IBM SPSS Modeler Reviews

Rapid prototyping, pre-production of models before roll out.

We put very few machine learning models in production, but we test a lot of them though. Nothing is real-time.

I can't say that our go-live has changed compared to a previously problematic base process.

We don't use IBM SPSS Modeler for governance and security issues. I can't talk about the visual modeling capability.

It's very stable, although it is not as user friendly as it could be.

I don't use it for any high performance applications.

I have not used technical support.

I'm not in the budget decisions, but IBM was chosen because of usability. It's point and click, whereas the other out-of-the box-solution, or open-source solutions, require full-on programming and a much higher skill level.

I was not involved in the initial setup.

Usually open-source solutions.

If you're hiring a data scientist, you don't need IBM SPSS Modeler. If you only have an MBA who needs to be running proofs of concept, then buy IBM SPSS Modeler.

Building predictive models, including customer churn and lead generation.

Performance has been great. I've used it for about eight years or so, lots of flexibility. It continues to be a very flexible platform, so that it handles R and Python and other types of technology. It seems to be growing with additional open-source movement out there on different platforms.

We aren't putting that many machine-learning models into production. This is not the primary tool we use. This is more for me in terms of data exploration and knowledge discovery, that kind of thing. I really haven't done any production models in my current role. In previous roles I have.

In terms of cloud environments, it's actually a combination. Long story, but it's a combination of different things.

It's more for data, as a data repository. 

My experience so far using Modeler is good. I haven't noticed any issues with our current solution.

I don't use it for governance and security issues or for visual modeling. For data visualization we use ThoughtSpot, Tableau, Power BI. In terms of the graphic capability, those are existing platforms that have a larger user base, so it's unlikely that we'll use Modeler exclusively for data visualization.

I really can't think of anything off the top of my head because I feel like I'm under utilizing it as it is, because we're doing specific things. Two or three years ago, I would've said R and Python integration, but they've done that.

I've been using it since it was called Clementine. Every version seems to be better than the previous, but I don't think I've ever had any catastrophic failures, or any bugs that were significant enough to not have a work-around available.

The scalability was kind of limited by our ability to get other people licenses, and that was usually more of a financial constraint. It's expensive, but it's a good tool.

I haven't used tech support recently. We used IBM designates for things like training and the like, which has always been very good, but I can't really think of any issue that required any technical support.

It's a solution that was available when I entered the role. I have heard from others who were in the process of trying to start from ground-zero, and the tendency for them is to go with open-source because of the revenue model, obviously. 

I would say, if you're considering that open source-solution, definitely consider Modeler as well. Put together some kind of proposal that allows you to figure out how much time it's going to take individual people to create those models, versus being able to have an out-of-the-box solution that gets your team going more immediately.

Support is another benefit of going with Modeler over open-source. SPSS has been around for a long time. IBM acquired them, and they've added functionality and features to meet the needs of growing data science populations.

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.

We use it to try to do predictive modeling and data exploration. I have a team of people that are working with the tool right now. We have gone through some SPSS training, so primarily we take the data and figure out what they need to try to predict or what they are trying to figure out, then we use the tool to normalize the data, maybe doing some text analytics. We are trying to get into doing some identity resolution with it, so we are using the professional version (the higher version) with it.

It has performed well. We are a bit limited because we are using it on a desktop, but we are moving it into a server architecture so we can have a little bit more horsepower for it. Also, we are getting licenses to do an SPSS server on the back-end, so as to offload some of the work off the desktop. This will help it perform a lot better. However, so far, it has worked pretty well.

We're doing real-time right now, but we are doing batch once we get the server product up and going. In terms of models, we are getting it off the ground. We have been using it for about six months, and we have been just playing with getting our models up and going, so we actually have the whole pure data and Hortonworks analytics products that we are going to be deploying in the analytics environment, that's where our server product will go, then we will have all of the governance pieces in place to start doing production deployment. So, we are almost there.

We are all completely on-premise. It has been fine on-premise, because we host a whole lot of IBM products. Sometimes it gets a little bit convoluted with the licensing. Right now, we just have the fixed user licenses that we deployed. We are trying to get some floating licenses out there to expand the use of it to a bunch of other people.

It has provided us a lot of small wins that we could bring to our leadership and it has given them confidence with what we were doing in regards to analytics. We have used this to help us pursue bigger, better products, such as IBM PureData. It was a stepping stone, a launching off point, for much bigger products with IBM.

Our go live process has change a little bit compared to a previously programmatic process. We are still getting it built out right now so we are not quite where it is completely mature.

All the statistical models that you are able to access. 

We have integration where you can write third-party apps. This sort of feature opens it up to being able to do anything you want. 

It gives you a GUI interface, which is a lot more user-friendly and easier to use compared to writing R scripts or Python, like some Anaconda type code. It makes it more open and accessible to users that are not as familiar with programming.

We have been able to do some predictive modeling with it. For a business case example: It definitely helped identify issues in the airline industry. The model was able to uncover a few airlines that had some anomalous behavior that we were able to pursue the issues and get them corrected.

The platform that you can deploy it on needs improvement because I think it is Windows only. I do not think it can run off a Red Hat, like the server products. I am pretty sure it is Windows and AIX only. Every company is looking at solutions that go towards Red Hat, so if that is not offered, that would be one thing. 

It seems very stable. SPSS seems like a very mature product. We have not had any issues with it at all.

It is pretty scalable because you can have an SPSS server that we can work to offload, and it seems like we could deploy it to many people if we had the money. It is a little bit costly, but that is with any product like this. Compared to SAS, FICO, or any of their competitors, I think it is comparable.

We used technical support for licensing. The experience was okay. It took us a week or two to try to get over their hurdles. 

We have direct contact with some IBM partners that work with us directly, so we just go to them when we have any technical issues. This is more on the user end of using the product, and they are very helpful.

We were using Excel and beating the heck out of it. We realized with Excel reaching its limits that we need to find out other options. We started to use R, then uncovered this IBM solution by our actual IBM rep, who found that we had licenses for this parked at another location that were not being used. So, we decided to jump right in and we got some training on it.

The initial setup was somewhere in between straightforward and complex. I would not say complex. It seemed pretty straightforward. I think anything that made it more complicated was about our environment, not about the tool itself. 

Cost can be a consideration or a factor when looking to try to deploy to more people. Everybody has to be cost conscious, so find a way to receive bigger bundle discounts. We use a lot of IBM products, so I assume we are getting some discounts now.

Just IBM.

Once you get to the limits of Excel, then you go out and get your pick. Go with a product you know and a vendor you already know

Most important criteria when selecting a vendor: We have familiarity with this vendor already. We are already in IBM shops, so it made it easy to go after those products because we already had a good relationship with them.

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 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 are primarily interested in the supply chain data analytics, focusing mainly on procurement. We believe that there is a lot of value in spend analytics because of the following:

• Understanding and identifying opportunities.
• Reduced prices.
• Ability to better do negotiations.
• Understanding better the categories. 

We are interested in finding the right model in order to do data mining correctly. We want to learn and understand which models are best for us, then know in which cases to use them. 

It is pretty scalable.

Based on several discussions that we have had with our local representative, the initial setup should be quite short, a few weeks, or two or three weeks for the PoC. We expect him to transfer the data to us, allowing our internal analysts to do the analysis. 

We have a local representative in Israel who specializes in SPSS. He will help us do the PoC, allowing us to understand if we will pursue this process.

We have the impression that he is an expert in this area. We expect him to help and guide us through the process.

We are also interested in Watson Analytics. There is an issue that we are trying to understand because we are in a different industry. We find it quite challenging to transfer the data to the cloud. Therefore, we want to understand if it is possible to do it on-premise. We are trying to investigate this issue.

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.

Customer segmentation and churn analytics.

We get best results in customer segmentation and churn analytics and we have retained our customers. Our retention score has improved as a result of these projects.

We haven't used machine learning solutions yet.

Our business units' capabilities with SPSS Modeler is high. They no longer waste time on modeling and algorithms, meaning they are not coding any more. For example, segmentation projects now take one to three months, rather than six months to a year, as before.

In the future, SPSS and Cognos Analytics will be integrated. We will be using the two products together.

We have not yet used IBM SPSS Modeler for governance and security issues.

It would be helpful if SPSS supported open-source features, for example, embedding R or Python scripts in SPSS Modeler. We don't need that now, but in the future it may be useful.

We haven't suffered from any stability issues. It's a stable product.

We haven't had any performance problems. The product runs every data volume performantly and produces results.

We are doing our solutions in-house, but sometimes we require local support from IBM partners, but not too often. We are happy with the support the partners provide.

We have SPSS know-how in our company, and other products are not as stable as SPSS. Also, we have local support in Turkey.

Straightforward. It was not complex.

Oracle and SAP. SPSS, however, is widely known and widely used in Turkey. University students learn it, so it's easy to find professionals to work with it.

You should analyze your needs and your data, your projects. There is a lot of choice in data analytics. Which one is best depends on your needs and your budget. It depends on what you are looking to achieve.

Pricing data analytics.

We are putting seven machine learning models in production to start. We may expand up to 10. This is real-time as we are pulling data out of Cognos BI server every morning. We manipulate and reload the data throughout the day based on parameters that come in from the field, then that gets put back into the system and refreshed for the next day.

We have a private cloud, which is our corporate cloud. Everything is done off of a shared server. 

To date, working with IBM SPSS Modeler has been very good, our installers and trainers have been excellent. The product seems to be quite robust and doing what we need.

This is a new installation for us. We have not implemented it fully. It is going live now. Therefore, the impacts have yet to be determined. We are anticipating a more streamlined process.

It handles large data better than the previous system that we were using, which was basically Excel and Access. We serve upwards of 300,000 parts over a 150 regions and we need to crunch a lot of numbers.

The speed of the system could be improved, but I think that will be fixed once we get our data in line.

I do not what additional features that I would like to see in the next release as I am still learning the features in this release!

So far, the stability has been rock solid. It is very good, but slow. The slowness may be because we have not finalized all the background information in SPSS. It still needs some tweaking.

It will scale up to anything we need.

We have not used the technical support yet.

Previously, we were using an ad hoc system that we developed in-house. It was based on Access databases spitting data back into Excel.

It is a very complex system, and we are dealing with a lot of different features, but the installation did a very good job of walking us through it. They made it as painless as possible.

We were looking for an ERP system that would help us streamline the whole process. My director reviewed four or five different scenarios and decided on IBM.

We did look at other vendors, but I cannot name them as I was not part of the selection process.

SPSS and TM1 are so versatile that it depends on how you set it up within your company and with whomever guides them through it, because it is so customizable. You need a good guide and what you want out of it, as it is very transparent.

Most important criteria when selecting a vendor: ease of use. They should be able to handle our unique situation. We have many branches with many moving parts, and also a lot of internal customers.