Sensor Data Management

Accurate, real-time, autonomous sensor feedback.

Organizations world-wide use sensors to monitor operations and guide decision-making. From the manufacturing industry, which uses sensor data to control production, to the automotive industry, where sensors can gauge system function and provide key inputs for autonomous vehicles, the speed and accuracy of sensor processing is crucial.

sensor data challenges can prevent businesses from achieving optimal results.

Delayed sensor feedback.

Processing delays of sensor data often reduce the efficacy of a system, because real-time processing lies beyond the capabilities of many sensor systems.

Inaccurate or failed sensor data.

With time and extended use, sensors gradually move out of alignment or fail altogether. When they do, organizations do not always have adequate tools to spot the failure in a timely way, and prevent missing or inaccurate data from adversely affecting outcomes.

Autonomous monitoring and sensor control.

Our vastly improved algorithms process data faster and with higher accuracy that allows systems to keep up with the inflow of sensor data. In addition, Ellipsoid Analytics’ unprecedented capabilities to detect anomalies and sensor failure creates an autonomous system that controls for input accuracy and makes output calculations 100% numerically accurate.

Real-time Sensor Operational Monitoring

Sensor data needs to arrive in a timely manner to be actionable. By providing feedback in real-time, businesses can monitor their systems more efficiently and adjust based on up-to-date information. Real-time sensor monitoring also allows for more accurate operational decisions within a system — leading to performance improvements in the short-term, and a more solid foundation for building better operational algorithms in the future.

Autonomous Sensor Failure Detection

EA’s sensor data management identifies sensor failure in real-time and adjusts feedback accordingly. Our autonomous system creates the tightest allowable maximum deviation in the industry in a data-deterministic fashion without the need for human supervision.  Organizations can monitor the overall health of their sensor systems and make numerically informed decisions about  replace failed sensors and/or managing their output.