Harnessing IoT Data for Business Value
SAS Analytics offers a robust, scalable, and open edge-to-enterprise platform that bridges IT and operational environments, covering the entire analytics life cycle. From visualization to statistical modeling and from descriptive to predictive and prescriptive analytics, SAS specializes in IoT analytics, incorporating AI, machine learning, and deep learning to help organizations minimize risks and extract tangible business value. The integration of IoT data from diverse sources, whether at the edge, in the cloud, or anywhere in between, facilitates the transition towards AIoT, the artificial intelligence of things.
Partner Ecosystem and Industry Applications
One of the key strengths of SAS Analytics is its collaboration with leading-edge partners to deliver transformative IoT and AI solutions that drive significant business value. Examples of these collaborative efforts include Livestock Monitoring for optimizing animal growth cycles, Flood Prediction & Preparedness for enhancing real-time situational awareness, and initiatives like the North Carolina Collaboratory utilizing IoT analytics to ensure efficient vaccine delivery. Additionally, deployments such as The Sinclair Hotel showcase how SAS, together with partners like T1A, Intel, and Cisco, improves guest experiences and operational efficiency.
Advanced Analytics Capabilities
SAS Analytics empowers organizations with advanced analytics solutions that embed AI and machine learning. By analyzing various structured and unstructured IoT data sources, users can leverage SAS and open-source machine learning models to gain insights from streaming data in real time. Real-world applications, such as Lockheed Martin's use of machine learning for predictive maintenance and SSAB's implementation of AI for production efficiency improvement, highlight the effectiveness of SAS Analytics in diverse industry contexts.
Streaming Analytics and Big Data Management
The combination of streaming data, advanced analytics, and AI in SAS Analytics enables the identification of patterns that support real-time decision-making. With streaming data processing capabilities, organizations can continuously score data streams and drive immediate actions. The solution also offers sophisticated big data management functionalities, allowing users to process, filter, and analyze vast amounts of data efficiently. Use cases like American Honda's automated warranty data reporting and Rijkswaterstaat's predictive infrastructure maintenance illustrate the practical applications of SAS Analytics.
Intelligent Edge Computing for Scalability
SAS Analytics provides optimized edge computing capabilities that leverage machine learning to process high volumes of data with low latency. The platform's distributed, in-memory grid processing on commodity hardware offers linear scalability as data volumes increase. Organizations like Rijkswaterstaat have transitioned from reactive to predictive maintenance using SAS Viya, leading to improved real-time insights and operational efficiency. This shift towards intelligent edge computing enables organizations to make informed decisions efficiently and effectively.
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