IBM SPSS Modeler Reviews

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.

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.

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.

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.

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 it for data modeling like arithmetic modeling, bank modeling. We have different models such as loan models. We use three products, SAS, R, and SPSS Modeler to do predictive modeling. We are a big IBM shop.

I'm not sure how many machine-learning models we are putting into production. I'm new, I've been at the company for five months, but I would say this year there should be at least five or six models. We do a PoC on modeling and, based on what fits better, that's what we go with. So the bottom line is that a handful of models will go live but we'll be trying 10 to 15 models to do the predictions and see what best suits the company.

This is batch. We do monthly modeling, we do weekly modeling. It's not daily. We run weekly model reports too. We also change the parameters that we enter based on the industry, as things change.

We don't have cloud, it's all on-prem.

Our go-live process has changed compared to the previously programmatic code based process. It’s not just the time to go-live but it’s also the process itself; the improvement in terms of performance, and maintenance is also important. I would say it has saved us a lot of time, about 20 or 30% of our time. I don’t have the numbers in front of me but I think something along those lines.

We are big-time into data analytics. AI is another area which we want to start looking at. Digital banking is important. We are looking more into digital banking and we are trying to put some features in there. I think the trend is more on that area of data analytics, digital.

I can't comment on our use of SPSS Modeler for governance and security issues.

We use analytics with the visual modeling capability to leverage productivity improvements.

New features are always welcome, but I’m not the core person. A separate team can comment on this, but not me.

There are issues, we try to mitigate them. There are always issues. We’re trying to be stable but there are a few areas...

It’s definitely scalable, it’s all on the same platform, it’s well integrated. I think the integration is important in terms of scalablility because essentially, having the entire suite helps a lot to scale it, market it. Even in terms of processing, it’s easier.

I personally have not had experience with IBM technical support, but the group has worked with them. I haven't heard anything from them, so I think it's okay.

We already had SAS, we had R. It’s all legacy and it’s all homegrown. But we had an IBM shop also.

I would say, look through every product in the market, like we do, and try to pick what works best.