It offers exceptional scalability abilities. I would rate it ten out of ten.

While it is scalable, there is room for further enhancement.

In terms of scalability, I would rate it around eight out of ten. It provides a good capacity because it directs you to the main points you need to focus on without having to search around. The functionality is capable, and it helps in business development and making informed decisions.

During my master's degree, everyone in the business intelligence department used SPSS Statistics, and also in my previous work, it was mainly the technical business analyst team and probably around ten people in the strategy department. So, it's between five and ten users in my experience.

I rate the tool's scalability an eight or nine out of ten. 

The product is easily scalable.

We have more than 100 IBM SPSS Statistics end users including the data processing team in our organization.

We have about 30 to 40 users leveraging it in our organization. In my department, seven to nine people are using it. The users are mostly in IT.

I've never found a dataset that it couldn't handle, but then, my datasets are mainly, perhaps, about a 100,000 cases. Within that range SPSS Statistics does it very easily.

I believe more than 50% of those in the university are working on it, so it's a long scale of users. For instance, tomorrow I am leading training for using the Modeler in a department in the university. All the time, a lot of people ask for a daily cost for using SPSS because it is the most common solution here in Saudi Arabia.

I have used IBM SPSS Statistics for hundreds of thousands of cases, but not into the millions. It's worked very well. It is scalable.

There are approximately 100 people are using this solution in the university.

SPSS can handle whatever you throw at it, whether your data set contains 10,000, 100,000, or a million objects. It's like the heavy artillery of analytical tools. 

Scalability is also limited, but if you have a high-speed computer, then scalability will be better. But compared to PyCharm and Python and R, it's very limited.

It's running on-premises, so you're only limited by your resources, not theirs. SPSS is a product that sits on the server or your PC, and it works. So if your PC is very old, of course, everything else won't work correctly there.

I don't believe this solution would be the best for companies that need to scale. It doesn't have the level of scalability available to grow very much.

The solution is scalable.

This solution is not scalable.

If I had known or implemented the version, then it may be scalable. The problem is that it's used on the original core and it's receiving a long analysis. It should be able to use multi-cores, and CPU, but it doesn't do anything like that. 

I am the only user.

Up until now, not really. In telecom, we have a really big amount of data. I think that's not really been a problem for SPSS to handle.

In terms of scalbility, it is scalable. There is a scalability fee for this kind of package. The business package can be measured only with the amount of data it can analyze. 

Because if I talk about the Oracle database or the RNC database, the measure of scalability depends on the number of notes I can add to the RSE cluster, how much data will my database be able to handle, and what kind of hardware requirements I need. The IBM package is just a normal package. It doesn't have a SPSS package to measure the scalability. There's only one factor, how much volume of data it can analyze at any point of time. Take an example of its competitor, SAS. SAS analytical software has a server called Laser Server, an ASR server, which is capable of handling around 120 terabytes of data at a time.

That kind of thing should be here. I can't assume that SPSS as a front-end tool can assess that much volume of data. Having said that, it should have some kind of work around or a bigger version, just like SAP has given three versions of Lumira Discovery, Lumira designer and business studio. So on the same lines SPSS should have options for a normal user or for an advanced user which can take more amount of data.

Right now, there are 5 or 6 users in my company using the product. I have plans to increase usage because currently, I am working with some new customers on their market analysis and ideas. So I'll introduce this to them for their future analysis.

We've found the solution to be very scalable. If a company needs to scale up, they can do so quite easily.

Currently, we are only operating with two licenses.

It's used a fair amount. However, obviously, since the COVID-19 lockdown, work has been very slow. Right now, it's not being used very much. I'm probably not planning to increase my usage due to the fact that I need to first get sales back up to where they were near the beginning of the year, before the quarantining and social distancing.

This product is scalable and there are three versions. There is a smaller version, a mid-level, and there is a full enterprise version. The licensing can make it difficult to deploy models although it should be possible with the enterprise version. However, I have never tried it.

The solution is scalable, but our system isn't so big. We just use it for simple scenarios. However, I believe this solution to have similar scalability capabilities to other IBM products. We have approximately ten users on the solution currently.

We have 500 users of this solution. I'm coming from an organization with 40,000 people.

While we don't really know yet what the performance will be like under greater loads for big data, I am pretty confident that the product is very scalable.  

This solution's scalability is good.

I have used SPSS with 100,000 records. At that point, it was reaching its limit. Processes would not run, or would run incompletely. I am told for very large files, SAS may be better, but I have not confirmed this.

We have not scaled this solution as it is sufficient for us.

We have one hundred people using this solution. Most are IT, consultants.

Not applicable.

The scalability of this solution is not good because we cannot use it with Big Data. Instead, we used IBM SPSS Modeler.

It breaks under the weight of a moderate amount of data.