IBM SPSS Modeler Reviews

I was familiar with using IBM SPSS Modeler separately in the private sector before that. It's a good tool for extracting data from data warehouses, creating streams, and manipulating logic to extract final data.

There are performance issues. Extracting data from many combined tables can take hours and occasionally crash the server due to memory leaks. This performance problem bothers people. The performance issue seems to be related to the server. We design streams on the client and submit them to the server, which generates a large SQL statement. There are two potential bottlenecks: one in the server and another in data extraction. I'm unsure about the exact mechanics of data splitting when fetching from the database.

When streams become larger, performance bottlenecks may occur in the IBM SPSS Modeler server or the database. Sometimes the server crashes and needs to be restarted to release memory on both sides. I'm not sure exactly where the problem is caused, as I focus on stream design rather than server issues. The problem could be on the IBM SPSS Modeler server and database.

We use the solution for data mining.

It helps our customers who want to find some insights from their data.

Mostly, the solution is used for business analytics, including for several CSV files and Oracle databases, in order to sort of create flows for financial reports.

The solution has not improved the functioning of my company. So, we switched off the solution.

The solution is used for analyzing data and also for training.

It's a very organized product. It's easy to use.

Even if you look to the IBM SPSS Statistics, it is the same procedure, you can categorize data clearly that is nominal or scale. Therefore, you can analyze it in a simple way. This is different than most of the other software. IBM Modeler is really organized and it has too many automated methods that can be used easily even for the people who do not have a lot of experience.

I've been following the progress since SPSS was bought by IBM and transferred from Modeler to IBM SPSS Modeler. I'm recognizing the number of nuggets or techniques added to the software itself.

I used to give training courses in this procedure. I try to approach it like driving a car. If you drive one car, you can drive any other car. When IBM is so very organized, I find that, if you used it, you will learn how to choose the correct Data Science or Data Mining techniques. This will give you the ability to use the knowledge gained on other platforms. Most of the other solutions are trying to imitate IBM Modeler.

You have a lot of techniques and you have to use the proper techniques based on your needs, however, even some governmental places ask me for help. Sometimes they need association techniques, or clustering techniques, however, no doubt, you have to use the protective care modeling techniques, which are currently known as supervised modeling or regression techniques.

There are so many ways of analyzing data. If you have a huge amount of data with so many variables, you can use the solution to understand the learnings behind the data. Based on the objective of your research, you have a lot of techniques at your disposal. 

The initial setup was simple. 

The solution can scale.

The stability is good.

I used SPSS Modeler for doing both predictive analytics, advanced analytics and more specifically to create and maintain machine learning predictive models.

The solution can be used for many projects, mainly for sophisticated data. It's applicable when you're dealing with a lot of data, for example, we're now dealing with a cancer related project, trying to figure out how we can build a predictive model. We're also using it at the university to predict the courses our students will follow. It helps with preparation related to capacity. We use the solution for both educational and research purposes. I have some students that I'm supervising for data mining and I teach this solution. I'm interested in data science in general. I'll be publishing a book on the subject in Arabic. I'm an associate professor of statistics and we are customers of IBM. 

I like the automation and that this product is very organized and easy to use. I think these features can be found in many products but I like IBM Modeler because it's very clear about how to use it. There are many other good features and I discovered something that I haven't seen in other software. It's the ability to use two different techniques, one is the regression technique and the other is the neural network. With IBM you can combine them in one node. It improves the model which is a big advantage. 

We use the solution for generalized linear models. We use it for modeling and analyzing real data using models. 

It is very organized and easy to use. It's very clear in IBM, and this makes it easy to build your projects. It's not complicated at all. 

If you have no experience at all, it still remains very simple. 

The quality is very good. 

The intelligence is superior to open-source options.

It is very powerful. 

The solution is not complex to set up. 

Creating analytical models that we put into production: Everything ranging from pricing to just-in-time inventory management.

We have had multiple models go into production. We are at around roughly 10 models right now. We were able to quickly transform and move existing models into the SPSS environment, so we saw increases in accuracy resulting from this. Therefore, we are running faster and more accurately.

This is batch. We are using models for safety and to predict what drivers are likely to leave (i.e., just-in-time inventory management), so grows it across the enterprise.

We're using a public Azure cloud. We are not deploying apps, but we are doing the analytics. We are pulling the data in with it, then we are writing the tables.

It has performed as it should. I have not had any issues.

We are creating models and putting them into production much faster than we would if we had gone with a strictly, code-based solution, like R or Python. In the time it takes to write the code to build one model, I am building three models inside SPSS.