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Guiding principles for our product application roadmaps include, but are not limited to, the following:
Machine Learning Algorithms learn and improve without explicit programming.
They use all data available – the more data, the better the predictions.
Machine Learning can forecast and mitigate problems before they occur and optimize recovery.
Potential applications include:
Outage prediction and response
DER use optimization
Crew deployment optimization
Autonomous damage assessment
Drone image analysis
Predict and adapt to prosumer behavior
Product and service matching
Social media feed analysis
Increased automation and intelligence
Less repetitive tasks for users
More information to make operational decisions (proposed action and why)
User Configurability
All product components aligned to any utility’s processes (workflows, rules, locations, etc.)

Part of upfront design for all new features (basic vs. advanced options)

Performance and scalability
Real-time application results amidst extra-large network models
Big data archiving, querying, and reporting for all components
Data scalability
Database structure – native virtualization for RT database, big data for web apps and historian
Front-end processing – distributed FEPs with native virtualization
Application scalability
Decoupled design – modules designed to solve on a locational basis with bottom-up approach
Interface architecture – leveraging RESTful APIs (proven web-scalable technology)
User scalability