Dataiku vs Domino Data Science Platform comparison

Dataiku and Domino Data Lab are both solutions in the Data Science Platforms category. Dataiku is ranked #7 with an average rating of 7.5, while Domino Data Lab is ranked #15 with an average rating of 8.0. Dataiku holds a 11.8% mindshare in Data Science Platforms, compared to Domino Data Lab’s 2.6% mindshare. Additionally, 100% of Dataiku users are willing to recommend the solution, compared to 100% of Domino Data Lab users who would recommend it.

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Executive SummaryUpdated on Dec 5, 2024

Dataiku and Domino Data Science Platform are competing products in the data science and analytics space. Dataiku seems to have the upper hand in ease of use and support, while Domino excels in feature offerings and flexibility.

Features: Dataiku is recognized for its collaborative environment, data preparation tools, and user-friendly machine learning capabilities. Domino Data Science Platform features an open ecosystem that allows for flexibility and integration with various tools and platforms, offering deeper customization that appeals to teams looking for more control over data science operations.

Ease of Deployment and Customer Service: Dataiku offers a smooth deployment process supported by robust customer assistance. In contrast, Domino Data Science Platform provides a flexible deployment model that can be tailored to fit specific business requirements but may require more technical involvement. Dataiku's responsive customer service complements its ease of deployment, whereas Domino offers flexibility at the expense of increased complexity.

Pricing and ROI: Dataiku provides competitive setup costs and delivers substantial ROI through its simplicity and speed of deployment. Domino Data Science Platform may be more costly initially, but it promises a high ROI through its extensive feature set and flexibility that supports diverse data science projects, often making the decision dependent on budget constraints versus the desired level of customization and complexity.

To learn more, read our detailed Dataiku vs. Domino Data Science Platform Report (Updated: November 2024).

Buyer's Guide

Dataiku vs. Domino Data Science Platform

November 2024

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Categories and Ranking

IBM SPSS Statistics

Mindshare comparison

As of December 2024, in the Data Science Platforms category, the mindshare of IBM SPSS Statistics is 2.7%, up from 2.7% compared to the previous year. The mindshare of Dataiku is 11.8%, up from 7.6% compared to the previous year. The mindshare of Domino Data Science Platform is 2.6%, down from 2.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

Md Masudul Hassan

Agricultural Finance and IT Researcher at Sher-e-Bangla Agricultural University

Comprehensive data analysis capabilities with a user-friendly interface, providing an efficient and reliable platform for researchers and analysts

I believe that offering short-term SPSS licenses, perhaps when customer sourcing is available, could make it more affordable. These licenses shouldn't include features tailored for universities or large sales organizations. Instead, they could offer discounts or additional facilities for smaller entities to access the software. In developing countries, it would be beneficial to provide certain features to users at no cost initially, while also customizing pricing options. For example, offering basic features to the first hundred users can help them become familiar with the software and its capabilities. This approach encourages users to upgrade to higher tiers as they become more experienced and require additional functionality.

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Sabrine Bendimerad

Data Scientist at Electricite De France

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.

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AHMEDSAHRAOUI

Machine Learning Engineer at Unemployed

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…

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Quotes from Members

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

Pros

"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."

"I've found the descriptive statistics and cross-tabs valuable. The very simple correlations and regressions are as well."

"The most valuable feature of IBM SPSS Statistics is all the functionality it provides. Additionally, it is simple to do the five-way analysis that you can into multidimensional setup space. It's the multidimensional space facility that is most useful."

"The software offers consistency across multiple research projects helping us with predictive analytics capabilities."

"Since we are using the software as a statistical tool, I would say the best aspects of it are the regression and segmentation capabilities. That said, I've used it for all sorts of things."

"The most valuable features are the solution is easy to use, training new users is not difficult, and our usage is comprehensive because the whole service is beneficial."

"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."

"The SPSS interface is very accessible and user-friendly. It's really easy to get information in it. I've shared it with experts and beginners, and everyone can navigate it."

More IBM SPSS Statistics pros

"Cloud-based process run helps in not keeping the systems on while processes are running."

"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."

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

"Data Science Studio's data science model is very useful."

"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."

"If many teams are collaborating and sharing Jupyter notebooks, it's very useful."

"I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person."

More Dataiku pros

"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

"I'd like to see them use more artificial intelligence. It should be smart enough to do predictions and everything based on what you input."

"In some cases, the product takes time to load a large dataset. They could improve this particular area."

"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."

"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."

"SPSS slows down the computer or the laptop if the data is huge; then you need a faster computer."

"The statistics should be more self-explanatory with detailed automated reports."

"The product should provide more ways to import data and export results that are user-friendly for high-level executives."

"The technical support should be improved."

More IBM SPSS Statistics cons

"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."

"I think it would help if Data Science Studio added some more features and improved the data model."

"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."

"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."

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

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

"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."

More Dataiku cons

"The deployment of large language models (LLMs) could be improved."

"The predictive analysis feature needs improvement."

Pricing and Cost Advice

"If it requires lot of data processing, maybe switching to IBM SPSS Clementine would be better for the buyer."

"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."

"It's quite expensive, but they do a special deal for universities."

"We think that IBM SPSS is expensive for this function."

"The price of this solution is a little bit high, which was a problem for my company."

"While the pricing of the product may be higher, the accompanying service and features justify the investment."

"The price of IBM SPSS Statistics could improve."

"The pricing of the modeler is high and can reduce the utility of the product for those who can not afford to adopt it."

More IBM SPSS Statistics 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."

Information not available

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Top Industries

By visitors reading reviews

Financial Services Firm

17%

Computer Software Company

University

Manufacturing Company

Financial Services Firm

18%

Educational Organization

16%

Manufacturing Company

Computer Software Company

Financial Services Firm

32%

Manufacturing Company

11%

Computer Software Company

Insurance Company

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.

See all answers

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...

See all answers

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...

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

It is very expensive.

See all answers

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...

See all answers

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

We use the solution for data science and machine learning.

See all answers

What needs improvement with Domino Data Science Platform?

The deployment of large language models (LLMs) could be improved. Currently, Domino provides a simple server that can...

See all answers

What is your primary use case for Domino Data Science Platform?

We used Domino Data Science Platform for developing and working with machine learning models. It facilitated end-to-e...

See all answers

What advice do you have for others considering Domino Data Science Platform?

It's important to have a DevOps team well-versed with cloud-native solutions to manage Domino effectively. Relying so...

See all answers

Comparisons

IBM SPSS Modeler vs IBM SPSS Statistics

Compared 16% of the time

Alteryx vs IBM SPSS Statistics

Compared 13% of the time

TIBCO Statistica vs IBM SPSS Statistics

Compared 13% of the time

Microsoft Azure Machine Learning Studio vs IBM SPSS Statistics

Compared 11% of the time

KNIME vs IBM SPSS Statistics

Compared 2% of the time

More IBM SPSS Statistics Competitors

Databricks vs Dataiku

Compared 37% of the time

KNIME vs Dataiku

Compared 15% of the time

Alteryx vs Dataiku

Compared 11% of the time

RapidMiner vs Dataiku

Compared 11% of the time

IBM SPSS Modeler vs Dataiku

Compared 1% of the time

More Dataiku Competitors

Amazon SageMaker vs Domino Data Science Platform

Compared 36% of the time

Databricks vs Domino Data Science Platform

Compared 28% of the time

Microsoft Azure Machine Learning Studio vs Domino Data Science Platform

Compared 7% of the time

Alteryx vs Domino Data Science Platform

Compared 6% of the time

KNIME vs Domino Data Science Platform

Compared 5% of the time

More Domino Data Science Platform Competitors

Product Reports

Buyer's Guide

IBM SPSS Statistics

December 2024

Download IBM SPSS Statistics product report

Buyer's Guide

Dataiku

December 2024

Download Dataiku product report

Buyer's Guide

Data Science Platforms

December 2024

Download Domino Data Science Platform product report

Also Known As

SPSS Statistics

Dataiku DSS

Domino Data Lab Platform

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Dataiku

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Domino Data Lab

Interactive Demo

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IBM

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Dataiku

Domino Data Lab

Overview

IBM SPSS Statistics is a powerful data mining solution that is designed to aid business leaders in making important business decisions. It is designed so that it can be effectively utilized by organizations across a wide range of fields. SPSS Statistics allows users to leverage machine learning algorithms so that they can mine and analyze data in the most effective way possible.

IBM SPSS Statistics Benefits

Some of the ways that organizations can benefit by choosing to deploy IBM SPSS Statistics include:

Ease of use. SPSS Statistics enables users to simply and intuitively take control of their statistical needs. The solution is designed so that analysts who do not know how to code can easily make full use of the various tools and capabilities that SPSS Statistics has to offer. Its command language is so straightforward that it does not require users to undergo special training before they use it.

Comprehensive and flexible build. SPSS Statistics is designed to be both a comprehensive and highly flexible analytics solution. It enables users to utilize a variety of integrations that make it easy for users to add features that they might feel they are missing.

Automation. SPSS Statistics makes it simple for users to automate basic tasks that they might otherwise devote too much time worrying about. Tasks like calculation or data gathering can be delegated to the system while more conceptual tasks like data analysis are given to an organization’s analysts to handle.

IBM SPSS Statistics Features

Intuitive user interface. SPSS Statistics enables users to deploy an intuitive interface that makes the process of system management simple. Among the other components of this interface is a drag-and-drop feature that makes analysis and management possible for anyone who wants to use it.

Advanced data visualizations. Analysts that employ SPSS Statistics gain access to tools that empower them to create and export data visualizations. These visualizations can be formatted in many different ways depending on what the user needs.

Local data storage. SPSS Statistics has the ability to securely store data on a user’s computer. This enables them to add layers of security that would not necessarily be present if the data was stored in the cloud.

Reviews from Real Users

IBM SPSS Statistics is a highly effective solution that stands out when compared to many of its competitors. Two major advantages it offers are the wealth of functionalities that it provides and its high level of accessibility.

An Emeritus Professor of Health Services Research at a university writes, "The most valuable feature of IBM SPSS Statistics is all the functionality it provides. Additionally, it is simple to do the five-way analysis that you can in a multidimensional setup space. It's the multidimensional space facility that is most useful."

A Director of Systems Management & MIS Operations at a university, says, “The SPSS interface is very accessible and user-friendly. It's really easy to get information from it. I've shared it with experts and beginners, and everyone can navigate it.”

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.

Domino provides a central system of record that keeps track of all data science activity across an organization. Domino helps data scientists seamlessly orchestrate AWS hardware and software toolkits, increase flexibility and innovation, and maintain required IT controls and standards. Organizations can automatically keep track of all data, tools, experiments, results, discussion, and models, as well as dramatically scale data science investments and impact decision-making across divisions. The platform helps organizations work faster, deploy results sooner, scale rapidly, and reduce regulatory and operational risk.

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

Buyer's Guide

Dataiku vs. Domino Data Science Platform

November 2024

Free Report: Dataiku vs. Domino Data Science Platform

Find out what your peers are saying about Dataiku vs. Domino Data Science Platform and other solutions. Updated: November 2024.

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