How will AI lead industry with tremendous data!

Contact Here

 Our intelligent devices generate more data than ever before. Today's population of IoT devices numbers more than 10 billion worldwide, and by some estimates, there will be more than 25.4 billion devices by 2025, generating an unfathomable 73.1 ZB (zettabytes) of data. It is not humanly possible to track even a minuscule fraction of that incoming telemetry and analyze it to quickly extract needed business intelligence or spot issues and growing trends in real time.

Consider a nationwide fleet of long-haul trucks that needs to meet demanding schedules and can't afford unexpected breakdowns. With today's IoT technologies, fleet managers attempt to track thousands of trucks as they report engine and cargo status parameters and driving behavior to cloud-hosted telematics software every few seconds. Even with these tools, dispatchers and other personnel cannot possibly sift through the flood of incoming messages to identify emerging issues in the moment, make proactive adjustments across the fleet, and intervene to avoid costly downtime or delays.

A software technique called "real-time digital twins" provides a powerful new way to run these ML algorithms in real time and at scale. This technique assigns each physical data source a unique real-time digital twin, a software component that runs on an in-memory computing platform and hosts an ML algorithm (or other analytics code) along with associated state information required to track the data source. A data source can be any IoT device, such as a truck within a fleet or a specific component from it. Thousands of real-time digital twins run together to track incoming telemetry data from their sources and enable highly granular, real-time analysis that assists in timely decision making. In addition, the system can continuously aggregate state information from all real-time digital twins to help personnel maintain situational awareness.

Incorporating machine learning into real-time digital twins represents a significant step forward in streaming analytics that unlocks new capabilities and enhances situational awareness for fast, informed decision making. It can also help uncover anomalies in telemetry that likely would otherwise remain undiscovered. This combination of technologies gives operational managers and data professionals better insights than ever before into the torrents of telemetry they must track every day.