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Users of the software have found it to be versatile and powerful, with a wide range of use cases. From data analysis in academic journals …
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What is SAS Enterprise Miner?
SAS Enterprise Miner is a data science and statistical modeling solution enabling the creation of predictive and descriptive models on very large data sources across the organization.
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Popular Features
- Automatic Data Format Detection (5)9.393%
- Extend Existing Data Sources (5)9.090%
- Connect to Multiple Data Sources (5)8.181%
- Visualization (5)7.171%
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What is SAS Enterprise Miner?
SAS Enterprise Miner is a data science and statistical modeling solution enabling the creation of predictive and descriptive models on very large data sources across the organization.
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What is SAS Enterprise Miner?
SAS Enterprise Miner is a data science and statistical modeling solution enabling the creation of predictive and descriptive models on very large data sources across the organization.
Business users and subject-matter experts with limited statistical skills can generate their own models using SAS Rapid Predictive Modeler. Its GUI steps them through a workflow of data mining tasks. Analytics results are displayed in easy-to-understand charts that provide the needed insights.
Business users and subject-matter experts with limited statistical skills can generate their own models using SAS Rapid Predictive Modeler. Its GUI steps them through a workflow of data mining tasks. Analytics results are displayed in easy-to-understand charts that provide the needed insights.
SAS Enterprise Miner Technical Details
| Operating Systems | Unspecified |
|---|---|
| Mobile Application | No |
Frequently Asked Questions
SAS Enterprise Miner is a data science and statistical modeling solution enabling the creation of predictive and descriptive models on very large data sources across the organization.
Reviewers rate Automated Machine Learning highest, with a score of 9.9.
The most common users of SAS Enterprise Miner are from Mid-sized Companies (51-1,000 employees).
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Community Insights
TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!
- Business Problems Solved
- Pros
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- Recommendations
Users of the software have found it to be versatile and powerful, with a wide range of use cases. From data analysis in academic journals to risk modeling in the banking industry, this software has proven to be a valuable tool for solving various business problems. It has been used by students for lecture assignments and major projects, as well as by professionals in the financial services sector for credit risk models, propensity models, and decision trees for client segmentation.
One notable feature is the software's ability to build complex data mining and statistical models quickly and easily. Users have praised its simplicity in determining important variables for pricing models, making it ideal for researchers and analysts. Additionally, the software allows users to build multiple predictive models and efficiently compare results, helping them make informed decisions and increase their return on investment.
Furthermore, the software's flexibility in model selection and its accuracy in producing forecasts have made it a leading analytics solution in the market. The graphical analysis provided by the software is often considered cleaner compared to other products, enhancing the user experience and facilitating insights. Whether it's analyzing large volumes of claims, performing sentiment analysis, or creating scoring models for prediction, users have found this software to be a reliable and efficient tool for solving statistical problems and extracting valuable insights from data.
Overall, customers recommend this software for its performance across various industries and business functions. It has proven to be an indispensable tool in improving decision-making processes, enhancing internal efficiencies, and gaining valuable insights from data analysis.
Easy to Use and Accurate: Users found SAS Enterprise Miner easy to use and accurate, with the ability to handle multiple data sources effectively. Several reviewers mentioned this as a positive aspect of the software.
Wide Range of Algorithm Choices: Users appreciated the wide range of algorithm choices available in SAS Enterprise Miner. They mentioned that it includes various options for data mining tasks such as random, neural networks, support vectors, ensemble modeling, and more. This was stated by many users.
Top-Class Customer Support: Users praised the customer support provided by SAS for Enterprise Miner. They mentioned that it is top class and helpful when they encountered any issues or had questions about using the software. A significant number of reviewers highlighted this aspect.
Difficult to Learn: Several users have found the software difficult to learn, requiring hours of effort and training. They mentioned that it is not easy to grasp the nuances of each model, especially for someone with a stats background.
Outdated Interface: Many users expressed their dissatisfaction with the software's interface, describing it as old-school and not updated with trendy methods. They felt that the dashboard and interface were outdated, clunky, and not user-friendly.
Expensive and Difficult to Obtain: Users mentioned that the software is expensive and can be challenging to obtain, particularly for students or small businesses. Some reviewers also pointed out that it takes time to gain access to SAS tools and is costly compared to other data analysis software on the market.
Users have made several recommendations based on their experiences with SAS Enterprise Miner. First, it is recommended to request a demo session and prepare specific questions to address business problems before making a decision. This allows users to fully evaluate the software's capabilities and determine if it aligns with their needs. Second, users suggest trying both R and SAS Enterprise Miner free trials to determine which software is better suited for their data needs. This allows for a comparison of the features and functionalities offered by each option. Lastly, incorporating open source languages within SAS Enterprise Miner is advised. This recommendation aims to enhance the software's flexibility and expand its capabilities by leveraging additional tools and resources.
Attribute Ratings
Reviews
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SAS Enterprise Miner is used by my department for smaller machine learning models (clustering) and predictive analytics (future churn rate calculation), as well as data exploration and pattern analysis. We use the tool as well as recommend it for clients to help them improve business decision making (e.g loan decision making) and internal efficiencies.
- Very easy to use and intuitive.
- High performance.
- Open source integration with R.
- Amazing data science models.
- Very good data preparation and exploration toolkit.
- Still the same, very old and clunky GUI.
- For smaller organizations, it can be quite pricey.
- For less experienced users, the software can be a little overwhelming.
87.5%
8.8
70%
7.0
90%
9.0
80%
8.0
80%
8.0
- It has a positive ROI to our business, as our sales lead rate increased after we started recommending SAS EM.
- Our business operation numbers improved after we introduced SAS EM and started using predictive analytics for our customer retention and customer chain prediction.
- The statistical modelling for the risk controls in our financial department helped to reduce the related residual risk.
SAS EM has a very great set of machine learning and predictive analytics toolsets, which helped our organization achieve its goals. We used other tools, but for us, SAS EM was the most intuitive and easy to learn the tool and it provides greater data exploration and data preparation capabilities compared to the other tools we used.