Find out what your peers are saying about Knime, IBM, SAS and others in Data Mining.
| Product | Mindshare (%) |
|---|---|
| SAS Enterprise Miner | 10.1% |
| IBM SPSS Modeler | 13.3% |
| IBM SPSS Statistics | 13.1% |
| Other | 63.5% |
| Product | Mindshare (%) |
|---|---|
| SAS Visual Analytics | 1.7% |
| Tableau Enterprise | 9.6% |
| Qlik Sense | 4.8% |
| Other | 83.9% |
| Company Size | Count |
|---|---|
| Small Business | 3 |
| Midsize Enterprise | 4 |
| Large Enterprise | 7 |
| Company Size | Count |
|---|---|
| Small Business | 13 |
| Midsize Enterprise | 10 |
| Large Enterprise | 19 |
SAS Enterprise Miner enables comprehensive data management and analytics, handling extensive data volumes with diverse algorithms for model creation. Its integration and flexibility in SAS code usage make it suitable for both enterprise and personal use.
SAS Enterprise Miner is recognized for its data pipeline visualization, data processing, and statistical modeling capabilities. Its user-friendly GUI and automation support data mining tasks, decision tree creation, and clustering. However, improvements are needed in its interface visualization, affordability, technical support, and integration with languages like Python and cloud-native tech. Enhanced performance, visualization, and model development auditing, along with text analytics in the main license, are desirable upgrades. Integration with Microsoft SQL and combined offerings remains a priority.
What are SAS Enterprise Miner's most important features?SAS Enterprise Miner is applied across industries like banking, insurance, and healthcare for data mining, machine learning, and predictive analytics. It aids in activities such as text mining, fraud modeling, and forecasting model creation, handling structured and unstructured data, and performing ad hoc analysis to model business processes and analyze data clusters.
SAS Visual Analytics offers rapid data processing and advanced forecasting with interactive reporting and visualization. It integrates with diverse data sources, enhancing scalability and automation, enabling data-driven decisions and extensive insight generation.
SAS Visual Analytics provides comprehensive data handling through its advanced reporting and visualization features. Businesses benefit from its ability to process data quickly and deliver insights via interactive dashboards and well-structured reports. Although it faces performance challenges with large datasets and has a complex installation process, it supports both cloud and on-premises deployments. Users can leverage its capabilities in data extraction, transformation, and loading, making it a valuable tool for finance, statistical analysis, and enterprise reporting. Despite some gaps in machine learning and integration with newer data stores, its scalability and flexibility in data management remain key advantages.
What are the most significant features of SAS Visual Analytics?SAS Visual Analytics is implemented across sectors such as insurance and education for tasks like building dashboards and performing business intelligence. It is extensively used in finance and statistical analysis, turning complex data sets into actionable insights, supporting both cloud and on-premises environments.
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