How to Achieve Edge-to-cloud Visibility?

To fully make use of enterprise data, it is best to use a platform that ensures a seamless transition from local edge systems to the cloud. Any enterprise operation that thrives on digital advances needs access to data. Full transparency and visibility are more crucial than ever. Turning that data into high-quality insight-capable data, storing it in the form of long-term data histories, and sharing it with stakeholders across geographies cannot take place without the cloud. This is why, in recent years, there has been a concerted effort across enterprises to popularize cloud adoption. At the same time, the edge is equally vital as it ensures quick reaction times and faster insight. Also, it allows companies to perform simple predictive analytics locally, which is the advantage of real-time data coming from the edge of the IoT network.

The bottom line is that, to make IoT truly work in your favor, you need both edge and cloud capabilities. Even more so, companies need to create solutions that allow for the uninterrupted, fast, and secure transition from the edge to the cloud service. These solutions need edge data to make the most out of predictive maintenance, asset tracking, and condition monitoring that are performed locally. But equally so, companies require cloud capabilities for more sophisticated analyses. For example, you may want to build machine learning solutions that fulfill the needs of global large-scale manufacturers.

Edge and cloud: In taking the best of both worlds, enterprises are fully equipped to enhance operations sustainably and at scale.

How IoT Platforms Can Help

This is where IoT platforms come into play. IronFlock serves as the connective tissue within data-driven organizations, allowing for seamless integration of edge and cloud systems. Accessing the data at the edge takes place over a secure internet connection. Devices are linked to the platform within minutes and data extraction apps can be deployed to devices within seconds. This way, organizations are best positioned to access the data they need to build machine learning models or enhance existing analytics solutions.

Once machine learning models are built in the cloud, you will also need the infrastructure to deploy those models back at the edge of the IoT network. This is why you need a consolidated IoT platform that extends in both directions to enable full connectivity. You get easy access to streamed data that can be used right away and close the development cycle by deploying updated models.

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Why the IoT Edge Matters

Whereas a cloud-first mentality is great for machine learning and advanced analytics, in short, anything that makes data science teams thrive within an organization, the edge should not be underestimated. As companies are facing increased volumes of data, it becomes necessary to manage that data more effectively and supplement the cloud with edge computing capabilities.

For one thing, the edge allows companies to arrive at insights faster.

Many use cases, especially in manufacturing and predictive analytics, require the zero-latency advantage of the IoT edge. In such cases, it only makes sense to extract and analyze data right at the edge device. This is how you can act on that data instantly in a variety of scenarios. This includes tackling maintenance issues, avoiding production hiccups, and overseeing overall effectiveness.

Specifically, the use cases that are suitable for the cloud usually depend on long-term data histories or big data analytics. If you want to do asset tracking, condition monitoring, or oversee overall equipment effectiveness, you will need that data immediately. Edge computing comes to the fore in such scenarios. This not only allows you to react right away but also significantly reduces the amount of data that is sent to the public cloud.

How You Can Extend Your Operations with an Edge-to-Cloud Platform

Once you have shifted to an IoT platform that consolidates your assets from the edge all the way to the cloud, you will be able to effectively bridge the gap between your edge devices such as sensors, actuators, and IPCs, and the sophisticated AI capabilities that come with cloud computing.

IronFlock easily connects to any industrial asset at the edge. It securely streams the data to the dedicated data studio where the data is stored and ready for analysis. By deploying IoT apps directly to the edge of the network, you can additionally tell devices what data to send to the cloud and how to interpret that data. The platform enables additional analyses and ML in the cloud. At the same time, it makes it possible to run any IoT application and perform analytics at the edge gateway.

This is how you benefit from both worlds. The cloud only receives the data that is relevant for data modeling, advanced analytics, and ultimately, machine learning or artificial intelligence tasks. And the edge processes the data coming from the devices, pre-processes, and hands it over to the cloud where necessary. So again, you leverage all the data coming from legacy equipment, sensors, PLCs, and more sophisticated industrial PCs. At the same time, you take taking full advantage of the advanced capabilities of the cloud.

Our Best-of-both-worlds Approach

IronFlock can help you here. The platform allows you to collect IoT data from connected devices, perform data processing at the edge, store that data in the public or private cloud, build machine learning models right on the platform, and deploy these back to the IoT to come full circle. So it enables you to do both. That is, you get local edge analytics and basic data visualizations on the basis of the streamed data. But you also build ML models and update existing AI at the edge. These are conveniently packaged as IoT apps so that you can roll out any modifications over the air.

Here is a summary:

IoT Edge Analytics

This is great for asset monitoring and tracking, condition monitoring, and predictive analytics. You can build standard predictive maintenance use cases, and oversee any type of operation on the shop floor. Also, you track KPIs conveniently from a single platform, detect outliers, and send out red button alerts. Practically, you do anything that requires fast access to real-time data.

Machine Learning and AI Models in the Cloud

Storing the data in the platform’s data studio, you can build your machine learning models. Then you visualize right in the dedicated data science workbooks using Python and SQL cards. Taking it from there, you package these models as IoT apps and deploy them to the edge in one seamless gesture.

Data Visualization in Custom Dashboards

Once the edge data is streamed into the data studio, you easily build visualizations based on that data. For this, you use a variety of templates and customization options. Or you integrate with an external visualization tool such as PowerBI or Tableau if this is your preference. This is how you get a bird-eye view of your shop floor, leading to greater asset visibility.

OTA Deployments and Updates

All logic created on the platform can be deployed to the edge over the air, with just a few clicks. Further, the platform comes with all the infrastructure needed to continuously monitor the ML models. So you can debug, improve performance while testing right on the IoT device, and run OTA updates.  

This is how you achieve edge-to-cloud visibility for your industrial assets and therefore, full round-the-clock transparency on the shop floor. And then again, this is how IoT development gets faster. This allows you to move from PoC to deployment in just over 60 days. And you can implement a massive scale-out edge deployment quicker, creating the basis for a wealth of industrial automation cases.

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