Share your experience using OpenText Magellan Text Mining

I use IBM SPSS Modeler for teaching data science, and for data mining. I use it for my research.

The most valuable features of the IBM SPSS Modeler are visual programming, you don't have to write any code, and it is easy to use. 90 to 95 percent of the use cases, you don't have to fine-tune anything. If you want to do something deeper, for example, create a better neural network, then you have to go into the features and try to fine-tune them. However, the default selection which is made by the tool, it's very practical and works well.

Overall the solution is very powerful.

Neural networks are quite simple, and now neural networks are evolving to these architecture related to deep learning, etc. They didn't incorporate this in IBM SPSS Modeler.

I have been using IBM SPSS Modeler for approximately 20 years.

IBM SPSS Modeler is a stable solution. They are releasing new versions every year, fixing small things.

The scalability of the IBM SPSS Modeler is great. It is a professional tool and can scale as much as your hardware will allow.

IBM SPSS Modeler support documentation needs a lot of improvement. They are very good documents and have good explanations for certain things, but certain areas don't have an explanation.

I rate the support of IBM SPSS Modeler a one out of five.

I have used other solutions previously, such as Weka and KNIME.

In the beginning, KNIME and Weka were not as powerful as IBM SPSS Modeler. Weka missed some important features in the results. For example, you have the regression line, you don't have the P values or the significance of the parameters. KNIME was inspired by IBM SPSS Modeler.

IBM SPSS Modeler is easy to set up.

I rate the setup of IBM SPSS Modeler a five out of five.

I am using the free version of IBM SPSS Modeler, it is the educational edition version.

If you don't work developing algorithms and are only using them, this is the right tool. If you want to go deep into the algorithms, then you have to go to programming, such as Python, R, etc. If you are a practitioner, like me, who applies the tool in real-life projects, this is the tool I would recommend.

I rate IBM SPSS Modeler a nine out of ten.

We use KNIME for a lot of predictive modeling. We use it to grab data, prepare it for modeling, do automated machine learning analysis, sometimes forecasting, and then try to deploy the models into production.

Since KNIME is a no-code platform, it is easy to work with. You don't have to write any codes and try to fix all the bits and pieces of coding or the intricacies of the programming language. Instead, getting a quick data prep or big data and eventually running it through your hypothesis is pretty fast. It's not ideal for huge data sets worth gigabytes, but it's okay since very few people have big data sets.

KNIME is not good at visualization. I would like to see NLQ (Natural language query) and automated visualizations added to KNIME.

I have been using KNIME for two to three years.

Unless you are working with terabytes worth of data, KNIME is a stable solution.

The solution is scalable and can be used up to terabytes of data. Around two to three people are using the solution in our organization.

The solution’s initial setup is quick and easy.

One person can deploy the solution within ten minutes.

The solution is very essential when we require an explainable data modeling pipeline. We can show the workflows of KNIME to our customers and talk about it instead of showing the code and expecting them to read, which they can never do.

The process of providing KNIME to the client, how it works, where we get the data, what the initial data statistics were, and what we get in return are pretty explainable. We worked on multiple retail projects and insurance scoring projects.

KNIME is perfect for data pre-processing projects. The important thing is that when someone builds a KNIME workflow, we can quickly onboard and change it for something else. It means that we don't need to read and understand the code. It means that it's replicable and reusable.

If somebody does something, somebody else can quickly onboard and enhance, improve, or totally change the workflow from scratch. It's pretty hard and time-consuming for typical use cases where we utilize coding. KNIME's open-source nature has a good impact on our analytics work.

Recently, KNIME added something relevant to generative AI integration, which was a good move. Alteryx is slightly more powerful than KNIME, and Dataiku is more powerful than both KNIME and Alteryx. I sometimes work with the on-premises version of KNIME and sometimes the cloud version. The solution does not need any maintenance.

Users should quickly start using KNIME for whatever they want to do, and they'll learn it on the go easily. I would recommend the solution to other users.

Overall, I rate the solution an eight out of ten.