IBM SPSS Modeler Reviews

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.

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.