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.

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.

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. 

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.

• The ability to quickly prototype. 
• The integration into all the existing environments. 
• The ability to not have to manage a lot of code.

I would like better integration into the Weather Company solution. I have raised a couple of concerns about this integration and having more time series capabilities.

It works fine. I have not had any stability issues; it is always up.

It scales. I have not run into any challenges where it will not perform.

Technical support is great - 90% of the time.

The organization did not have a solution before this one. I was familiar with SPSS having worked there. I knew its capabilities and got them involved on the front-end.

The initial setup was straightforward. Though, I had done it before.

I have never done studies on the time savings. Based off the ability to build codes quicker, then put them into production because we have collaboration employment services which is another analytic solution from IBM, so we are able to productionalize the models and manage the models from this environment. Altogether, this saves us a lot of time versus if we want a programmatic solution and had to have developers write C# and Java around it. Overall, it is a huge increase to time savings.

I looked at Microsoft and Alpine Data. I also considered SaaS.

I chose IBM SPSS because of their experience with the solution, what they brought to bear, and their relationships.

1. They are established. 
2. Having worked there, I knew the tool, I had used it in prior roles. 
3. The cost models: I prefer to own a solution versus leasing, much like a SaaS solution. This was one of the things that stood out. 
4. I know the product managers for SPSS and where they were heading from a roadmap solution, and it is very much aligned with what I was trying to do. 

It was this altogether, as well as the price.

Take your time and do some PoCs with this solution and other solutions. At the end of the day, you will be highly impressed with SPSS capabilities and the capability to get models into production. You should take a hard look at SPSS.

Most important criteria when selecting a vendor: 

• The vendor's willingness to invest in the relationship
• Vendor's experience
• Product's stability
• Bringing the enterprise solution to bear.

There are a lot of vendors out there that have been around for three or four years, what I would consider startups. Then you have enterprise solutions, which have been around for 20 or 30 years.

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.

I use it for quick prototyping. I get my students to use it every now and then but we don't actually use it in a class, or there would be more users.

The performance is fantastic. Version 15 used to have some bugs but the newest version, version 18, is a lot better. I really like the functionality that includes R and Python nodes. SPSS should have added that years ago.

What I like is that when you are trying to use a particular algorithm, it actually has the algorithm name. For example, there is actually a node for a C5 decision tree,  whereas with other software you get a generic decision tree and you don't know if you are doing C5 or some other kind of decision tree. I do like that, how the nodes are actually named what the algorithm is.

It is just a lot faster. So you do not have to write a bunch of code, you can throw that stuff on there pretty quickly and do prototyping quickly.

I am really happy with it right now, which is why I still use it. I think it is just fine the way that it is, but again, I'm not really using it to deploy real solutions. It is just for prototyping and academic kind of stuff.

When I used it in the office, back in the day, we did have some stability issues. Sometimes it just randomly crashed and we couldn't get good feedback. But when I use it for my own stuff now I don't have any problems.

It's the same situation as the stability.

I do not have issues now but I used to use technical support when I worked at an office. They would always tell us, "We're looking into it," but we never really got any good feedback. But they did the best they could. It's software, it has bugs sometimes.

I was not involved in the initial setup but the upgrades are straightforward. They do not give me any problems.

I would definitely recommend that other people try it. I still use it because when I got the latest version it had all those things that I wish I would've had when I was working at the office.

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.