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IBM SPSS Modeler OverviewUNIXBusinessApplication

IBM SPSS Modeler is #3 ranked solution in top Data Mining tools and #7 ranked solution in top Data Science Platforms. PeerSpot users give IBM SPSS Modeler an average rating of 8 out of 10. IBM SPSS Modeler is most commonly compared to KNIME: IBM SPSS Modeler vs KNIME. IBM SPSS Modeler is popular among the large enterprise segment, accounting for 64% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a comms service provider, accounting for 22% of all views.
IBM SPSS Modeler Buyer's Guide

Download the IBM SPSS Modeler Buyer's Guide including reviews and more. Updated: June 2022

What is IBM SPSS Modeler?

IBM SPSS Modeler is an extensive predictive analytics platform that is designed to bring predictive intelligence to decisions made by individuals, groups, systems and the enterprise. By providing a range of advanced algorithms and techniques that include text analytics, entity analytics, decision management and optimization, SPSS Modeler can help you consistently make the right decisions from the desktop or within operational systems.

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https://www.ibm.com/products/spss-modeler/pricing
 
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IBM SPSS Modeler was previously known as SPSS Modeler.

IBM SPSS Modeler Customers

Reisebªro Idealtours GmbH, MedeAnalytics, Afni, Israel Electric Corporation, Nedbank Ltd., DigitalGlobe, Vodafone Hungary, Aegon Hungary, Bureau Veritas, Brammer Group, Florida Department of Juvenile Justice, InSites Consulting, Fortis Turkey

IBM SPSS Modeler Video

Archived IBM SPSS Modeler Reviews (more than two years old)

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Program Director at ABRS
Real User
GUI and flow management are helpful features but its weak documentation requires improvement

What is our primary use case?

Evaluation for training and consulting.

What is most valuable?

GUI and flow management. 

What needs improvement?

Weak documentation and user guide.

For how long have I used the solution?

Still implementing.
Disclosure: I am a real user, and this review is based on my own experience and opinions.
AltanAtabarut - PeerSpot reviewer
Founding Partner at Altdata Analytics
Real User
Top 20
Automated modelling, classification, or clustering are very useful. Customer support is hard to contact.
Pros and Cons
  • "Automated modelling, classification, or clustering are very useful."
  • "A lot of jobs that are stuck in Excel due to the huge numbers of rows are tackled pretty quickly."
  • "Customer support is hard to contact."
  • "It is not integrated with Qlik, Tableau, and Power BI."
  • "Expensive to deploy solutions. You need to buy an extra deployment unit."

What is our primary use case?

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...

How has it helped my organization?

  • 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.

What is most valuable?

  • 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.

What needs improvement?

  • 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.

For how long have I used the solution?

Three to five years.

What do I think about the stability of the solution?

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.

What do I think about the scalability of the solution?

The server is not cheap and not scalable enough.

How are customer service and technical support?

Hard to contact and get any benefit.

Which solution did I use previously and why did I switch?

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.

How was the initial setup?

Setup is a little problematic for desktop. A nightmare for server.

What about the implementation team?

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.

What was our ROI?

It got us a good amount of money with quick and efficient modeling.

It earns its money before the year-end.

What's my experience with pricing, setup cost, and licensing?

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.

Which other solutions did I evaluate?

I checked out RapidMiner, which is a good alternative. However, SPSS Modeler is more capable and automated. 

What other advice do I have?

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.

Disclosure: I am a real user, and this review is based on my own experience and opinions.
Buyer's Guide
IBM SPSS Modeler
June 2022
Learn what your peers think about IBM SPSS Modeler. Get advice and tips from experienced pros sharing their opinions. Updated: June 2022.
611,060 professionals have used our research since 2012.
Miguel  Villalobos - PeerSpot reviewer
Director - Institute of Advanced Analytics at a university with 1,001-5,000 employees
Real User
Drag and drop makes it very easy to build and test streams
Pros and Cons
  • "It's very easy to use. The drag and drop feature makes it very easy when you are building and testing the streams. That's very useful."
  • "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."
  • "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."

What is our primary use case?

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.

How has it helped my organization?

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.

What is most valuable?

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.

What needs improvement?

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.

What do I think about the stability of the solution?

Stability is very good.

What do I think about the scalability of the solution?

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.

How was the initial setup?

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.

What other advice do I have?

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.

Disclosure: I am a real user, and this review is based on my own experience and opinions.
Suebkul  Kanchanasuk - PeerSpot reviewer
Lecturer at School of Science, University of Phayao
Real User
New algorithms are added into every version of it
Pros and Cons
  • "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."
  • "The standard package (personal) is not supported for database connection."
  • "Unstructured data is not appropriate for SPSS Modeler."

What is our primary use case?

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.

How has it helped my organization?

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. 

What is most valuable?

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.

What needs improvement?

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.

For how long have I used the solution?

One to three years.
Disclosure: I am a real user, and this review is based on my own experience and opinions.
it_user841950 - PeerSpot reviewer
Vp, Data And Analytics at a financial services firm with 1,001-5,000 employees
Real User
Saves us notable time in our go-live process
Pros and Cons
  • "We use analytics with the visual modeling capability to leverage productivity improvements."
  • "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"

    What is our primary use case?

    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.

    How has it helped my organization?

    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.

    What is most valuable?

    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.

    What needs improvement?

    New features are always welcome, but I’m not the core person. A separate team can comment on this, but not me.

    What do I think about the stability of the solution?

    There are issues, we try to mitigate them. There are always issues. We’re trying to be stable but there are a few areas...

    What do I think about the scalability of the solution?

    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.

    How are customer service and technical support?

    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.

    Which solution did I use previously and why did I switch?

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

    What other advice do I have?

    I would say, look through every product in the market, like we do, and try to pick what works best.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    it_user841890 - PeerSpot reviewer
    Business Intelligence Manager at a manufacturing company with 1,001-5,000 employees
    Real User
    Ease of use, the user interface, is the best part; the ability to customize streams with R and Python is useful
    Pros and Cons
    • "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."
    • "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. 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."

    What is our primary use case?

    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.

    How has it helped my organization?

    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.

    What is most valuable?

    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.

    What needs improvement?

    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.

    What do I think about the stability of the solution?

    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.

    What do I think about the scalability of the solution?

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

    How are customer service and technical support?

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

    Which solution did I use previously and why did I switch?

    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.

    How was the initial setup?

    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.

    Which other solutions did I evaluate?

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

    What other advice do I have?

    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.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    Product Manager at a financial services firm with 10,001+ employees
    Real User
    Very stable, with point and click usability, although not as user friendly as it could be
    Pros and Cons
    • "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."
    • "It's not as user friendly as it could be."

    What is our primary use case?

    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.

    How has it helped my organization?

    I can't say that our go-live has changed compared to a previously problematic base process.

    What is most valuable?

    We don't use IBM SPSS Modeler for governance and security issues. I can't talk about the visual modeling capability.

    For how long have I used the solution?

    Three to five years.

    What do I think about the stability of the solution?

    It's very stable, although it is not as user friendly as it could be.

    What do I think about the scalability of the solution?

    I don't use it for any high performance applications.

    How are customer service and technical support?

    I have not used technical support.

    Which solution did I use previously and why did I switch?

    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.

    How was the initial setup?

    I was not involved in the initial setup.

    Which other solutions did I evaluate?

    Usually open-source solutions.

    What other advice do I have?

    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.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    it_user841905 - PeerSpot reviewer
    Dealer Analytics Product & Services Manager at a manufacturing company with 1,001-5,000 employees
    Real User
    A very flexible platform, handles R and Python and other types of technology
    Pros and Cons
    • "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."

      What is our primary use case?

      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.

      What is most valuable?

      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.

      What needs improvement?

      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.

      For how long have I used the solution?

      More than five years.

      What do I think about the stability of the solution?

      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.

      What do I think about the scalability of the solution?

      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.

      How is customer service and technical support?

      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.

      What other advice do I have?

      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.

      Disclosure: I am a real user, and this review is based on my own experience and opinions.
      Charles-Antoine Drouin - PeerSpot reviewer
      Bi Analyst at Health Canada
      Real User
      Stability is good, but we have run into a few problems doing some entity matching/analytics
      Pros and Cons
      • "Stability is good."
      • "We are using it either for workforce deployment or to improve our operations."
      • "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 have run into a few problems doing some entity matching/analytics."
      • "​Initial setup of the software was complex, because of our own problems within the government."

      What is our primary use case?

      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. 

      How has it helped my organization?

      It has not yet improved my organization.

      What is most valuable?

      The muddling capabilities to help us find some trends.

      What needs improvement?

      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.

      What do I think about the stability of the solution?

      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.

      What do I think about the scalability of the solution?

      It is hard to define at this stage.

      How are customer service and technical support?

      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.

      Which solution did I use previously and why did I switch?

      We were not previously using a different solution.

      How was the initial setup?

      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.

      Which other solutions did I evaluate?

      I was not a part of the contracting and RFP phase.

      What other advice do I have?

      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.

      Disclosure: PeerSpot contacted the reviewer to collect the review and to validate authenticity. The reviewer was referred by the vendor, but the review is not subject to editing or approval by the vendor.
      it_user841911 - PeerSpot reviewer
      IT Specialist at a government with 51-200 employees
      Real User
      We have been able to do predictive modeling with it
      Pros and Cons
      • "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."
      • "We have been able to do some predictive modeling with it"
      • "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."

      What is our primary use case?

      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.

      How has it helped my organization?

      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.

      What is most valuable?

      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.

      What needs improvement?

      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. 

      What do I think about the stability of the solution?

      It seems very stable. SPSS seems like a very mature product. We have not had any issues with it at all.

      What do I think about the scalability of the solution?

      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.

      How are customer service and technical support?

      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.

      Which solution did I use previously and why did I 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.

      How was the initial setup?

      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. 

      What's my experience with pricing, setup cost, and licensing?

      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.

      Which other solutions did I evaluate?

      Just IBM.

      What other advice do I have?

      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.

      Disclosure: I am a real user, and this review is based on my own experience and opinions.
      it_user840903 - PeerSpot reviewer
      Enterprise analytics manager at a healthcare company with 10,001+ employees
      Real User
      The visual modeling capability is one of its attractive features
      Pros and Cons
      • "It is very scalable for non-technical people."
      • "The visual modeling capability is one of its attractive features."
      • "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."
      • "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."
      • "The biggest issue with the visual modeling capability is that we can't extract the SQL code under the hood."

      What is our primary use case?

      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.

      How has it helped my organization?

      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. 

      What is most valuable?

      The visual modeling capability is one of its attractive features.

      What needs improvement?

      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. 

      For how long have I used the solution?

      Three to five years.

      What do I think about the stability of the solution?

      It is not consistently stable. I hope they plan on improving it. C&DS is not stable at all.

      What do I think about the scalability of the solution?

      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.

      How is customer service and technical support?

      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. 

      How was the initial setup?

      It is very easy to set up. Once we deployed it and got the license code registered, it was fine. 

      Which other solutions did I evaluate?

      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. 

      Disclosure: I am a real user, and this review is based on my own experience and opinions.
      it_user840873 - PeerSpot reviewer
      Analyst at American Airlines
      Real User
      Streamlines projecting models and forecasting, gives us new, faster learning capabilities

      What is our primary use case?

      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.

      What is most valuable?

      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.

      What needs improvement?

      We're still learning, the beginning of the application. We haven't played with all the features to be able to say.

      For how long have I used the solution?

      Trial/evaluations only.

      What do I think about the stability of the solution?

      So far so good. We're still learning a lot of the capabilities.

      What do I think about the scalability of the solution?

      I do not know, that's more on the developer side.

      Which solution did I use previously and why did I switch?

      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.

      Which other solutions did I evaluate?

      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.

      What other advice do I have?

      Give it a try, start with a proof of concept, and see where it leads.

      Disclosure: I am a real user, and this review is based on my own experience and opinions.
      VP at a aerospace/defense firm with 10,001+ employees
      Real User
      Our local representative will help guide us through the PoC process

      What is our primary use case?

      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. 

      What is most valuable?

      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. 

      For how long have I used the solution?

      Trial/evaluations only.

      What do I think about the scalability of the solution?

      It is pretty scalable.

      How was the initial setup?

      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. 

      What about the implementation team?

      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.

      Which other solutions did I evaluate?

      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.

      Disclosure: I am a real user, and this review is based on my own experience and opinions.
      it_user840852 - PeerSpot reviewer
      Director of Engineering at a logistics company with 1,001-5,000 employees
      Real User
      We are creating models and putting them into production faster than we would if we had gone with a strictly code-based solution
      Pros and Cons
      • "It scales. I have not run into any challenges where it will not perform.​"
      • "​It works fine. I have not had any stability issues; it is always up.​"
      • "We are creating models and putting them into production much faster than we would if we had just gone with a strict, code-based solution, like R or Python."
      • "​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.​"

      What is our primary use case?

      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.

      How has it helped my organization?

      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.

      What is most valuable?

      • The ability to quickly prototype. 
      • The integration into all the existing environments. 
      • The ability to not have to manage a lot of code.

      What needs improvement?

      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.

      What do I think about the stability of the solution?

      It works fine. I have not had any stability issues; it is always up.

      What do I think about the scalability of the solution?

      It scales. I have not run into any challenges where it will not perform.

      How are customer service and technical support?

      Technical support is great - 90% of the time.

      Which solution did I use previously and why did I switch?

      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.

      How was the initial setup?

      The initial setup was straightforward. Though, I had done it before.

      What was our ROI?

      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.

      Which other solutions did I evaluate?

      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.

      What other advice do I have?

      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.

      Disclosure: I am a real user, and this review is based on my own experience and opinions.
      Unit Manager at a insurance company with 1,001-5,000 employees
      Real User
      Gives us best results in customer segmentation and churn analysis, improves retention
      Pros and Cons
      • "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."
      • "It would be helpful if SPSS supported open-source features, for example, embedding R or Python scripts in SPSS Modeler."

      What is our primary use case?

      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.

      How has it helped my organization?

      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.

      What is most valuable?

      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.

      What needs improvement?

      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.

      What do I think about the stability of the solution?

      We haven't suffered from any stability issues. It's a stable product.

      What do I think about the scalability of the solution?

      We haven't had any performance problems. The product runs every data volume performantly and produces results.

      How are customer service and technical support?

      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.

      Which solution did I use previously and why did I switch?

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

      How was the initial setup?

      Straightforward. It was not complex.

      Which other solutions did I evaluate?

      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.

      What other advice do I have?

      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.

      Disclosure: I am a real user, and this review is based on my own experience and opinions.
      it_user840840 - PeerSpot reviewer
      Analyst at a transportation company with 10,001+ employees
      Real User
      It will scale up to anything we need
      Pros and Cons
      • "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."
      • "So far, the stability has been rock solid."
      • "It will scale up to anything we need."
      • "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."

      What is our primary use case?

      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.

      How has it helped my organization?

      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.

      What is most valuable?

      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.

      What needs improvement?

      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!

      For how long have I used the solution?

      Still implementing.

      What do I think about the stability of the solution?

      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.

      What do I think about the scalability of the solution?

      It will scale up to anything we need.

      How are customer service and technical support?

      We have not used the technical support yet.

      Which solution did I use previously and why did I switch?

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

      How was the initial setup?

      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.

      Which other solutions did I evaluate?

      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.

      What other advice do I have?

      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.

      Disclosure: I am a real user, and this review is based on my own experience and opinions.
      PeerSpot user
      Senior Operations Manager – Serviceablity and Insights at a manufacturing company with 10,001+ employees
      Real User
      It Has Improved Our Organization With Its Quickness and Ease of Use With the Guarantee of Robust Modeling Techniques and Trustworthy Accuracy.

      What is our primary use case?

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

      How has it helped my organization?

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

      What is most valuable?

      Quick insights.

      What needs improvement?

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

      For how long have I used the solution?

      More than five years.
      Disclosure: I am a real user, and this review is based on my own experience and opinions.
      it_user766611 - PeerSpot reviewer
      Graduate Assistant
      Vendor
      Performed well for statistical hypothesis analysis but doesn't offer enough languages

      What is our primary use case?

      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. 

      What needs improvement?

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

      What was my experience with deployment of the solution?

      The instillation was easy.

      What do I think about the stability of the solution?

      The stability was good.

      What do I think about the scalability of the solution?

      It was scalable.

      Which solution did I use previously and why did I switch?

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

      What other advice do I have?

      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. 

      Disclosure: I am a real user, and this review is based on my own experience and opinions.
      it_user766605 - PeerSpot reviewer
      Clinical Assistant Professor at a university
      Vendor
      I really like the functionality that includes R and Python nodes
      Pros and Cons
      • "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."
      • "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."

      What is our primary use case?

      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 is most valuable?

      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.

      How has it helped my organization?

      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.

      What needs improvement?

      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.

      What do I think about the stability of the solution?

      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.

      What do I think about the scalability of the solution?

      It's the same situation as the stability.

      How is customer service and technical support?

      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.

      How was the initial setup?

      I was not involved in the initial setup but the upgrades are straightforward. They do not give me any problems.

      What other advice do I have?

      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.

      Disclosure: I am a real user, and this review is based on my own experience and opinions.
      it_user766578 - PeerSpot reviewer
      Product Team at a healthcare company with 11-50 employees
      Real User
      It is easy to use and robust but does not offer a lot of variation

      What is our primary use case?

      I use it for research. It is able to do the basics but does not offer much variation. There are not a lot of options.

      What is most valuable?

      It is easy to use and robust.

      What needs improvement?

      When I do clustering, I want to try a different stream, but currently the only thing that I can really pick is averaging. There are no other options. I would like to see more options added and some modeling things.

      What do I think about the stability of the solution?

      The stability is good.

      What do I think about the scalability of the solution?

      The scalability is good.

      Which other solutions did I evaluate?

      JMP, because my team is moving toward SAS. I don't quite like SAS because of the user interface, but now they have JMP which is graphical and I like it.

      What other advice do I have?

      I would recommend SPSS to someone who has just started trying to run a lot of modeling, it's a good starting point. It is very easy to use and will do the basics.

      It does what it needs to do, but it's simple. If I want to modify things, then maybe I need to find something else.

      Disclosure: I am a real user, and this review is based on my own experience and opinions.
      it_user766575 - PeerSpot reviewer
      Research Assistant
      Vendor
      The stability is good though there have been occasional crashes
      Pros and Cons
      • "I think it is the point and drag features that are the most valuable. You can simply click at the windows, and then pull up the functions."
      • "If IBM could add some of the popular models into the SPSS for further analysis, like popular regression models, I think that would be a helpful improvement."

      What is our primary use case?

      I used it mostly for the PCA, the principal component analysis, and I have been using that for my bachelor's thesis. It performed pretty well for my task, for the goal of my task.

      What is most valuable?

      I think it is the point and drag features that are the most valuable. You can simply click at the windows, and then pull up the functions. What you need is then directly over there, and then you can select the parameters over the windows. Then just click and the results show up.

      What needs improvement?

      If IBM could add some of the popular models into the SPSS for further analysis, like popular regression models, I think that would be a helpful improvement. 

      I would also like have more options to manipulate the interface of the report, as well as to be able to customize it and make it more personalized. Right now, SPSS doesn't give me that ability to do that.

      For how long have I used the solution?

      Six months.

      What do I think about the stability of the solution?

      The stability is good. There have been the occasional crashes, when the data goes all over or I have really messed up with the process and it just crashes. 

      What do I think about the scalability of the solution?

      The scalability is okay but has some limitations.

      Which solution did I use previously and why did I switch?

      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.

      How was the initial setup?

      The setup was straightforward.

      What's my experience with pricing, setup cost, and licensing?

      If you are in a university and the license is free then you can use the tool without any charges, which is good.

      Which other solutions did I evaluate?

      I don't think my university looked into any other vendors. We are providing licenses to students and faculty members.

      What other advice do I have?

      If I had a colleague that was looking for a status analyzer I would recommend that they get started with SPSS. And if they want to go further I would suggest another option like SAS. Because, once again, you'll have more power to develop the algorithms, to improve the algorithms.

      Disclosure: I am a real user, and this review is based on my own experience and opinions.
      Altan Atabarut - PeerSpot reviewer
      Founding Partner at a tech services company with 1-10 employees
      Real User
      A lot of jobs are tackled pretty quickly due to the automated reporting data preparation. ​Server installation was too hard and ineffective.
      Pros and Cons
      • "Some basic form of feature engineering for classification models. This really quickens the model development process."
      • "Formula writing is not straightforward for an Excel user. Totally new set of functions, which takes time to learn and teach."

      What is most valuable?

      • Automated data preparation
      • Some basic form of feature engineering for classification models. This really quickens the model development process.
      • Automated modelling, classification, or clustering are very useful as well.

      How has it helped my organization?

      Automated reporting data preparation: A lot of jobs, which were stuck in Excel due to huge numbers of rows, are now tackled pretty quickly.

      What needs improvement?

      • Formula writing is not straightforward for an Excel user. Totally new set of functions, which takes time to learn and teach.
      • Automating procedures: Writing macros is not easy and very hard to learn.
      • Unfortunately, it’s not integrated with Qlik, Tableau, and Power Bi.
      • Expensive to deploy solutions. You need to buy an extra deployment unit.

      For how long have I used the solution?

      I have been a user since 2010, when it was named Clementine, and worked with versions 15, 17, and 18.

      What do I think about the stability of the solution?

      This tool has a lot of bugs. The canvas gets crippled when you put on a lot of icons. You have to save, close, and open the stream from scratch.

      When there is a change in regional settings, menus and so on don’t pop up, probably due to software developed with Java.

      What do I think about the scalability of the solution?

      Server installation was too hard and ineffective. You need at least four to five concurrent users with huge data to benefit from a server, but the salespeople never tell you that and just try to oversell.

      How is customer service and technical support?

      Really bad local support. It took a long time for support to install a server.

      Disclosure: I am a real user, and this review is based on my own experience and opinions.
      PeerSpot user
      Quantitative Researcher at a financial services firm with 10,001+ employees
      Vendor
      Extremely easy to use, it offers a generous selection of proprietary machine learning algorithms with advanced tuning capabilities and integration with Python.
      Pros and Cons
      • "Extremely easy to use, it offers a generous selection of proprietary machine learning algorithms."
      • "It would be beneficial if the tool would include more well-known machine learning algorithms."

      What is most valuable?

      Extremely easy to use, it offers a generous selection of proprietary machine learning algorithms with advanced tuning capabilities and integration with Python.

      How has it helped my organization?

      Customer related tasks, such as: lifetime value, acquisition, retention, cross-sell and up-sell, segmentation. Fraud detection, recommender systems, sentiment analysis, and many more.

      What needs improvement?

      It would be beneficial if the tool would include more well-known machine learning algorithms. However, IBM already started to include several of these algorithms by implementing Python code. Plus, each user is capable of implementing his/her own machine learning algorithm in either Python or R.

      For how long have I used the solution?

      Since 2009.

      What do I think about the stability of the solution?

      The product is very stable. There were however few instances when too intensive tasks were performed and the tool froze and the unsaved activity was lost. I understood that in the latest version IBM addressed this issue and an automated saving capability of the workflow is available.

      What do I think about the scalability of the solution?

      The work can be easily scaled even without additional components offered by IBM, but it really depends on each organization. Some research is necessary in order to understand how to bypass those components, but in the end a substantial amount of money would be saved. IBM provides documentation regarding each component that SPSS Modeler could interact with.

      How are customer service and technical support?

      Based on the licensing purchased technical support is provided and is of great value. IBM SPSS Modeler offers a very comprehensive online documentation too.

      Which solution did I use previously and why did I switch?

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

      How was the initial setup?

      To local setup of each license is straightforward. If an organization plans on sharing workflows amongst team members or automate tasks, additional components need to be purchased and set up. I have not used other components provided by IBM, but most likely the setup of those would be straightforward too.

      What's my experience with pricing, setup cost, and licensing?

      Having in mind all four tools from Garner’s top quadrant, the pricing of this tool is competitive and it reflects the quality that it offers. I am of the impression that the price can be adjusted depending on the plans that each organization has.

      With respect to the licensing, there are different options available and it really depends on the needs of each organization. However, I believe the basic licensing is suitable for most of the cases.

      Which other solutions did I evaluate?

      I evaluated and tested SAS Enterprise Miner and KNIME.

      What other advice do I have?

      It is a great tool even for an individual with no or basic predictive modeling experience. Due to the very detailed online documentation and examples that IBM SPSS Modeler provides, even a novice employee can start using the tool and become productive in a short period of time. When it comes to advanced users that prefer to code in Python or R, IBM SPSS Modeler offers the capability to write Python or R code and create nodes for each specific task that can be easily reused just by drawing and dropping the nodes on the canvas.

      Disclosure: I am a real user, and this review is based on my own experience and opinions.
      PeerSpot user
      CEO with 1,001-5,000 employees
      Vendor
      First Look – IBM In-Database Analytics

      IBM SPSS has been supporting in-database analytic modeling for a while now. Their objective is to make it possible for analysts to run the complete data mining process end-to-end in-database – from accessing the data to data transformation and model building/scoring. In particular, they try to enable analysts to push data transformation and data preparation into the database as these are typically a big part of data mining projects. To achieve in-database execution they provide three main features – SQL Pushback, direct access to a database’s own analytic modeling routines and model deployment/scoring options.

      To build a predictive analytic model in IBM SPSS Modeler, an analyst creates an analytic workflow. This consists of multiple tasks or nodes to read, merge or transform data; split data into different test sets; apply modeling algorithms and more. SQL Pushback takes the nodes in this workflow that relate to data access and transformation and pushes them to the database. The tool generates the SQL you need for these steps and executes that SQL on the database from which you sourced the data. This SQL is specific to the database concerned for the main supported databases (IBM DB2, Microsoft SQL Server, Netezza, Oracle, Teradata) and generic SQL is available for many nodes for other databases.

      IBM SPSS Modeler also reorders work streams to maximize the effectiveness of this SQL, particularly in terms of keeping the data in the database. For instance if multiple nodes that can be executed in-database are separated by one that cannot be then the nodes will be re-ordered to group the in-database nodes where this is possible.

      When In-database execution is possible for a node in the workflow it is color-coded purple to show this – modelers with strong database servers will try and turn “all the nodes purple” so that everything is being done in database. Some customers write raw SQL to use more extended functions like statistics functions that would not automatically be pushed back. SQL Pushback can be turned off so that high load production environments don’t get slowed by in-database analytics and users can decide to cache intermediate results in a database table simply by selecting a node and asking for caching.

      The second element of in-database analytic modeling is to build the model itself in-database. For this IBM SPSS Modeler Building use the analytic routines in Oracle (the ODM algorithms), Microsoft SQL Server, DB2 and InfoSphere Warehouse as well as (since the 14.2 release in June) Netezza. These in-database algorithms are presented as new node types in the workflow, allowing a modeler to simply select them as part of their usual workflow. In addition IBM SPSS Modeler has its own algorithms that can be used on the modeling server. These in-database algorithms allow data in the database to be scored and some allow the model to be calculated live when the record with which it is associated is retrieved. These in-database algorithms are typically parallelized by the database vendor and IBM SPSS Modeler inherently takes advantage of this.

      IBM SPSS Modeler supports a number of other deployment options besides the use of these in-database routines. A number of the standard IBM SPSS routines can generate SQL for scoring in-database – the model is built outside the database but the SQL allows the scoring to be done in-database once the model is built. Several of these routines support parallel execution on the modeling server. Models, no matter how they were built, can also be deployed using Scoring Services and made available using a web services interface for live scoring. Models can also be deployed using IBM SPSS Decision Management.

      Don’t forget the Decision Management Technology Map

      Disclosure: I am a real user, and this review is based on my own experience and opinions.
      PeerSpot user
      CEO with 1,001-5,000 employees
      Vendor
      First Look: IBM SPSS Modeler Update

      It has been a while since I was updated on IBM SPSS Modeler and I got an update from IBM recently. IBM SPSS Modeler is, of course, IBM’s primary data mining and predictive analytics workbench. It uses a standard workflow metaphor, letting you string together nodes that process data, run algorithms, score data, etc. Both structured and unstructured data are supported, analytic tasks can be pushed back into your database or data warehouse and IBM SPSS Modeler produces the models that are consumed in IBM Analytical Decision Management. IBM SPSS Modeler comes in Professional and Premium editions, with the Premium version supporting unstructured data with text analytics, as well as entity analytics and social network analysis.

      The most recent release is IBM SPSS Modeler 15. This added improvements in four areas:

      • Entity Analytics
        This functionality identifies whether two entities are really the same or not. Organizations often struggle with multiple entries that should be linked, but are not – multiple CRM records, for instance. In some scenarios, like fraud, organizations often have to find links that someone is deliberately obscuring. Entity Analytics uses “context accumulation” – the consideration of the things around something – to make it more actionable. In IBM SPSS Modeler, the Entity Analytics engine maps various data sources into the repository (to identify that fields are meant to contain similar data, like a phone number or middle name) and then matches entities in the different data sources to create a resolved or composite entity. Nodes in the workflow can process new cases and update the repository or take all of the resolved entities and process them through additional nodes. Users can control how aggressive the mapping is (how much of a match it needs) and will continually reconsider as new data arrives.
      • Big Data Analytics
        IBM SPSS Modeler is typically deployed as a client-server architecture and supports in-database mining using SQL pushback. The latest release added support for new databases such as SAP HANA and EMC Greenplum and allows users to leverage database UDFs in a model stream. Additional support for in-database algorithms from IBM Netezza was added in version 15, extending existing support (IBM InfoSphere Warehouse, Oracle Data Miner and Microsoft SQL Server algorithms are also supported). Release 15 also added more support for in-database scoring through generated UDFs using Scoring Adapters (previously only certain models could be pushed back for in-database scoring) with support for Teradata, IBM Netezza and DB2 for z/OS. Predictive Techniques and Visualizations
        Social network analysis was added to identify groups and the leaders of those groups (group analysis) from connection data, such as call detail records. It can also use existing churn information to see who the churner might influence to also leave (diffusion analysis). Generalized Linear Mixed Models, previously supported in IBM SPSS Statistics, were added, as were various mapping visualizations (coordinates, regions, minicharts on maps).
      • Various Productivity Enhancements
        Various usability improvements, improved functionality around stream parameters and data import among others, plus improved integration with IBM SPSS Statistics and IBM Cognos Business Intelligence.

      IBM SPSS Modeler is one of the products in our Decision Management Systems Platform Technologies report and you can get more information on IBM SPSS Modeler here.

      Disclosure: I am a real user, and this review is based on my own experience and opinions.
      it_user6549 - PeerSpot reviewer
      BI Expert at a university with 501-1,000 employees
      Vendor
      Great predictive analytics software, very powerful

      Valuable Features:

      The software is robust with advance statistical tools in hand from time series analysis to logistic regression, it can be used by banks for fraud detection, by convenience stores for market basket analysis, for cluster analysis on customer segmentation.

      Room for Improvement:

      The software is quite expensive and IBM is currently marketing its other digital dashboard tools such as IBM cognos, now we aren't sure on the plans of IBM integrating these two softwares.

      Other Advice:

      It's a great software for advanced users, users should have a background in statistics, sql server and strategic planning, preferably the software is for strategic planning offices and business intelligence units.
      Disclosure: I am a real user, and this review is based on my own experience and opinions.
      Buyer's Guide
      Download our free IBM SPSS Modeler Report and get advice and tips from experienced pros sharing their opinions.
      Updated: June 2022
      Buyer's Guide
      Download our free IBM SPSS Modeler Report and get advice and tips from experienced pros sharing their opinions.