IBM SPSS Modeler vs SAS Analytics vs SAS Enterprise Miner comparison

IBM and SAS are both solutions in the Data Mining category. IBM is ranked #3 with an average rating of 8.0, while SAS is ranked #5 with an average rating of 8.5. IBM holds a 16.5% mindshare in DM, compared to SAS’s 8.1% mindshare. Additionally, 86% of IBM users are willing to recommend the solution, compared to 95% of SAS users who would recommend it.

IBM SPSS Modeler

Read 40 IBM SPSS Modeler reviews

3,450 Views
1,656 Comparison Views

86% willing to recommend

SAS Analytics

Read 13 SAS Analytics reviews

1,254 Views
1,241 Comparison Views

95% willing to recommend

SAS Enterprise Miner

Read 13 SAS Enterprise Miner reviews

1,830 Views
659 Comparison Views

93% willing to recommend

IBM SPSS Modeler

SAS Analytics

SAS Enterprise Miner

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Executive Summary

We performed a comparison between IBM SPSS Modeler, SAS Analytics, and SAS Enterprise Miner based on real PeerSpot user reviews.

Find out what your peers are saying about Knime, IBM, Weka and others in Data Mining.

To learn more, read our detailed Data Mining Report (Updated: May 2026).

Buyer's Guide

Data Mining

May 2026

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Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Mindshare comparison

As of May 2026, in the Data Mining category, the mindshare of IBM SPSS Modeler is 16.5%, down from 17.6% compared to the previous year. The mindshare of SAS Analytics is 8.1%, up from 6.6% compared to the previous year. The mindshare of SAS Enterprise Miner is 7.5%, up from 4.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Mining Mindshare Distribution
Product	Mindshare (%)
IBM SPSS Modeler	16.5%
SAS Analytics	8.1%
SAS Enterprise Miner	7.5%
Other	67.9%

Data Mining

Featured Reviews

Prof. Dr. Rudolf Bisping

Business Owner at SASS GmbH

Support and flexibility enable effective project initiation and meet customer needs but deployment requires enhancement

The customer comes to you and says they want to deploy it and make a production out of this, which is very difficult and expensive with IBM SPSS Modeler. With MATLAB, there is no problem. I have a solution, and then I convert my MATLAB solution to C programming language. This I can deploy, and I can check it, and it is MISRA compatible. It is very easy to deploy it, to go from MATLAB to C or C++, which is actually needed in the car industry. In the car industry, they want to have it in the hardware. You cannot put MATLAB or IBM SPSS Modeler in the hardware of a car, but with C, there is no problem with a microcontroller. They can shoot it into the microcontroller, and I can check it with Polyspace, and it is MISRA compatible, which is an industrial standard. There is nothing similar in IBM SPSS Modeler. I made solutions with IBM SPSS Modeler, and then the customer said they wanted to make a production out of it, and it was not possible. I stopped with IBM SPSS Modeler 18. It is now 18.6 from what I know at the moment. I do not believe that there is a possibility to design a graphic user interface with it. It is itself a graphic user interface, where you put all sorts of little icons into the display.

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Sanjay Charitesh Makam

Student at Rochester Institute of Technology

Data management has become faster and clearer but integration and large data handling still need work

SAS Analytics needs to improve for entry-level people in my college because buying licenses is somewhat costly for small businesses. SAS Analytics is also somewhat complex for students who are entering the data field. Additionally, SAS Analytics is not good at integration with other environments, so it should improve on that, and some syntaxes are not good. SAS Analytics has an older architecture in some areas, so it should enhance the UX side and performance in some aspects. SAS Analytics documentation should improve to be clearer for students who are currently using or trying to use SAS Analytics, so the documentation should be simplified. Customer service is pretty good as of now, as they respond in two to four hours on business days, but faster response times may solve many problems and attract more users. I rate SAS Analytics seven or eight out of ten. SAS Analytics should improve in massive data handling, as I mentioned earlier. SAS Analytics struggles when it handles massive data, and it should also get top-tier support. The support should improve from two to four hours down to one hour or even thirty minutes. SAS Analytics should also offer more low-code options for those who do not know how to code or to save more time, and they should consider reducing their pricing. Currently, SAS Analytics has limited flexibility and very limited integration, so improvements in those areas are necessary. SAS Analytics student version does not handle long or lengthy data well right now, so it should improve on that point. As I have mentioned, it struggles with larger data sets, but for current small or medium data sets, it is very good. Customer support should improve to fifteen to thirty minutes or immediate live agents during business hours. The two to four hours wait can sometimes take more than one day, and that does not make sense. We have to stop all our work until they reply if we encounter critical problems, so they should improve for certain.

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reviewer1352853

Executive Head of analytics at a retailer with 5,001-10,000 employees

A stable product that is easy to deploy and can be used for structured and unstructured data mining

We use the solution for predictive analytics to do structured and unstructured data mining I like the way the product visually shows the data pipeline. The product must provide better integration with cloud-native technologies. I have been using the solution for 20 years. The product is very…

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"I think the code modeling features are the most valuable and without the need to write a code back with many different possibilities to choose from. And the second one is linked to the activity of the data preparation."

"We have full control of the data handling process."

"It's a very good and reasonably priced solution."

"It is very scalable for non-technical people."

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

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

"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."

"So far, the stability has been rock solid."

More IBM SPSS Modeler pros

"It is able to connect to all major platforms, and all the smaller platforms that I have come across."

"It has facilitated timely analysis results with quality work and meaningful output."

"I use SAS daily to analyze data, produce reports, and other outputs."

"It has also been around for an extremely long time, has a strong history, and good market penetration."

"It has improved the level of efficacy and validity of our reports, and this has also extended to the area of customer service and relations."

"Modeling ones and figures, such as PROC LIFETEST, PROC LOGISTICS, PROC GPLOT. PROC FREQ and PROC MEANS, are also among the valuable features."

"We have noticed at least a thirty to forty percent saving in time and a thirty to forty percent increase in the efficiency of our work."

"SAS Analytics is a very versatile and comfortable tool and it is powerful because I can work with data from many millions of records, big data with SAS Analytics in a short time versus other software."

More SAS Analytics pros

"Overall it is a good solution."

"Most of the features, especially on the data analysis tool pack, are really good; the way they do clustering and output is great, you can do fairly elaborate outputs, and the results and the ensembles are fantastic."

"Technical support has been good, and when I called them at the start of using the product with some issues they were very helpful."

"The solution is able to handle quite large amounts of data beautifully."

"Performance is excellent."

"I found the ease of use of the solution the most valuable. Additionally, other valuable features include: the user interface, power to extract data, compatibility with other technologies (specifically with PS400), and automation of several tasks."

"The setup is straightforward. Deployment doesn't take more than 30 minutes."

"SAS internal support is very qualified and if we have any issues, we contact them and trust that they can help."

More SAS Enterprise Miner pros

Cons

"Dimension reduction is very important, especially if you are working with millions of recordings and thousands of variables."

"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."

"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."

"The forecasting could be a bit easier."

"I can say the solution is outdated."

"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."

"It would be beneficial if the tool would include more well-known machine learning algorithms."

"Formula writing is not straightforward for an Excel user. Totally new set of functions, which takes time to learn and teach."

More IBM SPSS Modeler cons

"They could enhance the AI capabilities of the product."

"I would like to see their interface to R added to either Base SAS or SAS Analytics."

"SAS Analytics could improve in areas such as handling stress tests and workloads, as well as predicting financial movements, specifically profit and loss movements."

"The training for SAS Business Intelligence is often difficult to arrange. It is often cancelled due to not enough people being enrolled."

"The graphing and visualization features could be enhanced, in my opinion."

"One of the things that can be simplified is self-service analytics, especially for a citizen developer or a citizen data scientist."

"I would like to see their interface to R added to either Base SAS or SAS Analytics."

"This solution should be made more user-friendly. It needs a better visual output to explain logic to senior executives."

More SAS Analytics cons

"The visualization of the models is not very attractive, so the graphics should be improved."

"The solution needs an easier interface for the user. The user experience isn't so easy for our clients."

"The solution is very stable, but we do have some problems with discrepancies involving SAS not matching with the latest Java versions. It's not stable in cases where SAS tries to run on a different version because SAS doesn't connect with the latest Java update. Once a month we need to restart systems from scratch."

"We really don't like the protocols the solution offers. The solution is much more complex than other options."

"The user interface of the solution needs improvement. It needs to be more visual."

"The preparation of both the mining and modeling process could be improved. The solution requires data and will reflect data, but the preparation of the data is not useful for end-users; we ended up having to do the preparation in another tool."

"The user interface of the solution needs improvement. It needs to be more visual."

"The initial setup is challenging if doing it for the first time."

More SAS Enterprise Miner cons

Pricing and Cost Advice

"Its price is okay for a company, but for personal use, it is considered somewhat expensive."

"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. You may feel like you are getting robbed if you can't receive a good discount."

"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."

"The government has funds and a budget, it's hard to say if it's expensive or cheap. In Canada, they have a yearly budget. They used to encourage people to use the modeler for development. If ten users use the server with ten licenses, it runs faster. But if forty users use the same appliance, everything slows down. People then think it's not easy to do things and prefer using remote tools like Python to extract data from the database. It's not about being expensive or cheap, but about people's knowledge and experience in how to do the work."

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

"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."

"$5,000 annually."

"I am using the free version of IBM SPSS Modeler, it is the educational edition version."

More IBM SPSS Modeler pricing and cost advice

"Our licensing covers the usage for around 50 data analysts."

"The cost for SAS Business Intelligence can prove to be a little prohibitive."

"Licensing was rather straightforward."

"I rate the product’s pricing a one out of ten."

"I think that the cost-benefit ratio is okay."

"Setup costs were quite reasonable."

"Prices were comparable with alternative solutions."

"It is relatively expensive. It is not an easy software to afford."

More SAS Analytics pricing and cost advice

"The solution must improve its licensing models."

"This solution is for large corporations because not everybody can afford it."

"The solution is expensive for an individual, but for an enterprise/institution (purchasing bulk licenses), it is not a high price for the use that will come from it."

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Top Industries

By visitors reading reviews

Financial Services Firm

10%

Government

10%

University

Outsourcing Company

Financial Services Firm

25%

University

14%

Construction Company

Outsourcing Company

Financial Services Firm

18%

Construction Company

12%

Educational Organization

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	9
Midsize Enterprise	4
Large Enterprise	32

By reviewers
Company Size	Count
Small Business	4
Midsize Enterprise	2
Large Enterprise	11

By reviewers
Company Size	Count
Small Business	3
Midsize Enterprise	4
Large Enterprise	7

Questions from the Community

What is your experience regarding pricing and costs for IBM SPSS Modeler?

The government has funds and a budget, it's hard to say if it's expensive or cheap. In Canada, they have a yearly bud...

See all answers

What needs improvement with IBM SPSS Modeler?

The customer comes to you and says they want to deploy it and make a production out of this, which is very difficult ...

See all answers

What is your primary use case for IBM SPSS Modeler?

I have been using IBM SPSS Modeler for a long time. I am using IBM SPSS Modeler mainly for ETL. Sometimes I use it to...

See all answers

What needs improvement with SAS Analytics?

With SAS Analytics, I find that the graphs are very poor, and since I use Power BI, I think SAS Analytics needs to en...

See all answers

What is your primary use case for SAS Analytics?

I work with SAS Analytics solutions, specifically with SAS Data Mining, because I extract information from the core s...

See all answers

What is your experience regarding pricing and costs for SAS Analytics?

As a student, I do not pay anything for pricing; my college gives me credentials, and SAS Analytics is free for stude...

See all answers

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Product Reports

Buyer's Guide

IBM SPSS Modeler

May 2026

Download IBM SPSS Modeler product report

Buyer's Guide

SAS Analytics

May 2026

Download SAS Analytics product report

Buyer's Guide

Data Mining

May 2026

Download SAS Enterprise Miner product report

Also Known As

SPSS Modeler

No data available

Enterprise Miner

Overview

IBM SPSS Modeler is a robust tool that facilitates predictive modeling and data analysis through intuitive visual programming and customizable automation, enabling users to streamline data analytics processes with effectiveness.

IBM SPSS Modeler combines ease of use with powerful functionalities, including statistical analysis and quick prototyping. Users can leverage visual programming and drag-and-drop features, making data exploration efficient. Its diverse algorithms and capability to handle large datasets enable comprehensive data cleansing and predictive modeling. Integrating smoothly with Python enhances its versatility. However, improvements in machine learning algorithms, platform compatibility, and visualization tools are necessary. Licensing costs and existing performance issues may require consideration, particularly concerning data extraction and interface convenience.

What are the critical features of IBM SPSS Modeler?

Ease of Use: Features intuitive visual programming, facilitating smooth operation.
Customizable Automation: Supports personalized automation processes.
Visual Programming: Employs drag-and-drop interfaces for efficient task execution.
Integration with Python: Enhances flexibility with Python compatibility.
Diverse Algorithms: Supports a wide range of algorithms for data analysis.
Data Cleansing and Modeling: Enables rigorous data cleansing and predictive modeling.

What benefits or ROI should users consider?

Enhanced Productivity: Streamlined processes improve efficiency.
Flexible Integration: Accommodates R and Python, boosting adaptability.
Scalable Analytics: Capably handles large datasets and complex models.
Quick Prototyping: Facilitates rapid model development.
Improved Insights: Delivers profound analytical insights for decision-making.

IBM SPSS Modeler is implemented across various industries for diverse applications, including data analytics, predictive modeling, and HR analytics. Organizations utilize it to build models for customer segmentation and predictive analysis, leveraging its capabilities for large datasets, research, and educational purposes. It integrates efficiently with cloud and on-premise solutions, enhancing business analytics applications.

IBM

SAS Analytics offers a powerful suite of tools for statistical analysis, predictive analytics, and data handling, making it ideal for industries requiring robust data-driven decisions. Its extensive capabilities cater to professionals familiar with SQL and demand forecasting needs across sectors.

With a strong presence in analytics, SAS Analytics provides a seamless experience for data preparation, exploration, and reporting. Users benefit from its ability to handle large data sets, generate interactive reports, and integrate with multiple platforms. Despite its high costs and need for improved visualization and natural language querying, SAS Analytics remains a favored choice for those requiring comprehensive statistical modeling and risk analytics. Enhancing self-service analytics and accelerating support response times are areas of needed improvement. Companies use it extensively for business intelligence and demand forecasting, particularly in sectors like banking and financial services.

What are the key features of SAS Analytics?

Interactive Reports: Embeds intuitive and engaging reports.
Extensive Analytics Functions: Offers a wide range of analytics capabilities.
SQL Compatibility: Streamlines use for SQL-proficient users.
Predictive Analytics: Enhances analysis with SAS E-Miner.
Automation: Utilizes macros for automated tasks.
Large Data Handling: Manages extensive data effectively.

What benefits can users look for in reviews?

Increased Productivity: Saves time with automation and efficient data processing.
Enhanced Efficiency: Direct data source connections streamline workflows.
Versatility: Adapts to diverse analytical and reporting requirements.
Comprehensive Resources: Provides extensive help and support materials.

SAS Analytics is widely implemented in industries for tasks like national auto insurance pricing, financial replication, and marketing analytics. Teams in banking and financial services apply it for quantitative analyses, risk assessments, and generating detailed operational reports, demonstrating its adaptability and strength in handling complex data scenarios.

SAS

SAS Enterprise Miner enables comprehensive data management and analytics, handling extensive data volumes with diverse algorithms for model creation. Its integration and flexibility in SAS code usage make it suitable for both enterprise and personal use.

SAS Enterprise Miner is recognized for its data pipeline visualization, data processing, and statistical modeling capabilities. Its user-friendly GUI and automation support data mining tasks, decision tree creation, and clustering. However, improvements are needed in its interface visualization, affordability, technical support, and integration with languages like Python and cloud-native tech. Enhanced performance, visualization, and model development auditing, along with text analytics in the main license, are desirable upgrades. Integration with Microsoft SQL and combined offerings remains a priority.

What are SAS Enterprise Miner's most important features?

Data Management: Handles large data volumes efficiently.
Analytics: Integrates diverse algorithms for accurate model creation.
Integration: Supports flexible incorporation of SAS code.
Automation: Enhances data mining tasks through automation.
Clustering: Facilitates extensive clustering operations.

What benefits or ROI should users look for in reviews?

Performance: Delivers remarkable efficiency in data processing tasks.
Compatibility: Seamless integration with other technologies.
Flexibility: Adaptability for enterprise and personal use cases.
User-Friendly: Intuitive GUI aids in simplifying complex tasks.

SAS Enterprise Miner is applied across industries like banking, insurance, and healthcare for data mining, machine learning, and predictive analytics. It aids in activities such as text mining, fraud modeling, and forecasting model creation, handling structured and unstructured data, and performing ad hoc analysis to model business processes and analyze data clusters.

SAS

Sample 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

Aegon, Alberta Parks, Amway China, Axel Springer, Bank of America, Belgium Special Tax, CAP Index, CareSource, CBE Group, Cemig, Center for Responsible Lending, CESCE, Ceska sporitelna, Chantecler, Chico's, Chubb Group of Insurance Companies, CIGNA Thailand, City of Wiesbaden, Germany, Confused.com, Creditreform, Des Moines Area Community College, Deutsche Lufthansa, Directorate of Economics and Statistics, DIRECTV, Dow Chemical Company, Dow Chemical Company, Dun & Bradstreet, EDF Energy, Electrabel GDF SUEZ, ERGO Insurance Group, Erste Bank Croatia, Farmers Mutual Group, Finnair, Florida Department of Corrections, Geneia, Generali Hellas, Genting Malaysia Berhad, Grameenphone, Grandi Salumifici Italiani, HealthPartners, Highmark, Hong Kong Efficiency Unit, HP, Hyundai Securities, Illinois Department of Healthcare and Family ServicesInc Research, ING-DiBa, Institut Pertanian Bogor, InterContinental Hotels Group (IHG), IOM, Kelley Blue Book, Lenovo, Lillebaelt Hospital, Los Angeles County, Maspex Wadowice Group, National Bank of Greece, New Zealand Ministry of Health, New Zealand Ministry of Social Development, Nippon Paper, NMIMS, North Carolina Department of Transportation, North Carolina Office of Information Technology Services, Northern Virginia Electric Cooperative (NOVEC), Oberweis Dairy, ODEC, Ohio Mutual Insurance Group, Oklahoma State University, OneBeacon, Orange Business Services, Orange County Child Support Services, Organic, Orlando Magic, OTP Bank, Plano Independent School District, Project Odyssey, Royal Society for the Protection of Birds, RSA Canada, SCAD, Scotiabank, Singapore National Library Board, Sobeys Inc., SRA International, Staples, Statistics Estonia, Swisscom, SymphonyIRI Group, Telecom Italia, Telef‹nica O2, Town of Cary, Transitions Optical, TrueCar, Turkcell Superonline, UniCredit Bank Serbia, University of Alabama, University of Missouri, USDA National Agricultural Statistics Service

Generali Hellas, Gitanjali Group, Gloucestershire Constabulary, GS Home Shopping, HealthPartners, IAG New Zealand, iJET, Invacare

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