About ThingWorx Analytics
Deliver Industrial IoT Analytics with ThingWorx
ThingWorx Analytics automates complex analytical processes and seamlessly delivers powerful, operationalized insights.
The Internet of Things (IoT) is driving a massive increase in data as billions of new devices are connected every year. Each device generates potentially millions of new data points daily – unprecedented in both volume and pace. For the growing number of enterprises implementing smart, connected strategies and solutions, this data holds invaluable insights.
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How Does ThingWorx Analytics Work?
ThingWorx Analytics is designed to specifically tackle the challenges associated with the volume, velocity, and variety of IoT data. ThingWorx Analytics uses sophisticated artificial intelligence and machine learning technology to deliver reliable, actionable insights in real time to ThingWorx-powered solutions. Integrated with the ThingWorx platform, ThingWorx Analytics automates complex analytical processes and seamlessly delivers powerful, operationalized insights to ThingWorx-powered solutions.
- Operationalize insights, predictions, and recommendations across enterprise functions with automated IoT data to enhance decision-making
- Enable complex analytical capabilities for those who are not data experts with user-friendly interfaces, tools, and applications
- Enables analysis of historical data and forensic investigation of data after an incident through replay functionality
- Production-ready deployment enables enterprises to get up and running quickly – at the edge, on premise, or in the cloud
Incorporates supervised machine learning into industrial IoT solutions and extends data science practices with automated predictive and prescriptive modeling – without the need for algorithm expertise by users
Enables better understanding of industrial IoT data, providing a variety of advanced algorithms that allows users to discover useful patterns and correlations within data
Monitors data streams using a variety of statistical and machine learning techniques to learn “normal” conditions and identify unexpected changes in behavior
Anticipates future outcomes and offers the ability to make relevant outcomebased predictions based on data within ThingWorx
Improves future performance and results by automatically executing simulations to generate recommendations that will optimize the product and process performance
Uses integrated models and other computational providers within the application to simulate behavior of physical products in the digital world