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Dataiku vs Domino Data Science Platform comparison

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Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

IBM SPSS Statistics
Sponsored
Ranking in Data Science Platforms
10th
Average Rating
8.0
Number of Reviews
37
Ranking in other categories
Data Mining (3rd)
Dataiku
Ranking in Data Science Platforms
7th
Average Rating
8.0
Number of Reviews
8
Ranking in other categories
No ranking in other categories
Domino Data Science Platform
Ranking in Data Science Platforms
20th
Average Rating
7.6
Number of Reviews
2
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of November 2024, in the Data Science Platforms category, the mindshare of IBM SPSS Statistics is 2.8%, up from 2.6% compared to the previous year. The mindshare of Dataiku is 11.5%, up from 7.5% compared to the previous year. The mindshare of Domino Data Science Platform is 2.7%, up from 2.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms
 

Featured Reviews

AbakarAhmat - PeerSpot reviewer
Enhancing survey analysis that provides valued insightfulness
I used traditional tools where I would prepare data, click through menus, and use SQL Server for data visualization. We switched to IBM SPSS because it offers strong certification and aligns well with the standards we prioritize in our surveys. In terms of popularity, it stands out as the top choice in the market, especially in the research and university domains. Many different organizations and institutions use SPSS for statistical analytics. While there are other tools like MCLab and similar options available, SPSS is the most renowned and widely used among them.
Sabrine Bendimerad - PeerSpot reviewer
Saves a lot of time because I can quickly handle all the data preparation tasks and concentrate on building my machine learning algorithms
One of the main challenges was collaboration. Developers typically use GitHub to push and manage code, but integrating GitHub with Dataiku was complicated. While it was theoretically possible to use GitHub with Dataiku, in practice, it was difficult to manage our code effectively and push it from Dataiku to GitHub. Another limitation was its ability to handle different types of data. While Dataiku is powerful for working with structured data, like regular or geospatial data, it struggled with more complex data types such as text and image. In addition to the challenges with GitHub integration, the limited support for diverse data types was another feature lacking at that time.
AS
Accelerated machine learning model development with seamless deployment
We used Domino Data Science Platform for developing and working with machine learning models. It facilitated end-to-end development processes. Domino is based on Git, enabling collaboration similar to using Git. Each user operates on their own equivalent of a branch or fork, and once finished, they…

Quotes from Members

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

Pros

"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."
"SPSS is quite robust and quicker in terms of providing you the output."
"The learning curve to using this product is not steep. The program is appropriate for those who do not have a lot of background in programming, yet have to perform basic statistical analysis."
"IBM SPSS Statistics depends on AI."
"It has helped our analyst unit deliver work with more transparency and confidence, given that we can always view the dataset in totality, after each step of data transformation."
"I've found the descriptive statistics and cross-tabs valuable. The very simple correlations and regressions are as well."
"It is perfectly adequate if all you need are the results and not the trail of evidence."
"You can quickly build models because it does the work for you."
"I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person."
"If many teams are collaborating and sharing Jupyter notebooks, it's very useful."
"Cloud-based process run helps in not keeping the systems on while processes are running."
"The solution is quite stable."
"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."
"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."
"Data Science Studio's data science model is very useful."
"The most valuable feature is the set of visual data preparation tools."
"The workspaces, which are like wrappers of Docker containers, made it easy to start development environments using Domino."
"The scalability of the solution is good; I'd rate it four out of five."
 

Cons

"Perhaps in terms of visualization. It's not really easy to do some data visualization, just simple, descriptive analysis in SPSS. I think that could be an area for improvement."
"IBM SPSS Statistics does not keep you close to your data like KNIME."
"It would be helpful if there was better documentation on how to properly use the solution. A beginner's guide on how to use the various programming functions within the product would be so useful to a lot of people. I found that everything was very confusing at first. Having clear documentation would help alleviate that."
"I know that SPSS is a statistical tool but it should also include a little bit of analytical behavior. You can call it augmented analysis or predictive analysis. The bottom line is it should have more graphical and analytical capabilities."
"It could provide even more in the way of automation as there are many opportunities."
"Needs more statistical modelling functions."
"Improvements are needed in the user interface, particularly in terms of user-friendliness."
"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."
"The ability to have charts right from the explorer would be an improvement."
"Dataiku still needs some coding, and that could be a difference where business data scientists would go for DataRobot more than Dataiku."
"I think it would help if Data Science Studio added some more features and improved the data model."
"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."
"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."
"One of the main challenges was collaboration. Developers typically use GitHub to push and manage code, but integrating GitHub with Dataiku was complicated."
"I find that it is a little slow during use. It takes more time than I would expect for operations to complete."
"Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable."
"The deployment of large language models (LLMs) could be improved."
"The predictive analysis feature needs improvement."
 

Pricing and Cost Advice

"Our licence is on a yearly renewal basis. While pricing is not the primary concern in our evaluation, as products are assessed by whether they can meet our user needs and expertise, the cost can be a limiting factor in the number of licences we procure."
"The price of IBM SPSS Statistics could improve."
"While the pricing of the product may be higher, the accompanying service and features justify the investment."
"More affordable training for new staff members."
"If it requires lot of data processing, maybe switching to IBM SPSS Clementine would be better for the buyer."
"It's quite expensive, but they do a special deal for universities."
"SPSS is an expensive piece of software because it's incredibly complex and has been refined over decades, but I would say it's fairly priced."
"The price of this solution is a little bit high, which was a problem for my company."
"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."
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Top Industries

By visitors reading reviews
Financial Services Firm
17%
University
9%
Computer Software Company
9%
Manufacturing Company
8%
Financial Services Firm
18%
Educational Organization
16%
Manufacturing Company
9%
Computer Software Company
8%
Financial Services Firm
32%
Manufacturing Company
12%
Computer Software Company
9%
Insurance Company
9%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

Questions from the Community

What do you like most about IBM SPSS Statistics?
The software offers consistency across multiple research projects helping us with predictive analytics capabilities.
What is your experience regarding pricing and costs for IBM SPSS Statistics?
The cost of IBM SPSS Statistics is managed by organizations, not individual researchers. It is a very expensive produ...
What needs improvement with IBM SPSS Statistics?
IBM SPSS Statistics does not keep you close to your data like KNIME. In KNIME, at every stage, you can see the result...
What needs improvement with Dataiku Data Science Studio?
One of the main challenges was collaboration. Developers typically use GitHub to push and manage code, but integratin...
What is your primary use case for Dataiku Data Science Studio?
We use the solution for data science and machine learning.
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Also Known As

SPSS Statistics
Dataiku DSS
Domino Data Lab Platform
 

Learn More

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Interactive Demo

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Overview

 

Sample Customers

LDB 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
BGL BNP Paribas, Dentsu Aegis, Link Mobility Group, AramisAuto
Allstate, GSK, AstraZeneca, Federal Reserve, US Navy, Bristol Myers Squibb, Bayer, BNP Paribas, Moodys, New York Life
Find out what your peers are saying about Dataiku vs. Domino Data Science Platform and other solutions. Updated: November 2024.
816,406 professionals have used our research since 2012.