IBM SPSS Modeler Reviews

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.

People data, survey insights, HR analytics, nominal data, relational data, SEM modeling, logistic regression using nominal or ordinal groups.

Quickness and ease of use with the guarantee of robust modeling techniques and trustworthy accuracy.

Quick insights.

Easier coding language that is more flexible with other platforms. More server capabilities. More graphics.

I used SPSS for statistical hypothesis analysis and it performed well. 

It helped me in that I didn't need to write them by hand, and I could get a result in one or two minutes. That helped me a lot. 

I would like see more programming languages added, like MATLAB. That would be better.

The instillation was easy.

The stability was good.

It was scalable.

 I also use SAS, but SPSS is easier than SAS, and I enjoy it.

I would advise my colleagues to use SPSS, depending on the work that they want to do. Though for complex issues I might advise them to use better software. 

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.