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IBM Smart Analytics vs SAS Enterprise Miner comparison

 

Comparison Buyer's Guide

Executive Summary

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
SAS Enterprise Miner
Ranking in Data Mining
7th
Average Rating
7.6
Reviews Sentiment
6.2
Number of Reviews
13
Ranking in other categories
Data Science Platforms (23rd)
 

Mindshare comparison

As of April 2026, in the Data Mining category, the mindshare of IBM Smart Analytics is 3.2%, up from 0.7% compared to the previous year. The mindshare of SAS Enterprise Miner is 6.0%, up from 4.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Mining Mindshare Distribution
ProductMindshare (%)
SAS Enterprise Miner6.0%
IBM Smart Analytics3.2%
Other90.8%
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.
reviewer1352853 - PeerSpot reviewer
Executive Head of analytics at a retailer with 5,001-10,000 employees
A stable product that is easy to deploy and can be used for structured and unstructured data mining
We use the solution for predictive analytics to do structured and unstructured data mining I like the way the product visually shows the data pipeline. The product must provide better integration with cloud-native technologies. I have been using the solution for 20 years. The product is very…

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."
"Technical support has been good, and when I called them at the start of using the product with some issues they were very helpful."
"The technical support is very good."
"Most of the features, especially on the data analysis tool pack, are really good. The way they do clustering and output is great. You can do fairly elaborate outputs. The results, the ensembles, all of these, are fantastic."
"Most of the features, especially on the data analysis tool pack, are really good; the way they do clustering and output is great, you can do fairly elaborate outputs, and the results and the ensembles are fantastic."
"he solution is scalable."
"SAS internal support is very qualified and if we have any issues, we contact them and trust that they can help."
"The solution is able to handle quite large amounts of data beautifully."
"It enables statistical modeling of data using Base SAS (another product from the same vendor) as the backbone."
 

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."
"The initial setup is challenging if doing it for the first time."
"The user interface of the solution needs improvement. It needs to be more visual."
"The preparation of both the mining and modeling process could be improved. The solution requires data and will reflect data, but the preparation of the data is not useful for end-users; we ended up having to do the preparation in another tool."
"Plus it is prohibitively expensive and is not available with perpetual licensing."
"The solution is much more complex than other options."
"The visualization of the models is not very attractive, so the graphics should be improved."
"We really don't like the protocols the solution offers. The solution is much more complex than other options."
"The solution is quite expensive. The pricing is too high."
 

Pricing and Cost Advice

Information not available
"The solution must improve its licensing models."
"This solution is for large corporations because not everybody can afford it."
"The solution is expensive for an individual, but for an enterprise/institution (purchasing bulk licenses), it is not a high price for the use that will come from it."
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Top Industries

By visitors reading reviews
No data available
Financial Services Firm
19%
Construction Company
12%
Educational Organization
9%
Manufacturing Company
9%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
By reviewers
Company SizeCount
Small Business3
Midsize Enterprise4
Large Enterprise7
 

Also Known As

Smart Analytics
Enterprise Miner
 

Overview

 

Sample Customers

WIdO AOK, EEKA Fashion, SSGC, GS Retail
Generali Hellas, Gitanjali Group, Gloucestershire Constabulary, GS Home Shopping, HealthPartners, IAG New Zealand, iJET, Invacare
Find out what your peers are saying about Knime, IBM, Weka and others in Data Mining. Updated: March 2026.
886,011 professionals have used our research since 2012.