For users who need to quickly extract value from their IoT data and investments – in days, not months – SAS Analytics for IoT provides an integrated, business-focused interface that employs a proven way to organize, visualize and act on high volumes of diverse IoT data using a secure, flexible and scalable AI-embedded IoT Analytics platform. This solution’s capabilities are compelling for variety of users including line of business, engineering, IT and data science professional, extending the use of analytics and collaboration the enterprise, and optimizing current IoT investments – SAS, third party and open source.
Why is it important?
Progress from preventative to predictive and prescriptive analytics is slow as companies struggle with data complexities, and with how AI and machine learning can coexist with existing statistical models.
Embedded with advanced analytics and AI, SAS Analytics for IoT offers a solution to the data complexity issue across the analytics lifecycle with a sensor-based data model, streamlined and extensible ETL, an integrated business-focused data selection interface, and high frequency data visualizations that accelerate time to value from IoT data and investments – in days, not months — without coding or specialized skills. These capabilities translate into millions saved in unplanned downtime, timely improvements in operational excellence that boost bottom line results, and advances in digital transformation that enhance customer experience and generate services to drive new revenue streams.
The solution ensures that all types of users across the enterprise can quickly and confidently extract the value they seek from volumes of diverse IoT data and investments – SAS, third party and open source.
For whom is it designed?
For all types of users across a range of industries including, Manufacturing, CPG, Energy and Retail, who support mission critical processes and digital transformation, SAS Analytics for IoT’s streaming execution engine with AI provides real-time analytics, ensuring timely and accurate decision making. With a streamlined ETL and an extensible data model, users can do ad-hoc analysis and analytic system development without knowledge of the underlying data structure. By integrating data preparation, organization, business-focused data selection and exploration, SAS Analytics for IoT optimizes current IoT investments and skills (SAS, third party and open source), and extends the use of IoT analytics and the power of collaboration across the enterprise.
