However, I don't think it offers anything better than SPSS, and I really like several of the helpful components for usability for SPSS like peaks into nodes.
Do you think IBM SPSS Modeler delivers good value for the price?
Are you happy with IBM SPSS Modeler's feature set?
Did IBM SPSS Modeler live up to sales and marketing promises?
Did implementation of IBM SPSS Modeler go as expected?
Would you buy IBM SPSS Modeler again?
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.