IBM SPSS Modeler Reviews

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.

The supervised models are valuable. It is also very organized and easy to use. 

Time Series or forecasting needs to be easier. It is a very important feature, and it should be made easier and more automated to use. For instance, for logistic regression, binary or multinomial is used automatically based on the type of the target variable. I wish they can make Time Series easier to use in a similar way.

I have been using this solution since 2011. It was named Clementine at that time.

It is stable.

I don't contact them directly, but the help is good enough to be used.

I used SPSS Statistics at the beginning, which was a long time ago. I was satisfied and continued using it. I was using SAS when I was in the United States, but that was more than 25 years ago. You should know how to analyze data, and then you can work with any solution. It is like driving a car. If you know the rules, you can change and drive any car. It is not a big deal as long as you know the principles of driving.

The interface of SPSS is easier to use. Even though I have used SAS many times, I mostly recommend SPSS. Students or people who are working with data also prefer SPSS, especially in the Middle East, because of its interface. In the Middle East, it is number one in terms of the number of users. 

A lot of free solutions have come up, and there is a lot of competition. For instance, you have Orange, which is very easy to use, but it is not as strong as IBM SPSS Modeler. There is also KNIME, which is trying to imitate IBM SPSS Modeler. It is free, but it is not easy to use and organized as IBM SPSS Modeler. IBM SPSS Modeler is the easiest solution.

Its initial setup is easy.

Its price is okay for a company, but for personal use, it is considered somewhat expensive.

I am satisfied with this solution, and I would recommend it if you want to go for a paid solution.

I would rate IBM SPSS Modeler a nine out of ten.

We use this solution to generate and deal with data. We have feeders which have the methodologies and the data gives us leverage to deal with that and provide the graphic. 

I'm an application architect and we are customers of IBM.

It's a very good and reasonably priced solution. As a data analyst, the data aggregation is very good. It's a very quick method to merge the data. If you can use the data site, they offer you about 20 different analytic modes. It's simple and precise and it accelerates the data. 

This is an expensive predicament software solution. Currently, the terminals offer the tools for the data analytics, but it needs development. There's a limit to the license. For the data analytics, it's very similar to Tableau. The solution has lots of branches, departments and the teams - that makes it quite a complex solution. We don't always use or need the major developmental version. We care about the KPI. We only care about the KPI reporting so it would be helpful if things were simplified. 

I've been using this solution for a year. 

This is a stable solution. 

The scalability has been pretty good so far. 

I think the technical support is very good. I deal with both the technical side and the commercial data warehouse and have had no problems with technical support. 

I believe the licensing costs are $5,000 per year. 

Some of the team might prefer Tableau as it's a newer solution, but this one works for us. 

It's a very good product. We haven't used the full extent of its power because our team only use the basic part of the Modeler which deals with the migration of data. 

I would rate this solution an eight out of 10. 

We are in the early stages of its use, therefore we are trying to discern predictive analytics on it. We are using it either for workforce deployment or to improve our operations.

We are using on-premise to run our models (on machine). We did different prototypes, but it is still in its early stages. 

It has not yet improved my organization.

The muddling capabilities to help us find some trends.

I do not know yet what areas of improvement there might be.

I can't say what additional features that I would like to see in the future.

I have not worked with it enough yet. From what I have seen, it is good. We have run into a few problems doing some entity matching/analytics using this portion of the software. This is mainly because our data; we do not have enough data points to compare and match the different entities.

It is hard to define at this stage.

We had an IBM Guardium service contract where we used one of their resources to help us develop our prototype. It was a good experience, but they were helpful and responsive.

We were not previously using a different solution.

Initial setup of the software was complex, because of our own problems within the government. It took us four years to deploy it to a machine.

I was not a part of the contracting and RFP phase.

I have not used the visual modeling capability very much.

Most important criteria when selecting a vendor: As we are part of the government, we put up a proposal (an RFP). The government always select the lowest price meeting the requirements. That is who wins it. It is out of our control. We do not choose a vendor. It is a process.

Our primary use case is analytics. 

We are putting less than 10 machine learning models into production, and do not currently run our models on a cloud environment.

It minimizes coding.

Our go live process has been slightly enhanced compared to the previous programmatic process. There is now a faster time to production from the business end. We have C&DS, so we are able to drop the model streams in C&DS, then deploy it through there. 

The visual modeling capability is one of its attractive features.

The biggest issue with the visual modeling capability is that we can't extract the SQL code under the hood. We have a lot of non-technical analysts that develop streams, then when we want to translate it to native SQL, we can't extract it without opening up each node.

We would like to see better visualizations and easier integration with Cognos Analytics for reporting. 

It is not consistently stable. I hope they plan on improving it. C&DS is not stable at all.

SPSS Modeler should meet our needs going forward. It is very scalable for non-technical people. The challenge for the very technical data scientists: It is constraining for them.

C&DS will not meet our scalability needs.

I would not rate the technical support very well. The technicians have accents. When you do find someone, it is very hard to get somebody able to answer the technical questions. 

It is very easy to set up. Once we deployed it and got the license code registered, it was fine. 

We looked into SnapLogic, SaaS, and open source. We chose SPSS Modeler because of the drag and drop capabilities and most of our business analysts are non-technical, so this was attractive to them. 

We use IBM SPSS Modeler for data analysis, prediction, segmentation, etc. We use it as a data management tool.

Compared to other tools, the product works much easier to analyze data without coding.

The platform's cloud version needs improvements. The process to access workflow could be user-friendly. It could be easier to log in and manage security levels. Additionally, it needs to be more popularized and introduce customization options for small companies along with enterprises.

We have been using IBM SPSS Modeler for ten years.

The platform is stable. I rate its stability a seven out of ten.

The product's scalability could be better. I rate its scalability a five out of ten.

The technical support services could be better.

The setup process isn't easy and time-consuming to deploy in case of improving an already existing infrastructure.

The product generates a return on investment.

It is an expensive product.

The product is challenging to use. I rate it a seven out of ten.

Primary use case is feature engineering on a pre-prepared data set and mostly doing predictive modeling. Used on desktop. If it comes to ETL and data prep the tool is a waste of time...

• Pretty much the automated modeling process helps us to get going so quickly.
• A lot of jobs that are stuck in Excel due to the huge numbers of rows are tackled pretty quickly.

• Automated data cleansing, transformations and imputation of missing data.
• Some basic form of feature engineering for classification models, automated binning, etc. This really quickens the model development process.
• Automated modelling, classification, or clustering are very useful as well.

• Formula writing is not straightforward for an Excel user. Totally new set of functions, and it takes time to learn and teach.
• Automating procedures: Writing macros is not easy and difficult to learn.
• It is not integrated with Qlik, Tableau, and Power BI. Unfortunately…
• Expensive to deploy solutions. You need to buy an extra deployment unit.

With some specific encoding, it simply does not work. I installed the English version on a Turkish Windows locale and SPSS Modeler literally halted. No fixes. You have to change locale and install from scratch.

The server is not cheap and not scalable enough.

Hard to contact and get any benefit.

I used SAS Enterprise Guide and Enterprise Miner. Compared to those, SPSS Modeler is easier to learn and utilize. 

when compared to Alteryx, Alteryx is a much more userfriendly tool to use. I switched to Alteryx because it can do ETL on big data, has extensive abilities in spatial analytics.

Setup is a little problematic for desktop. A nightmare for server.

Used a vendor team, and it sucked. Nobody on IBM side really cared. It is a big company "Big Blue", and you are always a miniature customer.

It got us a good amount of money with quick and efficient modeling.

It earns its money before the year-end.

When you are close to end of quarter, IBM and its partners can get you 60% to 70% discounts, so literally wait for the last day of the quarter for the best prices if you don't want to get robbed by IBM. 

we switched to Alteryx because price performance advantages and great community and support.

I checked out RapidMiner, which is a good alternative. However, SPSS Modeler is more capable and automated. 

Do not dive into the server directly. It is very hefty for just doing calculations that can already be done by SQL Server R or Oracle or teradata at hand... Maximize the utilization of the desktop tool first.

It is not a BI tool. It is pure analytics. It does not do reporting as well. And you unfortunately cannot publish your results to Qlik, Tableau, or Power BI.

this was another reason we switched to Alteryx.

We just started using it for analytical performance. We're still in the testing phases of building a couple of different projects, proofs of concept.

So far, it's good. We're probably going to do a comparison with Watson, to test two different products, to see which one gives a better response.

Right now, I think we have about five or six different machine learning proofs of concept, using real-time data. We're running them on Bluemix, IBM Cloud.

Projecting models, forecasting. Being able to incorporate things that we could only imagine, and coming into new, faster learning capabilities from it.

I don't know if we're using visual modeling. We have developers on that.

We use it for governance and security issues because we work with the airline industry; we have to make sure with the PII information, to protect and to manipulate the data if the user does decide that they want to be excluded from it. This solution helped us with their personal information, that they want to be excluded, in identifying a couple of the criteria within the system.

We're still learning, the beginning of the application. We haven't played with all the features to be able to say.

So far so good. We're still learning a lot of the capabilities.

I do not know, that's more on the developer side.

We use multiple vendors, so we were trying to see which one would give us the most benefit.

In selecting a vendor we want to see the capability and the flexibility to display the data that we want, and also being able to manipulate the data in real-time.

For the different teams, people used Tableau, SAS, different applications that are out there. We wanted one that would not just give us the data, but forecast the data and predict the data.

Give it a try, start with a proof of concept, and see where it leads.