How Edge Analytics makes machines smart

Edge Analytics overcomes data latency with a simple intervention. It makes data collection, processing, and analysis at the edge of the network in real-time possible.

Breaking away from the traditional method of collecting and sending data to a centralized location, Edge localizes data processing. It gives the power to react instantaneously, as the data set changes, fulfilling the critical needs of many business processes which thrive on real-time decision making.

One of the most common examples of this technology is autonomous driving. The deployment of advanced analytics together with machine learning at the point of collection of data delivers real-time intelligence.

The years 2021 and 2022, witnessed the breakneck speed of digitization. With the number of IoT-connected devices increasing every day, Edge analytics has become mainstream. From the simplest of tasks to more complex industrial uses, the varied applications of Edge analytics make it ideal for use in distinct business processes.

The high reliability of Edge analytics in smart machines creates a platform for self-responsive actions by deciphering data in real-time. Behind the scenes, Edge analytics lends proactive actions to eliminate process disruptions and ensure 24/7 business continuity.

Cloud storage and processing of data are often marred by high expenditure and lengthy latency. Edge analytics assists in overcoming the most difficult challenge of the distant cloud data center. broadband usage makes Edge analytics one of the best technologies to overcome the time lag in data transmission.

The large-scale usage of IoT-connected systems becomes an impediment if interoperability concerns pop up. Edge analytics helps to plug the most prevalent problem of interoperability by placing operations at the edge. It powers the devices to churn data without costly and time-consuming transmission methods, making their applicability simpler and wide-ranging.

When each device harnesses its data, the processing takes place at the periphery without interfering with other processes. The overall workload of computation is distributed among all the devices, creating an opportunity to scale seamlessly without impacting another.

One of the main components of the traditional data processing workflow—transmission, is eliminated completely. The proximity of data processing, where the collections occur, not only enhances data collection and analysis but also enhances security with the decentralized approach. It enables efficient collaboration with strategically placed edge nodes. The placement of data in a centralized storage system, where distributed endpoints transact, is cut off.

Edge Analytics received the much-needed boost during the pandemic years. The Edge Analytics space is likely to see heightened activity in the coming years, led by technological development that augurs well for it.

1. 5G: With 5G’s promise of high-bandwidth and low latency wireless data delivery, the computational ability has been pushed to the edge from the cloud, opening a host of opportunities in Edge Analytics.

2. AI: Artificial intelligence is at the center of data. Edge and AI can go hand in hand by being a catalyst to overcome the technological challenges that engulf AI applications.

3. IoT: Localization of data with Edge Analytics will lead to IoT-enabled equipment such as wearables, vehicles, infrastructure, and manufacturing machinery to interact quicker, significantly cutting down the time taken for analysis and decision making.

4. Fog Computing: Fog computing is basically a decentralized infrastructure for computing where data, applications, computation and storage are distinctly placed. Mostly, fog computing is used in the short run, while in the long term, the computing is reserved for the Cloud. Fog computing performs similar activities to Edge, though only in the short run.

Deploying an Edge solution is a strategic decision. Before you embark, certain aspects like integration with existing hardware, and core applications used in the business should be considered carefully.

Though, it is prohibited by the size, the volume of data that can be analyzed at the periphery. There is a trade-off that you need to consider: data latency vs raw data processing. With Edge analytics, you have the power of processing data at the periphery, but you lose the raw data. Hence, its deployment should be viewed on a case-by-case basis. Bosch helps you to identify the right structure for AI use case deployment at the Edge. You can choose to deploy or enhance hardware for the identified architecture with Bosch.

Edge analytics has many benefits, such as media, automobile, energy, retail, and logistics, where its applications cut down processing time and costs by half. The advantage of using smart machinery for bandwidth-constrained or non-cloud environments is perhaps a big draw for many industrial uses.

Bosch offers a host of Edge Analytics services regardless of the choice of hardware selection. Our Edge Instance readiness program comprises containerization, Edge security, remote software and model updates, and DevOps.