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IBM Smart Analytics vs KNIME Business Hub comparison

 

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
6.8
Number of Reviews
63
Ranking in other categories
Data Science Platforms (3rd)
 

Mindshare comparison

As of June 2026, in the Data Mining category, the mindshare of IBM Smart Analytics is 4.3%, up from 1.0% compared to the previous year. The mindshare of KNIME Business Hub is 10.7%, down from 24.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Mining Mindshare Distribution
ProductMindshare (%)
KNIME Business Hub10.7%
IBM Smart Analytics4.3%
Other85.0%
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."
"KNIME has improved our organization because we are able to collect data in a way that we can interpret and it provides visuals."
"The product is open-source and therefore free to use."
"Stability is excellent. I would give it a nine out of ten."
"KNIME is more intuitive and easier to use, which is the principal advantage."
"It also has very good fundamental machine learning. It has decision trees, linear regression, and neural nets. It has a lot of text mining facilities as well. It's fairly fully-featured."
"It's very convenient to write your own algorithms in KNIME. You can write it in Java script or Python transcript."
"The product is very easy to understand even for non-analytical stakeholders. Sometimes we provide them with KNIME workflows and teach them how to run it on their own machine."
"It is a stable solution...It is a scalable solution."
 

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."
"One thing to consider is that the prebuilt nodes may not always be a perfect fit for your specific needs, although most of the time, they work quite well."
"It could be easier to use."
"One area that could be improved is increasing awareness and adoption of KNIME among organizations. Despite its capabilities, it is not as well-known as other tools. The advertising and marketing efforts to reach out to companies and universities have not been very successful."
"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."
"Compared to the other data tools on the market, the user interface can be improved."
"KNIME is not scalable."
"The main issue with KNIME is that it sometimes uses too much CPU and RAM when working with large amounts of data."
"KNIME needs to provide more documentation and training materials, including webinars or online seminars."
 

Pricing and Cost Advice

Information not available
"It's an open-source solution."
"I use the tool's free version."
"This is an open-source solution that is free to use."
"We're using the free academic license just locally. I went for KNIME because they have a free academic license."
"The client versions are mostly free, and we pay only for the KNIME server version. It's not a cheap solution."
"KNIME is free as a stand-alone desktop-based platform but if you want to get a KNIME server then you can find the cost on their website."
"The price for Knime is okay."
"This is a free open-source solution."
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Top Industries

By visitors reading reviews
No data available
Financial Services Firm
12%
Manufacturing Company
9%
University
8%
Educational Organization
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
By reviewers
Company SizeCount
Small Business21
Midsize Enterprise16
Large Enterprise32
 

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?
I would describe KNIME Decision Hub as somewhat helpful in making data-driven decisions more efficient. It could have been a scalable decisioning as a service at the back end, but it's not working ...
What is your primary use case for KNIME?
I mainly use KNIME Business Hub currently for data ETLs and then it meets with predictive analytics. Sometimes I utilize it for forecasting, but mostly it's predictive analytics. I have utilized bo...
 

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: June 2026.
902,270 professionals have used our research since 2012.