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H2O.ai vs KNIME 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)
H2O.ai
Ranking in Data Science Platforms
22nd
Average Rating
7.6
Number of Reviews
8
Ranking in other categories
Model Monitoring (8th)
KNIME
Ranking in Data Science Platforms
2nd
Average Rating
8.2
Reviews Sentiment
7.2
Number of Reviews
58
Ranking in other categories
Data Mining (1st)
 

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 H2O.ai is 1.5%, up from 1.5% compared to the previous year. The mindshare of KNIME is 11.0%, up from 8.6% 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.
Kashif Yaseen - PeerSpot reviewer
Plug-and-play convenience enhances productivity but needs better multimodal support
We mostly used the solution in the domain that I'm working. We had most of the use cases around chatbots and conversational BI The solution was plug-and-play, meaning most of the components were handled by the solution itself rather than building them from scratch. This was useful for our banking…
Hansong Choi - PeerSpot reviewer
A low-code platform that reduces data mining time by linking script
The solution allows for sharing model designs and model operations with other data analysts. Other solutions such as SAS, R, and Python consist of just the script which is difficult to share. The solution is a low-code platform which reduces data mining time and its platform includes a clickable icon for linking script.

Quotes from Members

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

Pros

"It offers very good visualization."
"SPSS can handle whatever you throw at it, whether your data set contains 10,000, 100,000, or a million objects. It's like the heavy artillery of analytical tools."
"It has the ability to easily change any variable in our research."
"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."
"Capability analysis is one of the main and valuable functions. We also do some hypothesis testing in Minitab and summary stats. These are the functions that we find very useful."
"The most valuable feature is its robust statistical analysis capabilities."
"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."
"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."
"Fast training, memory-efficient DataFrame manipulation, well-documented, easy-to-use algorithms, ability to integrate with enterprise Java apps (through POJO/MOJO) are the main reasons why we switched from Spark to H2O."
"The ease of use in connecting to our cluster machines."
"The most valuable feature of H2O.ai is that it is plug-and-play."
"The most valuable features are the machine learning tools, the support for Jupyter Notebooks, and the collaboration that allows you to share it across people."
"It is helpful, intuitive, and easy to use. The learning curve is not too steep."
"One of the most interesting features of the product is their driverless component. The driverless component allows you to test several different algorithms along with navigating you through choosing the best algorithm."
"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."
"This solution is easy to use and especially good at data preparation and wrapping."
"The product is open-source and therefore free to use."
"The solution is very easy to use"
"All of the features related to the ETL are fantastic. That includes the connectors to other programs, databases, and the meta node function."
"Usability, and organising workflows in very neat manner. Controlling workflow through variables is something amazing."
"What I like the most is that it works almost out of the box with Random Forest and other Forest nodes."
"This solution is easy to use and it can be used to create any kind of model."
"Since KNIME is a no-code platform, it is easy to work with."
 

Cons

"I would like SPSS to improve its integration with other data-filing IBM tools. I also think its duration with data, utilization, and graphics could be better."
"It could allow adding color to data models to make them easier to interpret."
"The solution could improve by providing a visual network for predictions and a self-organizing map for clustering."
"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."
"Better documentation on how to use macros."
"Improvements are needed in the user interface, particularly in terms of user-friendliness."
"The technical support should be improved."
"IBM SPSS Statistics does not keep you close to your data like KNIME."
"It needs a drag and drop GUI like KNIME, for easy access to and visibility of workflows."
"Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive."
"I would like to see more features related to deployment."
"It lacks the data manipulation capabilities of R and Pandas DataFrames. We would kill for dplyr offloading H2O."
"The interpretability module has room for improvement. Also, it needs to improve its ability to integrate with other systems, like SageMaker, and the overall integration capability."
"H2O.ai can improve in areas like multimodal support and prompt engineering."
"On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time."
"The model management features could be improved."
"The ability to handle large amounts of data and performance in processing need to be improved."
"KNIME doesn't handle large datasets or a high number of records well."
"They should look at other vendors like Alteryx that are more user friendly and modern."
"Not just for KNIME, but generally for software and analyzing data, I would welcome facilities for analyzing different sorts of scale data like Likert scales, Thurstone scales, magnitude ratio scales, and Guttman scales, which I don't use myself."
"I wish there were more video training resources for KNIME. The current videos are very short, and most learning is text-based. Longer training sessions would be helpful, especially for complex flowchart use cases. Webinars focusing on starting projects and analyzing data would also be beneficial."
"The overall user experience feels unpolished. In particular: Data field type conversion is a real hassle, and date fields are a hassle; documentation is pretty poor; user community is average at best."
"Data visualization needs improvement."
"There are a lot of tools in the product and it would help if they were grouped into classes where you can select a function, rather than a specific tool."
 

Pricing and Cost Advice

"More affordable training for new staff members."
"The pricing of the modeler is high and can reduce the utility of the product for those who can not afford to adopt it."
"If it requires lot of data processing, maybe switching to IBM SPSS Clementine would be better for the buyer."
"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."
"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."
"I rate the tool's pricing a five out of ten."
"The price of this solution is a little bit high, which was a problem for my company."
"We have seen significant ROI where we were able to use the product in certain key projects and could automate a lot of processes. We were even able to reduce staff."
"I use the tool's free version."
"It is expensive to procure the license."
"We're using the free academic license just locally. I went for KNIME because they have a free academic license."
"While there are certain limitations in functionality, you can still utilize it efficiently free of charge."
"For beginners, the free desktop version is very attractive, but the full server version can be more expensive. I have only used the free version and it offers a fair pricing system. I have been promoting it to others without any compensation or request from the company, simply because I am enthusiastic about it. I am not aware of the pricing for the server version, but it seems to be widely used."
"They have different versions, but I am using the open-source one."
"There is no cost for using KNIME because it is an open-source solution, but you have to pay if you need a server."
"KNIME is an open-source tool, so it's free to use."
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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
20%
Computer Software Company
11%
Manufacturing Company
10%
Insurance Company
7%
Manufacturing Company
13%
Financial Services Firm
13%
Computer Software Company
9%
Educational Organization
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

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...
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What do you like most about KNIME?
Since KNIME is a no-code platform, it is easy to work with.
What is your experience regarding pricing and costs for KNIME?
I rate the product’s pricing a seven out of ten, where one is cheap and ten is expensive.
What needs improvement with KNIME?
The current UI is primarily in English. Analyzing data in local languages might present challenges or issues.
 

Comparisons

 

Also Known As

SPSS Statistics
No data available
KNIME Analytics Platform
 

Learn More

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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
poder.io, Stanley Black & Decker, G5, PWC, Comcast, Cisco
Infocom Corporation, Dymatrix Consulting Group, Soluzione Informatiche, MMI Agency, Estanislao Training and Solutions, Vialis AG
Find out what your peers are saying about H2O.ai vs. KNIME and other solutions. Updated: October 2024.
816,406 professionals have used our research since 2012.