Compare Dataiku Data Science Studio vs. IBM Watson Studio

Dataiku Data Science Studio is ranked 12th in Data Science Platforms with 4 reviews while IBM Watson Studio is ranked 9th in Data Science Platforms with 6 reviews. Dataiku Data Science Studio is rated 7.6, while IBM Watson Studio is rated 8.4. 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 Watson Studio writes "It has greatly improved the performance because it is standardized across the company". Dataiku Data Science Studio is most compared with Alteryx, KNIME and Databricks, whereas IBM Watson Studio is most compared with IBM SPSS Modeler, Microsoft Azure Machine Learning Studio and Amazon SageMaker. See our Dataiku Data Science Studio vs. IBM Watson Studio report.
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Most Helpful Review
Find out what your peers are saying about Dataiku Data Science Studio vs. IBM Watson Studio and other solutions. Updated: January 2020.
391,616 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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The system's ability to take a look at data, segment it and then use that data very differently.The scalability of IBM Watson Studio is great.The most important thing is that it's a multi-faceted solution. It's a kind of specialist, not a generalist. It can produce very specific information for the customer. It's totally different from Google or any search engine that produces generic information. It's specialty is that it's all on video.The solution is very easy to use.It is a stable, reliable product.Technical support is great. We have had weekly teleconferences with the technical people at IBM, and they have been fantastic.The main benefit is the ease of use. We see a lot of engineers in our site and customers that really like the way the tools are able to work with the people.It has greatly improved the performance because it is standardized across the company.

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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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It's sometimes easy to get lost given the number of images the solution opens up when you click on the mouse and the amount of different tabs.The decision making in their decision making feature is less good than other options.So a better user interface could be very helpfulMore features in data virtualization would be helpful. The solution could use an interactive dashboard that could make exploration easier.We would like to see it more web-based with more functionality.We would like to see it less as one big, massive product, but more based on smaller services that we can then roll out to consumers.Initially, it was quite complex. For us, it was not only a matter of getting it installed, that was just a start. It was also trying to come up with a standard way of implementing it across the entire organization, which had been a challenge.

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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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Ranking
12th
Views
7,866
Comparisons
5,723
Reviews
4
Average Words per Review
493
Avg. Rating
7.5
9th
Views
4,270
Comparisons
3,271
Reviews
5
Average Words per Review
456
Avg. Rating
8.2
Top Comparisons
Compared 14% of the time.
Also Known As
Dataiku DSSWatson Studio, IBM Data Science Experience, Data Science Experience, DSx
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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 Watson Studio provides tools for data scientists, application developers and subject matter experts to collaboratively and easily work with data to build and train models at scale. It gives you the flexibility to build models where your data resides and deploy anywhere in a hybrid environment so you can operationalize data science faster.

Offer
Learn more about Dataiku Data Science Studio
Learn more about IBM Watson Studio
Sample Customers
BGL BNP Paribas, Dentsu Aegis, Link Mobility Group, AramisAutoGroupM, Accenture, Fifth Third Bank
Top Industries
VISITORS READING REVIEWS
Software R&D Company27%
Comms Service Provider14%
Financial Services Firm13%
Media Company7%
VISITORS READING REVIEWS
Software R&D Company39%
Comms Service Provider11%
Retailer9%
Media Company5%
Find out what your peers are saying about Dataiku Data Science Studio vs. IBM Watson Studio and other solutions. Updated: January 2020.
391,616 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.