Hitachi Solutions America introduces v 2.0 of its IoT Service Hub

Hitachi Solutions America, provider of intelligence solutions powered by the Microsoft Cloud, has introduced version 2.0 of its IoT Service Hub.

The new release includes efficiencies that strengthen the IoT platform’s real-time monitoring, rules-based analytics, and autonomous decision-making capabilities. The new functionality also widens the range of industries supported and products that can be integrated into the solution including architecture/engineering/construction (AEC).

Hitachi Solutions IoT Service Hub 2.0 includes new features that help:

More efficiently build, deploy, and support production IoT solutions

• Efficiency improvements help automatically manage and delete blobs
• New tools help clone large numbers of IoT devices/assets at one time
• Enhancements to the deployment process include the addition of stream analytics, sample data clean-up, and a data “starter set”

Deploy IoT Service Hub in a wider variety of industries and businesses

• Measurement type entity creation ensures flexibility with different IoT sensors that capture different types of data  — allowing support for more industries
• Automatic case and work order creation in Dynamics 365 provides increased data visibility and accessibility

Integrate with Microsoft IoT Central

• IoT Service Hub now integrates with Microsoft’s IoT Central to more quickly get those users up and running with basic IoT monitoring

“The enhancements to the IoT Service Hub increase the power, scalability, and usability of the platform,” said David Bishop, Senior Vice President and Head of R&D at Hitachi Solutions America. “This allows our clients to build more robust and flexible IoT solutions, implement them in a wider variety of industries and businesses, and support them more efficiently.”

Built on the Microsoft Azure cloud, the IoT Service Hub serves as a gateway for creating end-to-end, autonomous IoT equipment maintenance solutions that help proactively pinpoint service needs and manage the entire corrective service lifecycle. Using artificial intelligence and machine learning to perform predictive and rules-based analytics from sensor data gathered from equipment, workers, and the environment, the solution helps:

• Create a data-driven business model with improved operational efficiencies and reduced maintenance costs
• Leverage data analytics to help predict, adapt, and react more quickly and effectively
• Reduce unplanned downtime, enhance recovered capacity, and decrease inventory
• Empower decision makers with real-time visibility and deep operational intelligence