Engineering data preparation is the systematic process of cleansing, structuring, enriching, and tagging raw test and sensor data so engineers can find, trust, and use it quickly for analytics and AI. Done well, it turns chaotic files and logs into a reusable asset that cuts time-to-insight from days to seconds and unlocks predictive models.
Topics: Big Data, Analytics, IIoT, Electric Grid, F1, COTA, Autonomous Vehicles, Analog Data, ADAS, Hybrid Cars, Electric Cars, ESS, ANC, Edge Computing, Factory 4.0, Intelligent Data handling, Build Back Better Act, EV, Digital transformation
Viviota and Rockfish Partner to Accelerate AI-Driven Engineering with Real and Synthetic Data
Topics: Engineering Data, Big Data, Analytics, automotive industry, Electric Grid, Autonomous Vehicles, Machine Learning, aerospace, Hybrid Cars, Electric Cars, Sensor Data Management, Edge Computing, Factory 4.0, Manufacturing, Intelligent Data handling, Software, Digital transformation
The complexity of the electric power industry creates enormous opportunity for Fortune 500 companies and promising technology companies. Increased energy demands, capacity limitations, environmental constraints, varying load shapes, distributed generation and the deployment of new smart technologies all come into play.
The epic storm in February in our home state, Texas, exemplified the fagile nature of our electric grid and the catastrophic consequences of it failure. While new technologies will be developed to build a robust and resilient 21st Century grid, this new grid is still in its infancy and will take years, most likely decades, to reach that utopia of which we dream.
Topics: Big Data, IIoT, Electric Grid, Real-time computing