For manufacturers and users of connected devices, the demand for more, better information that lies within IoT data is outpaced only by the growth of the data itself. But applying traditional analytics approaches won’t work – these tools weren’t built for IoT.
Tackling the volume, velocity and variety challenge of data from the Internet of Things is key to delivering even more powerful IoT solutions for end users.
ThingWorx Analytics enables enterprises to find the true value in their IoT data – to learn from past data, understand and predict the future, and make decisions that will enhance outcomes.
Monitor edge devices and provide real-time pattern and anomaly detection on real-time data streams.
Provide automated predictive modeling and operationalization for a variety of different outcomes. Pattern and anomaly detection on real-time data streams.
Deliver prescriptive and simulative intelligence that identifies factors that contribute to an outcome and explains how to change a predicted outcome.
Automatically operationalize and maintain predictive and simulative intelligence to deliver to end-users.
ThingWorx Analytics is an integrated capability of the ThingWorx IoT technology platform that enables developers to quickly and easily add real-time pattern & anomaly detection, predictive analytics and simulation to the solutions they build.
Finds anomalies from edge devices in real-time. Automatically observes and learns the normal state pattern for every device or sensor. It then monitors each for anomalies and delivers real-time alerts to end users.
Automatically predicts future outcomes. Subscribes “Things” to relevant outcome-based predictions (time to failure, errors per hour, etc). Displays results in context to end users through any ThingWorx powered solution or experience.
*Requires ThingWorx Analytics Server.
Improve future performance and results with automated prescriptions and simulations. ThingOptimizer automatically identifies the key factors causing a given outcome.
*Requires ThingWorx Analytics Server