IBM SPSS Modeler Reviews

The solution can be used for many projects, mainly for sophisticated data. It's applicable when you're dealing with a lot of data, for example, we're now dealing with a cancer related project, trying to figure out how we can build a predictive model. We're also using it at the university to predict the courses our students will follow. It helps with preparation related to capacity. We use the solution for both educational and research purposes. I have some students that I'm supervising for data mining and I teach this solution. I'm interested in data science in general. I'll be publishing a book on the subject in Arabic. I'm an associate professor of statistics and we are customers of IBM. 

I like the automation and that this product is very organized and easy to use. I think these features can be found in many products but I like IBM Modeler because it's very clear about how to use it. There are many other good features and I discovered something that I haven't seen in other software. It's the ability to use two different techniques, one is the regression technique and the other is the neural network. With IBM you can combine them in one node. It improves the model which is a big advantage. 

Dimension reduction is very important, especially if you are working with millions of recordings and thousands of variables. It exists already, but it should be classified separately. The solution could be improved by adding a feature for statistical analysis like processes. They have some in the output, but not in the modes itself. I hope they can add statistical knowledge to the solution.

I've been using this solution since it was called Clementine, so it's been about nine years already. 

This solution is stable. 

Scalability is good although we use a limited amount of data. It is not like millions of records and it is based on the speed of the computer, the personal computer itself. I think it can handle huge data. 

Initial installation is very quick and straightforward. It can take up to half an hour. I carry out the installation. 

There is a basic fee and you pay extra for added features. You'll need to use Linux which also adds to the price.

We looked at other options but it was clear that IBM was the simplest solution to deal with because of its organization. You have the three main topics of data mine: data science as in association, segmentation and clustering. IBM has it organized for each branch and the added advantage of having an automated icon. If someone is not expert at this technique then this program can deal with maybe up to eight different models and you can pick the one you want.

They offer a four-week trial which is maybe enough time to study the product. It really is very good.

I would rate this solution a nine out of 10. 

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. 

Our primary use case is the analysis of economic impact.

We are using this product for analysis of an exhibition center. It is releasing a lot of media and Modeler assesses how the shows were in the last six months. When it comes to a relative comparison between two quarters, this tool gives you very accurate numbers, which are then reflected in the media release that goes out.

I think the tool is very helpful in terms of, when you work on a foothold, you can compare how many people came into the center between fiscal periods. You take two quarters and compare them and this tool is ideal because it gives you a lot of visibility on the before and after. It provides graphs for you to actually see how the impact has been on the financial side.

It's a user-friendly tool.

I think that Modeler needs to be more commercially effective because, of the competing tools, some of them are free and others are available at a very nominal cost.

When you are not using the product, such as during the pandemic where we had worldwide lockdowns, you still have to pay for the licensing. It is just wasting the term and they should have suspended the fees and extended the licensing timeline. Essentially, you can't stop it, even if you're not using it, and it is a little difficult to accept the cost in such situations.

What we really need is some flexible terms with respect to the renewal or a break from the strict license timeline. 

We have been using Modeler for between seven and eight years.

This is a pretty stable solution. The end-users and other people with hands-on experience are very happy with this tool. It has not let us down in the past and we have not really experienced any downtime.

It is simple for us to maintain because all we have to do is renew the key every year. It does not require any support, maintenance, or patch upgrades. It's just there, and as long as the renewal is done, we have access to run the tool and perform different combinations of impact assessment.

I think that it is pretty scalable, based on what I have heard from the team. However, we don't really have a need for that. We have two main uses who take on different directions of research and insight into different areas of study.

A small enterprise might not need this product because the cost for the renews of the license is pretty high. Because of this, smaller organizations would be better off with an open-source or free online tool to do their work. For large organizations, because you need stability and you need the reliability of data, I think this product is definitely required.

We have not needed to contact technical support. Once the system is configured, the key will give you access. I do not have downtime or major breakdowns. As long as you know what you need to do with it, the tool is fine. 

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.

The first time you implement this product, you need an implementation partner. I remember that it was pretty expensive. You go through a one-time on-premises deployment and configuration cost, and then it is just the renewal after that.

An authorized IBM reseller in the region assisted us with the deployment.

This tool, being an IBM product, is pretty expensive.

Our license key is renewed on a yearly basis.

I would recommend this product, although it depends on the nature of the business. Those in the public sector or semi-government organizations who are supposed to report a lot of impact assessment from different perspectives such as healthcare and education will have pretty decent output and results. 

We have been happy with this tool, although now the times have changed. There is some commercial pressure for us to implement something that is cheaper.

I would rate this solution an eight out of ten.

I use the product for my research purposes in running analysis on data.  

The most valuable feature for me is just being able to run statistical analysis on my data.  

I actually think it is a great product. Maybe there could be some enhancement with more extensive help built into the interface. This could help end-users to understand the features as well as how to use them. Apart from that, it is a great product.  

We have been using IBM SPSS (Statistical Package for the Social Sciences) for about a year-and-a-half.  

The initial installation is straightforward.  

I did not have to use an integrator or consultant for deployment. I was able to do it by myself.  

I would recommend IBM SPSS Modeler for people who need to do statistical analysis of this type.  

On a scale from one to ten (where one is the worst and ten is the best), I would rate this product as an eight-out-of-ten. It does the job.  

Evaluation for training and consulting.

GUI and flow management. 

Weak documentation and user guide.

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.

I use it for my classes. One of the classes I teach is Advanced Analytics for students in the actuarial sciences area. My students are also using it for projects that they have to do as part of the process leading toward their degrees.

Before that, I was using it when I worked for IBM, as a consultant. I was doing a project for IBM in their analytics.

The main benefit is it makes things a little easier to do. If you want to solve a problem with R, for example, that's a lot more of a struggle. Essentially, R is a programming language. This package makes it more user-friendly, particularly for people who do not have a background in programming.

It's very easy to use. The drag and drop feature makes it very easy when you are building and testing streams. That's very useful.

I understand that it takes some time to incorporate some of the new algorithms that have come out in the last few months, in the literature. For example, there is an algorithm based on how ants search for food. And there are some algorithms that have now been developed to complement rules. So that's one of the things that we need to have incorporated into it.

Stability is very good.

I have no issues with scalability. It's pretty scalable. It makes pretty good use of memory. There are algorithms take a long time to run in R, and somehow they run more efficiently in Modeler. 

One of the things that I have not done in Modeler, and I'm not sure if the capability is there, is to run things in parallel. I'm pretty sure they have it but I haven't used it.

In a certain sense, I was involved in the initial setup. When I joined the university, I started to try to develop a joint agreement between IBM and the university. Because even two or three years ago, IBM was very reluctant to have universities use Modeler at no cost to the faculty or students. Now, fortunately, that has changed. Now our students can have a six-month license. That is very good. I was pushing for that when I was at IBM and then finished pushing for it when I joined the university.

Weigh the pros and cons. A lot of companies do not want to go with SPSS Modeler because of cost. What I have told some of my customers - I do some consulting as part of my job at the university - is, don't look just at the dollars and cents, look at benefits in your use case.

In terms of selecting a vendor, the most important thing to me is the availability of support.

Maybe I'm biased because I used it for a long time at IBM, but I would give it a 10 out of 10.

SPSS Modeler is a friendly interface for a beginner user. This program covers all data preparation and pre-processing techniques. The model can be selected from the recommendation of the program, semi-automatic with predefined parameters for each model (or user-defined), and tuning the appropriated model.

Modeler is the program, which based on the CRISP-DM process, to cover the whole data mining process. It can be modified for the machine learning algorithm by using R or Python code. 

New algorithms are added into every version of Modeler, e.g., SMOTE, random forest, etc. The Derive node is used for the syntax code to derive the data.

Data encoding is friendly for UTF-8. The unstructured data is not appropriate for SPSS Modeler. Finally, the standard package (personal) is not supported for database connection.

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.

The primary use case is to augment our sales processes, to help our call center determine which customers to call, which products to push to those customers. 

Thus far it's been pretty effective. In a recent sample that I pulled, it successfully predicted two-thirds of our sales in a given week.

We're running batch, overnight, and I believe we have three machine-learning models in production at the moment.

We have separate models for our US call center and our UK call center. Each one is designed to do a customer recommendation, where it determines which customers should be ready to buy today, based on the recency of their last purchase, how frequently they purchase. And then it scores the opportunity with that customer, based on how much money they spend with us. It gives the salesmen a ranking of which customers are their biggest opportunity on that day, and they just go down that list and call them. It generates pretty good sales.

And then we have a second model that does item recommendations, based on some association modeling. The association model tells the sales rep what product that customer should be buying, based on their sales purchase history.

We're on-prem. I find the on-prem to be a pretty seamless experience, it flows directly from our data warehouse into the Analytics Server, and then we're able to deploy it back to the data warehouse for deployment into our CRM system.

The benefits are that this product makes us a more efficient sales staff. We're reducing the inefficiencies in the buying patterns of our customers, by calling them when we know they're ready to order, instead of waiting for them to call us. It makes us more effective in our calling practices as well. We're not just cold-calling anymore, we're actually calling customers we know are ready to buy.

In terms of our go-live process changing, I believe we're following some pretty standard practices there. I don't think we've changed too much, other than which servers we were using as production servers.

I think the ease of use in the user interface is the best part of it. The ability to customize some of my streams with R and Python has been very useful to me, I've automated a few things with that.

We don't use SPSS Modeler for governance or security issues.

Regarding visual modeling, it is not the biggest strength of the product, although from what I hear in the latest release it's going to be a lot stronger. I'm excited to see what they have coming down the line, because I know that's an area they've focused on the most recent release, and I'm not on the recent release yet. I haven't really been able to leverage it to make any productivity improvements with our data science or analytic teams. Most of my visualization gets done through Cognos.

Like I said, I'm really excited about the enhanced visualization that I know is coming down the pipeline. That, to me, has always been the biggest flaw in using this. It's very difficult to get good visualization.

I think mapping for geographic data would also be a really great thing to be able to use.

Also, I think it could be marketed better, actually. I think there's a lot of confusion among customers about whether they should be using SPSS Modeler, or DSX. And even some of the partners I've spoken to about it, they've given me some conflicting opinions on which one I should be using at my level of scale.

I haven't had many issues with stability. The only stability concern I ever had was just certain credentials, if the job failed multiple times it deactivated the credentials, and then became a whole process with IT to get the credentials reactivated to get the stream running again.

Scalability is infinite, because it can just spit out straight to our enterprise data warehouse, and we can use that to deploy anywhere.

 I haven't needed technical support. The product works pretty well.

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.

It was a little complex, but the person we work with, Chris Thomas, did a fantastic job walking us through it.

There were just a lot of steps and components to it. We bought the Modeler Gold package, so we had to consider CNDS, we had to consider ADM - we had a whole bunch of different components that had to get set up simultaneously. And when upgrading, we have to upgrade all of those components simultaneously in order to keep using it.

We ended up working directly with an IBM partner, but we also worked with Revelwood and LPA.

I'd give it a nine out of 10. I really think that for someone who is not the strongest programmer on the planet, but is trying to learn and trying to put together some of these basic data science projects, it's a really valuable tool, the UI is very user friendly. So, it definitely launched my journey into becoming a data scientist, and three years later I'm becoming a lot stronger with it.

In terms of advice, the right partner can make all the difference. You need somebody who you can bounce questions off of when you get stuck, because you're going to get stuck, it's just inevitable. If you haven't implemented data science and predictive modeling before, you're always going to hit a challenge that is unique to your data, or to your process, and you need somebody who can lend the weight of experience to just talk you through it.