SDC Award win for Schneider Electric

Schneider Electric has been awarded the ‘AI/Machine Learning Innovation of the Year’ category at the annual SDC (Storage, Digitalisation and Cloud) Awards 2020. The award recognises the innovative use of advanced artificial intelligence, ML and data analytics techniques within its EcoStruxure IT Expert™ Public API and its Monitoring & Dispatch Services.

“I’m delighted that both our Monitoring & Dispatch Services and EcoStruxure Public API have been recognised by the SDC Awards,” said Marc Garner, Vice President, Secure Power Division, Schneider Electric UK&I. “2020 has demonstrated the crucial roles that data centres and edge computing play in the digital economy. By creating simple to integrate, vendor-agnostic software platforms that leverage AI and machine learning, we’re helping customers to increase visibility from remote locations, reduce the complexity of managing multiple IT sites, and ensure uptime for mission-critical assets.”

Monitoring & Dispatch Services Schneider Electric’s Monitoring & Dispatch Services enable IT solution providers, managed service providers (MSPs), and end users to optimise resources, improve efficiency, and help prevent downtime by leveraging Schneider Electric to manage the operations of distributed IT infrastructure. By simplifying the management of edge computing sites, the new services reduce the complexities of edge management, ensuring data driven insights are delivered remotely for unmanned sites and enabling significant savings over the infrastructure lifecycle. Services include remote monitoring, troubleshooting, and on-site, next-business-day remediation, including replacement parts. Moreover, by employing a proactive approach to service, end users can save up to 40 percent** over the lifecycle management of their distributed IT.

Benefits of Monitoring & Dispatch Services include:

· Optimise resources: By streamlining and standardising operations, the new services eliminate the burden of managing dispersed physical infrastructure deployments without having to hire additional IT staff.

· Improve efficiency and reduce risk: It helps reduce mean time to repair with access to enhanced reports, providing the ability to plan for required maintenance, future upgrades, and expansions.

· Help prevent costly downtime: 24/7 remote monitoring against system instability and unexpected failures.

EcoStruxure IT Expert Public API

Schneider Electric’s public API (application programming interface) for EcoStruxure IT Expert enables IT solution providers and end users to seamlessly integrate a power and critical infrastructure monitoring platform into any preferred management system. The API builds on the success of EcoStruxure IT Expert, the industry’s first cloud hosted monitoring platform, which is vendor-agnostic and powered by AI.

The first of Schneider Electric’s public APIs helps users simplify management of distributed IT infrastructure. Furthermore, the vendor-agnostic system enables data centre operators and channel partners, including value added resellers (VARs), IT solutions providers, and MSPs, to proactively monitor, manage, and maintain uptime for critical IT assets from remote locations. With this increased access to energy and resource data, users can make changes that prevent downtime, increase efficiency, and drive sustainability.

EcoStruxure IT Expert API key benefits include:

· Third-party integration: Integrate the EcoStruxure IT platform into your existing systems to mitigate risk of downtime by monitoring power and critical infrastructures.

· Improved, instantaneous visibility: Access critical infrastructure data from your preferred system and retrieve a snapshot of its current state to determine any alarms.

· Smarter, data-driven decision-making: Capture alarm, inventory, sensor, and location data to make key decisions and, in the case of a partner, make proactive recommendations to customers.

· Improved reporting: Pull data from your power and critical devices into existing or new dashboards and gain the ability to add metrics with a granular level of detail.