Compare Dataiku Data Science Studio vs. IBM SPSS Modeler

Dataiku Data Science Studio is ranked 13th in Data Science Platforms with 4 reviews while IBM SPSS Modeler is ranked 3rd in Data Science Platforms with 17 reviews. Dataiku Data Science Studio is rated 7.6, while IBM SPSS Modeler is rated 8.2. The top reviewer of Dataiku Data Science Studio writes "User interface is colorful, beautiful, and well-designed but sometimes the solution can be slow". On the other hand, 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". Dataiku Data Science Studio is most compared with Alteryx, KNIME and Databricks, whereas IBM SPSS Modeler is most compared with KNIME, Alteryx and IBM Watson Studio. See our Dataiku Data Science Studio vs. IBM SPSS Modeler report.
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Most Helpful Review
Find out what your peers are saying about Dataiku Data Science Studio vs. IBM SPSS Modeler and other solutions. Updated: January 2020.
397,408 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
I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person.The most valuable feature is the set of visual data preparation tools.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.Cloud-based process run helps in not keeping the systems on while processes are running.

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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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Cons
I find that it is a little slow during use. It takes more time than I would expect for operations to complete.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.The ability to have charts right from the explorer would be an improvement.Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable.Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days).There were stability issues: 1) SQL operations, such as partitioning, had bugs and showed wrong results. 2) Due to server downtime, scheduled processes used to fail. 3) Access to project folders was compromised (privacy issue) with wrong people getting access to confidential project folders.

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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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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.

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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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Ranking
13th
Views
8,249
Comparisons
5,990
Reviews
4
Average Words per Review
493
Avg. Rating
7.5
3rd
Views
9,691
Comparisons
7,421
Reviews
17
Average Words per Review
496
Avg. Rating
8.2
Top Comparisons
Compared 13% of the time.
Compared 18% of the time.
Compared 15% of the time.
Also Known As
Dataiku DSSSPSS Modeler
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Dataiku
IBM
Overview

Dataiku DSS is the collaborative data science software platform for teams of data scientists, data analysts, and engineers to explore, prototype, build, and deliver their own data products more efficiently.

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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Sample Customers
BGL BNP Paribas, Dentsu Aegis, Link Mobility Group, AramisAutoReisebªro 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
Top Industries
VISITORS READING REVIEWS
Software R&D Company27%
Financial Services Firm16%
Comms Service Provider14%
Media Company8%
REVIEWERS
Financial Services Firm24%
University14%
Manufacturing Company14%
Government10%
VISITORS READING REVIEWS
Software R&D Company21%
Comms Service Provider13%
Financial Services Firm10%
Government10%
Find out what your peers are saying about Dataiku Data Science Studio vs. IBM SPSS Modeler and other solutions. Updated: January 2020.
397,408 professionals have used our research since 2012.
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.