Edge computing combines aspects of centralised computing in the form of either a cloud-based environment, or on-premises data centres and a distributed architecture where devices located on the edge of the network have capabilities to collect, process and analyse data locally.
In a traditional centralised architecture, data from the edge devices would be collected and stored in a central repository for processing and analysis. This kind of architecture is not customer- or user-centric, as it takes data away from a place where, if used correctly and timeously, it would be more impactful. Moving data away from the source may delay processing and analysis of data that is context-sensitive, which may lead to incorrect interpretations of results.
Organisations tend to place more emphasis on customer and revenue data; however, useful insights can also be derived from data that is collected downstream even before a customer is onboarded, which can assist in making good first impressions with prospective customers.
This data mostly comes from edge devices which are the first points of interaction at the beginning of the customer journey. For example, information like how long a customer holds the line before being attended to by a consultant. This data can be collected and analysed by an AI-powered IVR system to ensure calls are routed efficiently, or where smart queuing systems analyse data in real-time to proactively improve waiting periods.
Great user experience is as important for prospective customers as it is for existing customers. AI-powered intelligent edge devices can make this possible by analysing data in real-time and deriving insights that allow for quick responsiveness, which can greatly improve customer service.
Therefore, edge devices can complement the work of service consultants to provide a proactive and responsive service.
Edge computing and the cloud
Edge computing does not replace cloud computing; however, it brings together a centralised architecture of the cloud or data centres and a distributed architecture which supports connected devices at the edge of the network in a complementary manner.
This hybrid architecture can help improve operational efficiencies where time-sensitive workloads are run on the edge devices and workloads that are not time-dependent are run in the cloud or at the data centre.
Edge computing brings together the best of both worlds – the cloud brings on-demand, scalable resources in a secure environment, while the smart edge devices bring responsiveness, which improves overall user experience.
AI-powered intelligent edge devices analyse data in real-time, deriving insights that allow for quick responsiveness.
Data collected by edge devices may be periodically moved to the cloud for analysis in an integrated environment to meet various enterprise reporting needs as well as archiving, to adhere to applicable legal requirements relating to data retention.
There are various types of edge devices and these can range from sophisticated servers that run enterprise-level workloads in remote facilities, to specialised devices like internet of things sensors and smart cameras that are designed to perform a particular task.
Even though they can be connected to a centralised backbone in the form of a cloud or data centre, edge devices can perform tasks remotely and independently. They are less reliant on network connectivity and as such are well-suited to perform critical functions which can run uninterrupted even if there are network outages.
Edge computing benefits
Improved data security: Processing sensitive data such as patient records, personal information and other sensitive information requires a high level of security, and edge computing is ideal for these cases because data is collected and processed locally. This not only eliminates the risk of data being intercepted while in transit, but also helps reduce costs associated with data transmission while adhering to applicable data privacy laws.
Real time responsiveness: Because edge devices collect and process data locally without a need for internet connectivity, the decision process can be made in real-time, and this improved responsiveness results in better user experience.
Improved data quality: Quality data is central to decision-making. For mission-critical systems this becomes very important, as making wrong decisions due to incorrect or incomplete data may lead to undesired outcomes. As data processing takes place on the edge devices, close to the point of capture, the quality of data is greatly improved.
Improved system reliability: Edge computing improves system resilience by providing a self-sufficient system that can operate independently without a need to be connected to a central system over the internet.
Network resiliency: A stable network is required to enable and support automation. Private networks made possible by 5G technology are making industrial automation possible even in remote areas like mines or plants. 5G enables low latency connectivity among various edge devices on-site, providing a stable, dedicated network. This wireless broadband technology is transforming traditional industries like manufacturing, mining and agriculture by automating various functions, resulting in improved production.
Security and surveillance: Recent advances in artificial intelligence and machine learning have resulted in edge devices like smart cameras, which use computer vision to accurately process digital images and videos. Computer vision can help law enforcement agencies with analysing footage and assist to identify criminal suspects. Smart cameras can be used to proactively assist police by predicting the likelihood of crime being committed in a particular area, so that measures are put in place to prevent it.
5G private network: Mobile operators are actively investing in broadband technologies like 5G as they seek to pivot new products and services to attract new clients. The mining industry is embracing 5G technology to digitise operations and this has the potential to improve operational efficiencies and overall safety and in the long run could bring down the cost of mining.
Internet of things edge: IOT edge devices and sensors generate and locally process large volumes of data in real-time. Keeping the computing process on the edge of the network eliminates latency, making these devices process and transmit data in real-time.
Retail industry: Edge computing can bring together online and offline shopping to improve customer experience. Retail apps can be enhanced to personalise the shopping experience while at the physical shop. For example, by recommending products to purchase based on customer shopping history. The app can also notify customers of items that are currently on promotion. Edge computing is therefore powering a move to self-service and autonomous stores. This is opening new opportunities for the retail industry, and it can allow shops to operate 24/7.
Edge computing allows organisations to collect, process and analyse data in real-time to derive actionable insights. Edge devices can harness the power of AI and machine learning to enhance the user experience for both employees and customers.