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IBM Smart Analytics vs KNIME Business Hub comparison

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Comparison Buyer's Guide

Executive SummaryUpdated on Feb 8, 2026

Review summaries and opinions

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

Categories and Ranking

IBM Smart Analytics
Ranking in Data Mining
8th
Average Rating
7.0
Number of Reviews
1
Ranking in other categories
No ranking in other categories
KNIME Business Hub
Ranking in Data Mining
1st
Average Rating
8.2
Reviews Sentiment
6.7
Number of Reviews
66
Ranking in other categories
Data Science Platforms (3rd)
 

Mindshare comparison

As of September 2026, in the Data Mining category, the mindshare of IBM Smart Analytics is 4.8%, up from 1.7% compared to the previous year. The mindshare of KNIME Business Hub is 10.4%, down from 22.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Mining Mindshare Distribution
ProductMindshare (%)
KNIME Business Hub10.4%
IBM Smart Analytics4.8%
Other84.8%
Data Mining
 

Featured Reviews

RH
Program Manager - Enterprise Command Center at a financial services firm with 10,001+ employees
Adding LA on top of a well deployed & working Tivoli Framework opens up a flood of native logged data points. The visual presentation layer of LA is less than cutting edge.
The IBM monitoring software products (Tivoli) are not easy to instrument and require many separate pieces of the total framework to be operationally functional and useable. That said, adding LA on top of a well deployed & working Tivoli Framework opens up a flood of native logged data points for unstructured search & query. My team had a special need to implement custom alerting on 10s of thousands of MQ channels in a short amount of time, and the traditional approach (also w a Tivoli product) would have been very costly (labor) and time consuming (requiring individual app review). As an alternative, we had a new event stream create to track all MQ channels to generate logs and then used LA to visualize the behavior trends for review, reporting and eventually alerting. The effort took longer than I hoped ~6 months, but the traditional approach would have taken 2+ yrs to review and implement app by app.
NataliaRaffo - PeerSpot reviewer
Co Founder & Chief Data Officer Cdo at NTT DATA
Workflow automation has accelerated advanced analytics and machine learning delivery
Sometimes it is a little bit difficult to use some nodes when we have many large-scale data, for example, CSV files with a large amount of data. It is sometimes difficult to try to import the data in KNIME Business Hub nodes because I think that some features that are in the CSV in text, for example, large text, is difficult for KNIME Business Hub to import these fields. I don't know why, but it is very difficult. We need to try to use different nodes for importing the data, such as File Reader and CSV Reader. However, I think that it is always the features that have much text, it is difficult for KNIME Business Hub to understand and import this information. I don't know why, or maybe I don't know if we don't know what the better option is to configure the node to import all the CSV or the data set. However, we have always had this problem. In some nodes, sometimes it is the same because sometimes, for example, I have a CSV and in my CSV, I have a feature that is, for example, a date. When I import this data set in the File Reader node, I have problems with this field because it is a date, but the problem is that it imports it as text, for example. We try to use their nodes that convert text to date, but sometimes it is difficult, and it is not immediate to transform the text into a date. So we needed to convert the text into a date in the CSV, and then import it again in the KNIME Business Hub node and try to have a good read of this field. I know that KNIME Business Hub has some nodes to convert text to date and others, but sometimes it is difficult to use these nodes. I don't know why. Maybe it needs a specific format for the date and we need to transform our feature in this option. So sometimes it is a large process to convert these features. However, sometimes we need to investigate and search for other nodes, and try with other nodes to import these cases.

Quotes from Members

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

Pros

"Log Analytics (LA) allows a user to see patterns of behavior and isolate issues quickly, without the need to manually access individual systems and parse logs manually."
"It is very fast to develop solutions."
"It's a coding-less opportunity to use AI. This is the major value for me."
"I am impressed by the modularity and reusability in KNIME, especially the ability to make small adjustments to object configurations."
"The tool's analytic capabilities are good."
"For organizations with a small team of data analysts or data scientists, it is a very easy tool to become familiar with predictive modeling, and makes it possible to hand over projects to colleagues without the need to extensively document them."
"Overall KNIME serves its purpose and does a good job."
"With KNIME, I am able to get that process down to under one minute, with data broken down into individual tabs."
"All of the features related to the ETL are fantastic. That includes the connectors to other programs, databases, and the meta node function."
 

Cons

"The indexing engine (proprietary build of LogStash) is well... very LogStash'ish... It requires more work to normalize the log feeds than competing products."
"It's difficult to provide input on the improvement area because it's more of self-learning. However, there are times when I am not able to do certain things. I don't know if it's because the solution doesn't allow me or if it's because of the lack of knowledge."
"KNIME doesn't handle large datasets or a high number of records well."
"KNIME's documentation is not strong."
"For graphics, the interface is a little confusing. So, this is a point that could be improved."
"For now, the license is quite expensive for us and it would be helpful if that was reduced."
"The user interface could be a little bit more comfortable."
"There are some parameters that I would like to have at a bigger scale. The upper limit of one node that tries to find spots or areas in photos was too small for us. It would need to be bigger."
"Scalability is limited to a desktop application."
 

Pricing and Cost Advice

Information not available
"This is an open-source solution that is free to use."
"KNIME offers a free version"
"At this time, I am using the free version of Knime."
"KNIME is free and open source."
"KNIME is an open-source tool, so it's free to use."
"There is a Community Edition and paid versions available."
"Scaling to the on-premises version requires a licensing fee per user that is a bit expensive in comparison to R, Python, and SAS."
"KNIME desktop is free, which is great for analytics teams. Server is well priced, depending on how much support is required."
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Top Industries

By visitors reading reviews
No data available
Financial Services Firm
11%
University
8%
Manufacturing Company
8%
Outsourcing Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
By reviewers
Company SizeCount
Small Business22
Midsize Enterprise16
Large Enterprise35
 

Questions from the Community

Ask a question
Earn 20 points
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?
KNIME Business Hub could be improved by making the user experience more intuitive, especially for users who are new to data analytics. More guided features and documentation would be beneficial.
What is your primary use case for KNIME?
I automated the processing of paper-based data by digitalizing scanned documents and creating a KNIME Business Hub workflow that transforms the data into an analysis-ready format, reducing a manual...
 

Also Known As

Smart Analytics
KNIME Analytics Platform
 

Overview

 

Sample Customers

WIdO AOK, EEKA Fashion, SSGC, GS Retail
Infocom Corporation, Dymatrix Consulting Group, Soluzione Informatiche, MMI Agency, Estanislao Training and Solutions, Vialis AG
Find out what your peers are saying about Knime, IBM, Weka and others in Data Mining. Updated: September 2026.
914,262 professionals have used our research since 2012.