SAS Enterprise Miner and IBM SPSS Modeler are key players in predictive analytics. IBM SPSS Modeler gains an edge with its extensive feature set and intuitive use, whereas SAS Enterprise Miner excels in data management.
Features: SAS Enterprise Miner features robust data handling, intricate data preparation, and integration with SAS programming for enhanced flexibility. IBM SPSS Modeler offers intuitive visual modeling, an extensive algorithm library, and easy integration with third-party tools like Python and R for streamlined tasks.
Room for Improvement: SAS Enterprise Miner could enhance its response time in customer support, improve its initial setup complexity, and expand its cloud capabilities. IBM SPSS Modeler can improve on its heavy initial investment requirement, refine its model customization features, and further develop its visual modeling strength compared to standalone visualization tools.
Ease of Deployment and Customer Service: SAS Enterprise Miner offers flexible deployment options, both on-premises and cloud-based, with generally reliable customer support. IBM SPSS Modeler provides streamlined cloud integration and proactive customer service that is well-received by users.
Pricing and ROI: SAS Enterprise Miner demands a substantial initial investment yet promises high ROI for large-scale projects. IBM SPSS Modeler, while also costly, often delivers faster ROI due to its user-friendly implementation and operational versatility. Pricing structures should be considered based on specific business needs.
IBM SPSS Modeler is an extensive predictive analytics platform that is designed to bring predictive intelligence to decisions made by individuals, groups, systems and the enterprise. By providing a range of advanced algorithms and techniques that include text analytics, entity analytics, decision management and optimization, SPSS Modeler can help you consistently make the right decisions from the desktop or within operational systems.
Buy
https://www.ibm.com/products/spss-modeler/pricing
Sign up for the trial
https://www.ibm.com/account/reg/us-en/signup?formid=urx-19947
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.