Compare IBM Smart Analytics vs. SAS Enterprise Miner

IBM Smart Analytics is ranked 7th in Data Mining with 1 review while SAS Enterprise Miner which is ranked 4th in Data Mining with 1 review. IBM Smart Analytics is rated 7.0, while SAS Enterprise Miner is rated 7.0. The top reviewer of IBM Smart Analytics writes "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". On the other hand, the top reviewer of SAS Enterprise Miner writes "Enables Statistical Modeling Of Data Using Base SAS Although Limited GUI is a drawback". IBM Smart Analytics is most compared with SAS Enterprise Miner, whereas SAS Enterprise Miner is most compared with IBM SPSS Modeler, RapidMiner and KNIME.
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Ranking
7th
out of 16 in Data Mining
Views
846
Comparisons
100
Reviews
1
Average Words per Review
435
Avg. Rating
7.0
4th
out of 16 in Data Mining
Views
8,646
Comparisons
3,874
Reviews
1
Average Words per Review
484
Avg. Rating
7.0
Top Comparisons
Compared 11% of the time.
Compared 9% of the time.
Also Known As
Smart AnalyticsEnterprise Miner
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SAS
Overview
The IBM Smart Analytics System offers a wide range of analytics capabilities, enabling you to consume information in the most digestible format, gain insight, and make smarter decisions today and into the future.SAS Enterprise Miner is a solution to create accurate predictive and descriptive models on large volumes of data across different sources in the organization. SAS Enterprise Miner offers many features and functionalities for the business analysts to model their data. Some of the business applications are for detecting fraud, minimizing risk, resource demands, reducing asset downtime, campaigns and reduce customer attrition.
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Sample Customers
WIdO AOK, EEKA Fashion, SSGC, GS RetailGenerali Hellas, Gitanjali Group, Gloucestershire Constabulary, GS Home Shopping, HealthPartners, IAG New Zealand, iJET, Invacare
Find out what your peers are saying about Knime, IBM, SAS and others in Data Mining. Updated: July 2019.
360,582 professionals have used our research since 2012.
We monitor all Data Mining reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
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