Software Development

How Does Edge Computing Make Digitalization Accessible?

How Does Edge Computing Make Digitalization Accessible

Digital transformation at the edge is on the rise. It’s becoming clear how enterprise edge computing apps fit into digital transformation efforts as they acquire traction. Almost every major organization is rushing furiously into digital transformation with a sense of purpose. This year, they are predicted to reach $176 billion, growing 14.8% from 2021. They also estimate the growth rate to continue in the coming years, with edge computing hardware, software, and services investment reaching over $274 billion in 2025.

Throughout the digital ecosystem, edge computing is used in both discrete and continuous process applications. Edge computing is a distributed architecture that allows devices to interact with people in real-time and make educated decisions.

Businesses are taking advantage of edge computing on assets, machines, and production lines to increase plant reliability and overall plant efficiency through applications like machine analytics and asset performance. By the year 2028, mobile consumers will account for 21.7% of all infrastructure edge power, making them the greatest edge computing footprint.

What is Edge Computing?

Edge computing improves the performance of Internet devices and web applications by bringing computation closer to the data source. Edge computing helps workers to focus on production, and production-centric results are the payout for digital transformation strategies.

Organizations were compelled to reconfigure work processes as a result of the pandemic, adopting tactics such as remote operations and social spacing, and the resulting concerns about visibility and supply chain flexibility increased demand for edge computing. Concerns about industrial operations, maintenance, and meeting market needs grew as a result of the pandemic.

Also read: Why The Edge is Important for Unlocking IoT’s Full Features

Why Edge Computing?

Real-time data analysis is required in industries such as healthcare, finance, manufacturing, and telecommunications. There are only a few occasions in today’s highly evolved culture where quick and efficient data is required. Edge computing achieves this by speeding up the data flow from IoT devices.

Edge computing comes in handy in a range of scenarios. The reaction time of data is determined by the circumstances and scope of the project. We can make faster decisions with data that has been collected and analyzed. When data is consumed and processed quickly, it becomes more useful. We are living in a time when having the right insights at the right moment can have a big impact.

What are the benefits present in Edge Computing?

Look at the enticing benefits that edge computing is ready to offer your business:

Low Latency

In cases where latency is an issue, edge computing can benefit. It minimizes latency by avoiding the need to send data to a cloud or data center through a network. Latency is the amount of time it takes to process and assess data. A connected device’s reaction time to the internet is less than a second.

The reaction can be delayed or interrupted in a variety of scenarios. The response may be delayed or halted in different ways. It happens when the internet speed is down or when the network infrastructure is too far away from the device. In such instances, the data processing speed is slowed. The computing time should be close enough to compensate for the long-distance data transfer lag.

Data Security Risks

Every device is connected to the cloud, and cloud computing uses the internet to handle raw data. It can have security, privacy, and legal implications, especially when dealing with sensitive data. Edge computing eliminates this risk by processing data close to the source.  Administrations have the ability to keep data within their own borders while also ensuring that data retention standards are met. These regulations have given citizens more privacy.

Bandwidth Problems

Businesses employ cloud computing to process and analyze large amounts of data. To transport data to the cloud, the IoT requires massive quantities of electricity and bandwidth. IoT devices that generate data and run software should be connected to the cloud in order to collect and process data. Edge computing can allow enterprises to save costs on internet bandwidth. Large volumes of data can be processed in close proximity to the source.

Bandwidth cost issues

IoT devices generate large volumes of data and send non-essential updates to the cloud. As this method requires more bandwidth, the cost of bandwidth increases. The cost of the device needed to access that bandwidth, as well as storage and analysis charges, increases. Edge computing allows this data to be captured and analyzed locally before being transferred to the cloud. It will be far cheaper than sending unfiltered data over costly WAN networks.


Edge computing streamlines data processing, reducing network traffic. Computing IoT devices have a minimal resource impact on the system if the application and control planes are near to the data.

Better App Performance:

The lag time will be reduced when the data is processed and stored near the source. It improves the app’s function.

How is the performance of the edge refined?

Edge computing is the concept of performing computations on devices at the network’s edge. It allows for real-time data handling on the drone unit itself. When integrated with other technologies, edge computing also has the potential to improve security and privacy. Edge computing, which is similar to blockchain, is built on the concept of extending services from a centralized server to a large number of nodes or devices. Edge computing on blockchain-registered devices, when combined, offers users increased security, functionality, and privacy. In some areas, such as healthcare and adtech, there is a demand for edge computing.

What are the use cases for Edge Computing?


Consider a company that grows vegetables without the use of sunshine, soil, or pests. More than 60% of the time it takes for plants to grow is saved using this strategy. The system can identify water use, and nutritional content, and estimate the best harvest time by using sensors. Data is collected and processed to determine the effects of environmental factors, build crop-growing algorithms, and ensure that crops are harvested in the right way.

Improved Healthcare

In the healthcare industry, the amount of patient data collected by devices, sensors, and other medical equipment has risen. Cloud transformation like Edge computing is required to maintain a high volume of data in order for physicians to access the data, eliminate other normal data, and discover problematic data in real-time so that they may assist patients with ease and avoid health issues.


Edge computing was utilized by an industrial corporation to monitor manufacturing, allowing for real-time analytics and machine learning to detect production mistakes and enhance product quality at the edge. Environmental sensors were installed across the manufacturing plant thanks to edge computing, which provided insight into how each product component is created and stored, as well as how long they stay in storage. The manufacturer can now decide on the industrial facility and production activities more swiftly and correctly.

Also read: Top 10 Manufacturing Analytics Use Cases in Supply Chain

Network Optimization

Edge computing can help to optimize network performance by measuring connection speeds for users all across the internet and using analytics to find the most reliable, low-latency network channel for each user’s data. It is used to route traffic throughout the network in order to get the best possible performance in time-sensitive traffic.


For retail firms, monitoring, stock tracking, sales data, and other real-time business facts can generate lots of data. Edge computing can benefit from the analysis of this huge amount of data as well as the identification of business opportunities such as a successful elevated platform or campaign, sales forecasting, vendor ordering optimization, and more. Edge computing can be an efficient solution for data processing at each store because retail enterprises might differ considerably in local contexts.


On a daily basis, autonomous vehicles acquire up to 20 TB of data on their positions, performance, vehicle condition, speed limits, traffic conditions, and other vehicles. While the vehicle is in motion, data must be collected and analyzed in real-time. Each autonomous car becomes an edge as a result of this, which demands extensive onboard processing. Moreover, the data enables governments and businesses to manage vehicle fleets based on current ground conditions.

Workplace Safety

Edge computing can help businesses monitor workplaces or ensure that workers follow established safety protocols, especially in remote or unusually dangerous circumstances like construction sites or oil rigs, by combining and analyzing data from on-site cameras, worker safety devices, and a variety of other sensors.

Wrapping Up

Edge computing is a promising technology for businesses in today’s fast-paced environment. New methods of edge computing are not only advantageous but also unavoidable. Many firms have started a strong desire to gain the benefit of customizing, real-time data analysis experience, and decreasing system problems to meet the end goals of the business.

To ensure that technological breakthroughs are created in a timely manner, subscriptions, event-driven programming, isolation, the pay-per-use model, and other ideas are considered too. Edge computing solutions can be obtained at a reasonable cost from globally recognized cloud deployment services, assisting enterprises to make more profit in less time.

Written by
Madhu Kesavan

Madhu Kesavan is the founder & CEO of W2S Solutions, a globally recognized digital transformation company empowering enterprises and governments in their digital journey. With 20+ years in the IT market, he makes his vision for a sustainable future come true by leveraging technology.

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