Compare IBM SPSS Modeler vs. IBM SPSS Statistics

IBM SPSS Modeler is ranked 2nd in Data Mining with 17 reviews while IBM SPSS Statistics is ranked 3rd in Data Mining with 8 reviews. IBM SPSS Modeler is rated 8.2, while IBM SPSS Statistics is rated 7.6. The top reviewer of IBM SPSS Modeler writes "Ease of use, the user interface, is the best part; the ability to customize streams with R and Python is useful". On the other hand, the top reviewer of IBM SPSS Statistics writes "Has many existing algorithms that we can use but it should have the ability to create higher-level presentations". IBM SPSS Modeler is most compared with KNIME, Alteryx and IBM Watson Studio, whereas IBM SPSS Statistics is most compared with IBM SPSS Modeler, MathWorks Matlab and Weka. See our IBM SPSS Modeler vs. IBM SPSS Statistics report.
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Most Helpful Review
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Find out what your peers are saying about IBM SPSS Modeler vs. IBM SPSS Statistics and other solutions. Updated: January 2020.
390,245 professionals have used our research since 2012.
Quotes From Members

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

Pros
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.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.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.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 itThe 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.

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The most valuable feature is the user interface because you don't need to write code.It has the ability to easily change any variable in our research.They have many existing algorithms that we can use and use effectively to analyze and understand how to put our data to work to improve what we do.The solution is very comprehensive, especially compared to Minitabs, which is considered more for manufacturing. However, whatever data you want to analyze can be handled with SPSS.You can find a complete algorithm in the solution and use it. You don't need to write your own algorithms for predictive analytics. That's the most valuable feature and the main one we use.The best part is that they have an algorithm handbook, so you can open it up and understand how it works, and if it is useful, this is very important.Some of the most valuable features that we are using with some business models are machine learning algorithms, statistical models given to us by the business, and getting data from the database or text files.Most of the product features are good but I particularly like the linear regression analysis.

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Cons
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.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.The standard package (personal) is not supported for database connection.Unstructured data is not appropriate for SPSS Modeler.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.

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This solution is not suitable for use with Big Data.The design of the experience can be improved.The product should provide more ways to import data and export results that are user-friendly for high-level executives.One of the areas that should be similar to Minitabs is the use of blogs. The Minitabs blog helps users understand the tools and gives lots of practical examples. Following the SPSS manual is cumbersome. It's a good, exhaustive manual, but it's not practical to use. With Minitabs, you can go to the blogs and find specific articles written about various components and it's very helpful. Without blogs, we find SPSS more complicated.Each algorithm could be more adaptable to some industry-specific areas, or, in some cases, adapted for maintenance.The statistics should be more self-explanatory with detailed automated reports.Technical support needs some improvement, as they do not respond as quickly as we would like.I think the visualization and charting should be changed and made easier and more effective.

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Pricing and Cost Advice
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.It got us a good amount of money with quick and efficient modeling.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.It is a huge increase to time savings.

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The price of this solution is a little bit high, which was a problem for my company.We think that IBM SPSS is expensive for this function.

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report
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390,245 professionals have used our research since 2012.
Ranking
2nd
out of 16 in Data Mining
Views
9,561
Comparisons
7,362
Reviews
17
Average Words per Review
496
Avg. Rating
8.2
3rd
out of 16 in Data Mining
Views
3,421
Comparisons
2,812
Reviews
8
Average Words per Review
565
Avg. Rating
7.9
Top Comparisons
Compared 18% of the time.
Compared 15% of the time.
Compared 11% of the time.
Also Known As
SPSS ModelerSPSS Statistics
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IBM
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Overview

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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Your organization has more data than ever, but spreadsheets and basic statistical analysis tools limit its usefulness. IBM SPSS Statistics software can help you find new relationships in the data and predict what will likely happen next. Virtually eliminate time-consuming data prep; and quickly create, manipulate and distribute insights for decision making.
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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 TurkeyLDB Group, RightShip, Tennessee Highway Patrol, Capgemini Consulting, TEAC Corporation, Ironside, nViso SA, Razorsight, Si.mobil, University Hospitals of Leicester, CROOZ Inc., GFS Fundraising Solutions, Nedbank Ltd., IDS-TILDA
Top Industries
REVIEWERS
Financial Services Firm24%
Manufacturing Company14%
University14%
Healthcare Company10%
VISITORS READING REVIEWS
Software R&D Company22%
Financial Services Firm12%
Government11%
Comms Service Provider10%
REVIEWERS
University29%
Consumer Goods14%
Non Profit14%
Government14%
Find out what your peers are saying about IBM SPSS Modeler vs. IBM SPSS Statistics and other solutions. Updated: January 2020.
390,245 professionals have used our research since 2012.
We monitor all Data Mining reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.