INDUSTRIAL DATA PLATFORMS
Data analytics and AI have proven to have a huge impact on industries, especially in areas where manufacturing operations take place. However, the industrial data challenge has some of the most stringent requirements for a data platform:
- Data capture is part of the data platform: equipment, instruments, sensors, machines and gateways continuously generate data in different formats and through different channels. Creating a unified data capture layer is the first challenge for industrial IoT data platforms.
- Connect cloud analytics and edge operations: real-time operations require real-time responses. To achieve this rapid response, the data platform architecture must dynamically balance IoT, edge and cloud capabilities in real time.
- Security is paramount to protect critical operations and processes. Advanced tools that combine cybersecurity, governance and monitoring need to be created to unlock data to new domains where access to manufacturing data has almost always been restricted.
END TO END PLATFORM DELIVERY
Galeo’s engineering team will work with you to design, build and operate your end-to-end industrial data platforms, from sensors and devices integrated at the edge, to analytics systems in the cloud.
Our approach is based on best practices and proven technologies, which will enable your company:
- Build a multi-purpose, non-invasive data platform to extract value from operational data.
- Remotely manage and govern the complete lifecycle of all IoT devices.
- Dynamically balance your analytics capabilities across edge and cloud.
- Orchestrate, secure and protect data flows while democratizing access to data and analytics.
I need an Iot platform. Where do I start?
Connect and explore
- Design of OT-IT integration solutions and data capture at the Edge.
- Building and modeling simple IoT platforms for remote asset monitoring based on managed services in the public cloud.
- Development of embedded software in OT network gateways and PLCs.
- Integration of IoT platforms with corporate data repositories or information platforms.
- Gain visibility of assets and processes buried in isolated silos: disconnected plants and assets.
- Accelerate the adoption of digital technologies in the industrial and production environment: Edge Computing, Remote Monitoring, Advanced Analytics, etc.
- Dramatic reduction in operating and maintenance costs of on-site production assets.
Capture and analyze
- Implementation of complete IoT platforms, including alarm systems, notifications and integration with analytical environments based on the public cloud.
- Design and creation of digital asset network models.
- Implementation of hybrid real-time solutions combining different event technologies.
- Enable analytical capabilities on the data ingested by the IoT Platform.
- Construction of new datasets from the original ones (e.g. event-frames or exceptional alarm streams)
- Apply data quality flows and policies.
- Apply business rules, retention policies and package software artifacts that can run on edge or in the cloud on demand.
Integrates and automates
- Design and implementation of Cloud2Edge solutions with containerized software distribution on the Edge (IoTOps).
- Design and implementation of observability solutions at the Edge for the operation of large IoT deployments.
- Design of digital twins of asset networks and integration with IoT platforms and third-party APIs for exploitation in customized web interfaces.
- Integration mechanisms make rich IoT data available to other business systems such as end applications, reporting dashboards, etc.
- IoT platforms allow assets to be remotely commanded so that automatisms discovered based on ingested data can trigger actions towards the devices.
- Data models that support the creation of digital twins automatically enable the creation of end-user front-end applications by abstracting the developer from the underlying complexity.
- Digital twins facilitate the operation of complex assets and processes by improving the user experience and universalizing the operation.
Contact us and we will help you build and optimize your industrial data platform.
Today, a gas station is much more than a recharging point. It has become an urban facility offering a number of valuable services (car wash, 24/7 store, electric recharging, etc…).
The big barrier to achieving the expected results is a data integration problem. Different equipment and assets require different approaches to predictive maintenance, including basic Condition Based Monitoring systems.