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IBM Smart Analytics vs OpenText Intelligent Classification comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Mar 29, 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
9th
Average Rating
7.0
Number of Reviews
1
Ranking in other categories
No ranking in other categories
OpenText Intelligent Classi...
Ranking in Data Mining
8th
Average Rating
0.0
Number of Reviews
0
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of April 2026, in the Data Mining category, the mindshare of IBM Smart Analytics is 2.8%, up from 0.8% compared to the previous year. The mindshare of OpenText Intelligent Classification is 2.6%, up from 0.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Mining Mindshare Distribution
ProductMindshare (%)
OpenText Intelligent Classification2.6%
IBM Smart Analytics2.8%
Other94.6%
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.
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Also Known As

Smart Analytics
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Overview

 

Sample Customers

WIdO AOK, EEKA Fashion, SSGC, GS Retail
TORA Trading Services, North Star BlueScope Steel, Eldorado Computing, Linkaform, MOBIS Parts Australia, Storengy Engie, Delta RM, Knorr-Bremse Group, KMD, APP Corporation, Westpac Banking Corporation
Find out what your peers are saying about Knime, IBM, Weka and others in Data Mining. Updated: March 2026.
885,728 professionals have used our research since 2012.