Dataiku vs IBM SPSS Modeler comparison

Dataiku and IBM are both solutions in the Data Science Platforms category. Dataiku is ranked #6 with an average rating of 8.2, while IBM is ranked #14 with an average rating of 6.8. Dataiku holds a 13.0% mindshare in Data Science Platforms, compared to IBM’s 2.5% mindshare. Additionally, 100% of Dataiku users are willing to recommend the solution, compared to 86% of IBM users who would recommend it.

Dataiku

Read 12 Dataiku reviews

6,417 Views
1,082 Comparison Views

100% willing to recommend

IBM SPSS Modeler

Read 39 IBM SPSS Modeler reviews

1,583 Views
253 Comparison Views

86% willing to recommend

Dataiku

IBM SPSS Modeler

Comparison Buyer's Guide

Download the report

Executive SummaryUpdated on Dec 5, 2024

IBM SPSS Modeler and Dataiku are leading tools in data analysis and modeling. Dataiku is often preferred for its comprehensive features that many users feel justify the higher cost.

Features:IBM SPSS Modeler excels in predictive modeling, statistical analysis, and integration with IBM's ecosystem. It offers robust visual programming capabilities and combines different techniques like regression and neural networks in one node. Dataiku stands out with its collaborative data science platform, extensive machine learning tools, and easy integration with diverse data environments. It provides advanced data preparation and supports code from languages like Python and R.

Room for Improvement:IBM SPSS Modeler's visual modeling is less advanced than other software solutions, and its integration for governance and security needs to be enhanced. It's complex to use for beginners without data science expertise. Dataiku could improve its cost efficiency as the perceived value often comes with a steep price. Its user interface, while comprehensive, can be overwhelming for new users, and some users feel the automation of certain tasks could be further developed to enhance usability.

Ease of Deployment and Customer Service:IBM SPSS Modeler provides a structured deployment process with support from IBM’s network, which some users find rigid. Dataiku offers a flexible deployment model, favoring seamless integration and iterative updates, coupled with a responsive support team that provides personalized attention.

Pricing and ROI:IBM SPSS Modeler involves a higher initial setup cost due to its enterprise-grade features, promising long-term value with an emphasis on advanced analytics. Dataiku presents a scalable pricing structure, appealing to businesses seeking flexible data solutions. Both offer valuable returns, with IBM focusing on comprehensive analytics and Dataiku on flexibility and scalability.

To learn more, read our detailed Dataiku vs. IBM SPSS Modeler Report (Updated: June 2025).

Buyer's Guide

Dataiku vs. IBM SPSS Modeler

June 2025

Download the complete report

Helped 856,873 peers since 2012

Review summaries and opinions

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

Categories and Ranking

Dataiku

Ranking in Data Science Platforms

6th

Average Rating

8.2

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

IBM SPSS Modeler

Ranking in Data Science Platforms

14th

Average Rating

8.0

Reviews Sentiment

6.6

Number of Reviews

Ranking in other categories

Data Mining (4th)

Mindshare comparison

As of June 2025, in the Data Science Platforms category, the mindshare of Dataiku is 13.0%, up from 8.7% compared to the previous year. The mindshare of IBM SPSS Modeler is 2.5%, down from 2.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

RichardXu

Data Science Lead at a mining and metals company with 10,001+ employees

The platform organizes workflows visually and efficiently

One of the valuable features of Dataiku is the workflow capability. It allows us to organize a workflow efficiently. The platform has a visual interface, making it much easier for educated professionals to organize their work. This feature is useful because it simplifies tasks and eliminates the need for a data scientist. If you are knowledgeable about AI, you can directly write using primitive tools like Pantera flow, PyTorch, and Scikit-learn. However, Dataiku makes this process much easier.

Read full review

PeterHuo

Application Architect at Canada Border Services Agency

Good tool for extracting data from data warehouses, creating streams, and manipulating logic to extract final data

There are performance issues. Extracting data from many combined tables can take hours and occasionally crash the server due to memory leaks. This performance problem bothers people. The performance issue seems to be related to the server. We design streams on the client and submit them to the server, which generates a large SQL statement. There are two potential bottlenecks: one in the server and another in data extraction. I'm unsure about the exact mechanics of data splitting when fetching from the database. When streams become larger, performance bottlenecks may occur in the IBM SPSS Modeler server or the database. Sometimes the server crashes and needs to be restarted to release memory on both sides. I'm not sure exactly where the problem is caused, as I focus on stream design rather than server issues. The problem could be on the IBM SPSS Modeler server and database.

Read full review

Quotes from Members

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

Pros

"The advantage is that you can focus on machine learning while having access to what they call 'recipes.' These recipes allow me to preprocess and prepare data without writing any code."

"The most valuable feature of this solution is that it is one tool that can do everything, and you have the ability to very easily push your design to prediction."

"Extremely easy to use with its GUI-based functionality and large compatibility with various data sources. Also, maintenance processes are much more automated than ever, with fewer errors."

"The solution is quite stable."

"I believe the return on investment looks positive."

"The most valuable feature is the set of visual data preparation tools."

"Traceability is vital since I manage many cohorts, and collaboration is key as I have multiple engineers substituting for one another."

"Dataiku is highly regarded as it is a leader in the Gartner ranking."

More Dataiku pros

"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."

"The most valuable features of the IBM SPSS Modeler are visual programming, you don't have to write any code, and it is easy to use. 90 to 95 percent of the use cases, you don't have to fine-tune anything. If you want to do something deeper, for example, create a better neural network, then you have to go into the features and try to fine-tune them. However, the default selection which is made by the tool, it's very practical and works well."

"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."

"We use analytics with the visual modeling capability to leverage productivity improvements."

"In the solution, I like the virtualization of data flow since it shows what goes where, which is mostly the strength of the tool."

"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."

"Compared to other tools, the product works much easier to analyze data without coding."

"We have been able to do some predictive modeling with it"

More IBM SPSS Modeler pros

Cons

"One of the main challenges was collaboration. Developers typically use GitHub to push and manage code, but integrating GitHub with Dataiku was complicated."

"In the next release of this solution, I would like to see deep learning better integrated into the tool and not simply an extension or plugin."

"I find that it is a little slow during use. It takes more time than I would expect for operations to complete."

"One area for improvement is the need for more capabilities similar to those provided by NVIDIA for parallel machine learning training. We still encounter some integration issues."

"The license is very expensive. It would be great to have an intermediate license for basic treatments that do not require extensive experience."

"The interface for the web app can be a bit difficult. It needs to have better capabilities, at least for developers who like to code. This is due to the fact that everything is enabled in a single window with different tabs. For them to actually develop and do the concurrent testing that needs to be done, it takes a bit of time. That is one improvement that I would like to see - from a web app developer perspective."

"Dataiku still needs some coding, and that could be a difference where business data scientists would go for DataRobot more than Dataiku."

"The license is very expensive."

More Dataiku cons

"It's not as user friendly as it could be."

"The time series should be improved."

"It would be good if IBM added help resources to the interface."

"Unstructured data is not appropriate for SPSS Modeler."

"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."

"It is not integrated with Qlik, Tableau, and Power BI."

"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."

"There are performance issues. Extracting data from many combined tables can take hours and occasionally crash the server due to memory leaks. This performance problem bothers people. The performance issue seems to be related to the server. We design streams on the client and submit them to the server, which generates a large SQL statement. There are two potential bottlenecks: one in the server and another in data extraction. I'm unsure about the exact mechanics of data splitting when fetching from the database."

More IBM SPSS Modeler cons

Pricing and Cost Advice

"The annual licensing fees are approximately €20 ($22 USD) per key for the basic version and €40 ($44 USD) per key for the version with everything."

"Pricing is pretty steep. Dataiku is also not that cheap."

"It is an expensive product."

"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."

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

"$5,000 annually."

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

"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."

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

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

More IBM SPSS Modeler pricing and cost advice

See which vendors are best for you

Use our free recommendation engine to learn which Data Science Platforms solutions are best for your needs.

See recommendations

856,873 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

18%

Computer Software Company

Manufacturing Company

Educational Organization

Financial Services Firm

12%

Educational Organization

11%

University

Computer Software Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What is your experience regarding pricing and costs for Dataiku Data Science Studio?

I find the pricing of Dataiku quite affordable for our customers, as they are usually large companies. However, it is a pricey solution and I primarily recommend it to bigger companies.

See all answers

What needs improvement with Dataiku Data Science Studio?

There is room for improvement in terms of allowing for more code-based features. I would love for Dataiku to allow more flexibility with code-based components and provide the possibility to extend ...

See all answers

What is your primary use case for Dataiku Data Science Studio?

My company sells licenses for both Dataiku and Alteryx, and we have clients who use them. I engage with several companies in telecommunications, retail, and energy to assess how our clients are uti...

See all answers

What do you like most about IBM SPSS Modeler?

Compared to other tools, the product works much easier to analyze data without coding.

See all answers

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 budget. They used to encourage people to use the modeler for development. If ten us...

See all answers

What needs improvement with IBM SPSS Modeler?

See all answers

Comparisons

Databricks vs Dataiku

Compared 30% of the time

KNIME vs Dataiku

Compared 19% of the time

Alteryx vs Dataiku

Compared 12% of the time

Altair RapidMiner vs Dataiku

Compared 11% of the time

Starburst Galaxy vs Dataiku

Compared 1% of the time

More Dataiku Competitors

Microsoft Power BI vs IBM SPSS Modeler

Compared 26% of the time

IBM SPSS Statistics vs IBM SPSS Modeler

Compared 21% of the time

KNIME vs IBM SPSS Modeler

Compared 16% of the time

Altair RapidMiner vs IBM SPSS Modeler

Compared 8% of the time

SAS Visual Analytics vs IBM SPSS Modeler

Compared 2% of the time

More IBM SPSS Modeler Competitors

Product Reports

Buyer's Guide

Dataiku

June 2025

Download Dataiku product report

Buyer's Guide

IBM SPSS Modeler

June 2025

Download IBM SPSS Modeler product report

Also Known As

Dataiku DSS

SPSS Modeler

Overview

Dataiku Data Science Studio is acclaimed for its versatile capabilities in advanced analytics, data preparation, machine learning, and visualization. It streamlines complex data tasks with an intuitive visual interface, supports multiple languages like Python, R, SQL, and scales efficiently for large dataset handling, boosting organizational efficiency and collaboration.

Dataiku

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.

Buy
https://www.ibm.com/products/spss-modeler/pricing

Sign up for the trial
https://www.ibm.com/account/reg/us-en/signup?formid=urx-19947

IBM

Sample Customers

BGL BNP Paribas, Dentsu Aegis, Link Mobility Group, AramisAuto

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

Buyer's Guide

Dataiku vs. IBM SPSS Modeler

June 2025

Free Report: Dataiku vs. IBM SPSS Modeler

Find out what your peers are saying about Dataiku vs. IBM SPSS Modeler and other solutions. Updated: June 2025.

DOWNLOAD NOW

856,873 professionals have used our research since 2012.

See our Dataiku vs. IBM SPSS Modeler report.

See our list of best Data Science Platforms vendors.

We monitor all Data Science Platforms 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.