Edge computing is a novel concept that has been developed to address the problem of processing and store data closer to where it was created. Due to this, computation and analysis of data are able to occur locally and not solely on cloud servers or data centers.
This is specifically true given that modern data management is increasingly becoming more challenging than before due to the constant growth in the volumes of data and sources. Edge computing addresses the problems of high latency and low bandwidth that come with cloud-only designs, increases the robustness and redundancy of the setup, and allows faster analysis and decision-making.
In this blog post, we will be looking at the list of ten advantages of utilizing edge computing in the contemporary business world.
1. Low Latency
A big advantage of this is that it can reduce latencies extremely low, which is crucial for time-sensitive use cases. Pursuing a similar idea of localized data processing, edge computing does not involve constant data swapping between devices and central data centers. This means that it cuts latency down from hundreds of milliseconds to single-digit millisecond figures. In particular, applications like AVs, industrial automation, and AR, which need to analyze data instantly and respond promptly, are unattainable without it.
2. Reduces the Bandwidth Usage
Edge computing also reduces traffic intensity on the network and bandwidth consumption by processing, filtering and analyzing the data locally. In this case, only the processed analytical data or subsets that are deemed necessary are sent to the cloud or data centres. This helps save substantial amounts of money in terms of data transfer and storage in the cloud, as well as valuable network bandwidth.
3. Improved Dependability and Organizational Resilience
Since edge computing decentralizes the computing process to the edge devices closer to the sources of data, its architecture is inherently fault-tolerant, and there is no single entry point into the network. Thus, even if some of the edge nodes or a part of the network are unavailable, critical analytics and data processing continue on other edge devices. This makes systems more dependable and reduces the effects of network or power outages, making business continuity better.
4. Enhanced Protection of Data and Meeting the Regulatory Requirements
Storing such information in a network of cloud data centers centralizes the risk since the attack surface extends. Keeping sensitive data centralized in a cloud data center can be risky because the threats are numerous nowadays.
A potential use case of edge enterprise data management is that data can be collected, analyzed and filtered at the local edge devices located behind organization firewalls, sending only a portion of the data to the cloud. Decryption of the data and cybersecurity measures can also be taken to the edge of the network for increased security.
5. Faster and Better Real-Time Business Analytical Intelligence
Integrated with low-latency and on-site computation, the edge can provide advanced real-time data analysis and insights generation. While moving data to the cloud can be useful when data is collected over time and in large quantities, edge devices can quickly collect data from multiple sensors and apply analytics models to answer questions and make decisions immediately that require quick action.
It is also beneficial to be able to analyze data and provide an immediate response based on it, which is valuable in various fields of manufacturing, energy, transportation, etc.
6. Augmented Data Storage Capacity
It is also important to note that edge enterprise data management devices, while having significantly more limited local storage than a centralized cloud data center, expand the total calculated storage capacity in a network by far when utilizing an edge structure.
Terabytes of data may be processed and buffered on intelligent edge devices to augment cloud data center storage. Only essential data will have to be sent to the cloud for storage or archival use for a long period of time. Thus, optimizing storage in such a model is strategic due to the current rate of data growth.
7. Local support for the implementation of AI and ML models
Therefore, edge enterprise data management helps in the deployment of AI, machine learning and deep learning nearer to the data source. At the edge, real-time data is initially gathered and preprocessed to train ML models and update them constantly.
The trained models can then also be incorporated right at the edge nodes to compute probabilities on new input data that has arrived instead of having to send it back to the cloud. By adopting an edge-based approach, AI and ML can be scaled across different regions and geographies, and data privacy and cost concerns can be minimized.
8. Distributed Computing Power
Edge solutions provide widespread and easily accessible computation throughout the edge network nodes that are away from the central core. This moves the exponentiation of the cloud computing technology closer to the data and the user ends. They can consider using distributed computation resources at the edge more effectively to scale the data processing for everything, ranging from the IoT sensors to computer vision for quality control checks.
9. Reduced Network Congestion
With the increase in the use of IoT, IIoT and Smart infrastructure in industries, the congestion of network bandwidth has become more rampant, especially in industries that are more inclined to data-intensive operations. Edge enterprise data management helps to mitigate this by limiting the data to send to centralized clouds after some preliminary analysis and filtering are done on the edge. Data compute and storage are shifted closer to users through edge data centers to minimize transit.
10. Total cost of ownership (TCO)
It is important to consider the total cost of ownership when acquiring new technologies. Often, the initial acquisition cost may be less than that of an existing technology, but over the life of the technology, the costs are much higher due to upgrades, maintenance, and so on.
Over time, edge computing is set to become commonplace across industries as a result of it’s TCO. It will be a more efficient solution to the management of today’s data environment.
Conclusion
From real-time insights to cybersecurity to decreasing the time it takes to process data, there are numerous advantages to edge solutions in handling the data explosion of the present day. It actually enhances and supports centralized cloud computing to help businesses gain the full benefits of data. As 5G networks continue to take shape, coupled with the enhancement of edge capabilities, edge computing will gain increased adoption in smart cities, manufacturing floors, and retail shops, among others.