The last decade has witnessed an explosive growth in the number of IoT-driven connected devices in the manufacturing landscape. This explosion has led to a flood of data being generated within the ecosystem, from the production floors and spreading across the entire supply chain. Undoubtedly, in the coming years, this data deluge will continue to rise exponentially, raising new challenges in data management, security, storage, and analysis. Transferring this data avalanche from innumerable IoT devices and IoT sensors across the cloud, performing extensive real-time analysis, and deriving critical insights are sure to stretch the limits of any organization’s infrastructure. Moreover, all these activities consume a lot of time, which is a precious resource that manufacturers cannot afford to waste. So what’s the solution? Edge analytics, of course!
Finding the perfect answer in edge analytics
Edge analytics is an approach to data analysis in which an automated analytical computation is performed on data at the source instead of sending it back to the cloud. It does the processing close to the device thus reducing the amount of data traffic to the data centers. Simply put, it is analysis in real time and on site. Predicted to be the next big thing in the IoT, edge analytics improves efficiency and reduces the amount of data sent to the cloud for processing.
Bringing analytics to the edge
A large amount of data collected is of no tangible benefit or of no use in their raw form. By running the data through edge analytics as and when they are created, manufacturers can decide what information should be sent back to a cloud and what shouldn’t. This approach reduces the need to store and consolidate excessive data, wasting time and capacity. This will make data easier and less expensive to manage.
The edge gateway acts as an effective proxy between the IoT devices or sensors and the cloud since it analyzes data near the source, enabling faster and smarter decision making. For example, data captured from the sensors connected to manufacturing machinery can be proactively and securely analyzed at the edge. This approach reduces the load on the cloud and enabling immediate delivery of insights and alerts to stakeholders. Factory sensors and IoT devices connected to production floor assets and workers (e.g., sensors on mining staff, temperature-controlled storage, or pressure-controlled units), among many others, provide time-sensitive and precious data that requires immediate action. In such situations, edge analytics saves time and prevents data overload compared to transmitting the data to a central location.
Benefits of edge analytics
Edge analytics is a critical support to every manufacturing ecosystem’s IoT-enabled technology initiatives because it achieves the following benefits:
- Reduces costs of data management and storage
- Lowers costs related to operations
- Other IT assets will remain operational even when one device malfunctions
- Real-time analysis of data for faster, smarter decisions
- Decreases the data traffic to the cloud
- Keeping things local yields IoT-driven security and compliance benefits
- Offers extensive options for customization, easy integration with existing systems, and effortless deployment
- Provides easy options to incorporate cognitive capabilities including AR- and AI-based technologies
- Delivers action-ready data insights specific to different stakeholders – from the operators to the supervisors and the management team
- Supports IoT-enabled predictive maintenance of factory assets with zero time lapse and in fine-tuning the manufacturing process
- Streamlines management of an efficient data repository for easy reference and pattern or forecast-related studies
Edge & Cloud – complementing each other
As IoT expands and breaks new grounds within the manufacturing landscape, the relevance of edge analytics will continue to rise. With edge computing, the decision of which data to be processed and analyzed remains at the source. This can result in discarding or missing of some of the data. Hence, organizations, while implementing edge analytics, should take an informed decision to determine whether the loss of data is bearable to their processes. The benefits of edge analytics are definitely noteworthy. Hence, it need not be an either-or situation. Stakeholders can instantly analyze data at the edge and send the relevant portion of the data to the cloud, yielding in-depth insights. Most organizations find it optimal when they use both in combination, delivering increased operational efficiency.