Before IBM Modeler I was using IBM Resources called SPSS for the Statistics. The first time I used data mining techniques was through this solution. 

I tried an open-source option. We did not have the same level of power. It's not as good as IBM.

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.

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.

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.

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.

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.

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

I came to the World of Watson Conference in 2015, and when I saw SPSS Modeler and what it could do, I just sampled it, and it really, to me, spoke volumes about some of the inefficiencies in the way we were doing business. And, as a brand new BI practice at a company that never had one before, I was just trying to build my practice from the ground up, and I didn't want to limit it to just BI reporting, so I took on the challenge of bringing in this new software, and staking my reputation on it, and it's paying off.

The reasons we eventually chose this solution were that we were made a very good deal on the Gold package, which gave us more capability. I think without Collaboration and Deployment Services it wouldn't have been a worthwhile investment for us and it would have failed on the deployment. So that deal we got on the Gold package really sealed the deal for us.

What's most important when selecting a vendor is the proven practice of the product. Knowing that the product has had success for numerous other customers in the past for similar use cases, for similar types of customers. I think knowing that there are a variety of partners out there with expertise in the product is a very strong selling point for me. I don't like going to things where I can't get help, if I get stuck.

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

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.

We were not previously using a different solution.

No, I wasn't using a different solution beforehand. Though it has become the number one option. Before that it was always Excel and Main Tab, but if you want to get a deep statistical report, I would go to SPSS.

It is a challenge because you can't switch a tool just like that, because you've been using the tool for a while, and then obviously familiarization with something which is new takes some time. You have to go through the whole experience of how good and effective the new tool is. So, sometimes the business doesn't allow you to really look at a switch.

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.

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

I used MatLab before and switched because of the ease of use and continued to use it because of the integration with Python.

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