4 Major Edge Analytics Use Cases

Edge analytics is an approach that involves the acquisition, analysis, and computation of the collected data at the data source itself or endpoints, thereby eliminating the need to send back the data to a central data repository.

The digital era is well and truly underway. The advent of new-age technologies has transformed the way human race behaves and performs its day-to-day activities. Today, we are seeing inanimate things around us acquiring a digital heartbeat, which allows them to operate by themselves, much like us humans. While things around are getting smarter and functioning by themselves, people are experiencing increasing comfort and convenience in their daily lives. Businesses, on the other hand, gain meaningful intelligence from the analysis of IoT data, which then help them to make necessary product alterations. Excellent service to people can, undoubtedly, increase customer satisfaction levels, which will ultimately generate greater revenues. Realizing the advantages of big data, companies are installing millions of sensors, cameras, and actuators at various locations to gather all the information that makes sense to their business. However, the pace at which the data is generated and collected makes it quite challenging for companies to analyze and process it in real-time. Businesses who have embarked upon their digital transformation journey don’t just have the need to collect data but also to carry out data processing as rapidly as possible to achieve success. To achieve this speed, edge analytics can be useful.

As the name implies, edge analytics is pushing the analysis and processing workload towards the edge of the network rather than allowing all data to travel back to the cloud. With edge computing, businesses can not only generate more sales but also promote cost-savings, boost efficiency, and increase productivity. Let’s check out a few industries that can leverage edge analytics and drive business value.

Manufacturing

Manufacturing companies are generating and collecting high volumes of data every day. But to make constructive use of the collected data, there is a need to do something completely new in addition to the traditional computation methods. Monitoring equipment health, ensuring worker safety, and improving product quality and sales are time-sensitive activities that are carried out by manufacturing companies. All of these activities require continuous monitoring and tracking. Realizing that manually inspecting the manufacturing plant will not be feasible, companies have started embedding sensors to collect data that can then be turned into valuable descriptive as well as prescriptive insights. However, any delay in taking necessary remedial measures can lead to undesirable consequences. Hence, to achieve improved operational efficiency, companies have to collect, analyze, process data, and gain real intelligence almost instantaneously.

For example, downtime in manufacturing units can have a direct impact on production levels. Due to such, companies are often forced to deal with loss in revenues. With sensor technology, these instances can be eliminated only if they gain timely insights on the same. And edge analytics and computing can help manufacturing companies make this possible. With edge analytics, companies can glean meaningful real-time insights on whether any piece of equipment is likely to breakdown. Besides, sensors implanted in various parts of the manufacturing plant can collect metrics such as production quality and yield. Analyzing the parameters can help companies to know the reason for unexpected quality loss or decreased sales if any. Furthermore, wearables can collect information on worker safety status, which can indirectly help in building an excellent culture and achieving productivity goals.

Healthcare

Quality healthcare assistance is mandatory for rural places just as much as for urban ones. However, the healthcare industry often fails to provide the right care at the right time to rural areas even today. But, with the combination of appropriate IoT-enabled healthcare equipment and edge analytics, the healthcare industry can solve this issue. IoT-powered healthcare equipment can constantly collect patients’ health data. Edge analytics will then analyze the collected data without the requirement of consistent network connectivity. As a result of this, people residing in rural areas can immediately know if their health deteriorates. Prompt and timely medical treatment can then be provided to the rural population, which can save a lot of lives. Healthcare assistance like this will tremendously improve the effectiveness of healthcare.

Retail

Today, customers have become more tech-savvy, smart, and also impatient. They not only expect quick service deliveries but also wish for a seamless buying experience. Retailers, therefore, have to stay updated on their customer's preferences and offer them services and products that leave a positive impact on their customer base. Hence, retailers are expected to be continually innovating their services to thrive in this competitive market.

For example, we know that customers today prefer a multichannel buying approach. One such concept that has made a tremendous buzz in the retail landscape is showrooming. Customers prefer visiting stores to visualize and feel the product and then purchase online. The concept is to research the product in a physical retail store before buying it. Retailers have first to gauge individual customer buying habits and then send them discount coupons while ensuring that they don't bombard their customers with irrelevant ads. And to provide personalized service, retailers have to primarily collect customer details like purchase history, social media chatter, cookies, social media activity, and demographic information, which will help them know individual interests in a better way. Later, by using beacon technology, retail brands will know who has entered a physical retail store. Having this intelligence, brands can then reach out to customers with relevant offers and deals. However, a challenging scenario will arise for brands with a larger customer base. Collecting and processing such high-volumes of customer data can be overwhelming. Here’s where edge analytics can help retail brands. As edge analytics has the power to analyze data at the network edge, retail companies will reduce the burden on the central cloud and other analytics platforms. With edge analytics, retail brands can be quick with their situational responsiveness, which will ultimately help them generate more leads and revenue.

Transportation

As the transportation industry facilitates the movement of goods and passengers from one location to another, it has to pay special attention to safety and timely deliveries. Right from fleet management to accident prevention to freight and passenger safety, the transportation industry has to deal with several activities that must be operated accurately. To achieve this level of sophistication, the industry should collect real-time data from transport systems. The transport companies should, therefore, create business models that carry out real-time data analysis and processing that is obtained from their fleet of vehicles.

For example, sensors can help companies get details on whether a vehicle is likely to breakdown. Besides, drivers using wearables will help companies gain information on their driving habits. Information like this is vital to understand whether the modes of transportation are safe and efficient. However, just collecting this data will not be useful for transport companies. Real-time analysis and computation are equally essential to gain actionable insights and act is exigent situations. Edge analytics in transportation will help the companies collect, analyze, and process the data in real-time, allowing them to take necessary actions immediately. This will ensure optimized asset utilization, low maintenance costs, and most importantly, ensure goods and passenger safety.

Edge analytics is an ideal solution for businesses aiming to gain round-the-clock awareness, create predictive models, and offer excellent customer experiences. Pushing analytics to the edge of the network is not an overnight task, no doubt. But businesses who wish to succeed in their digital transformation journey should leverage edge analytics as quickly as possible, albeit with small initial steps.