Compare IBM Smart Analytics vs. SAS Enterprise Miner

IBM Smart Analytics is ranked 7th in Data Mining with 1 review while SAS Enterprise Miner is ranked 6th in Data Mining with 2 reviews. 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 "Good integration and good stability, but has a complex initial setup". IBM Smart Analytics is most compared with , whereas SAS Enterprise Miner is most compared with IBM SPSS Modeler, RapidMiner and KNIME.
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382,547 professionals have used our research since 2012.
Ranking
7th
out of 16 in Data Mining
Views
144
Comparisons
70
Reviews
1
Average Words per Review
440
Avg. Rating
7.0
6th
out of 16 in Data Mining
Views
4,736
Comparisons
3,586
Reviews
1
Average Words per Review
250
Avg. Rating
7.0
Top Comparisons
Compared 12% of the time.
Compared 8% 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: November 2019.
382,547 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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