Edge Analytics Market - Growth, Trends, COVID-19 Impact, and Forecasts (2021 - 2026)

Market Overview

The Edge Analytics Market was valued at USD 3.54 billion in 2020 and is expected to reach USD 17.13 billion by 2026, at a CAGR of 30% over the forecast period 2021 - 2026. Edge analytics, an alternative to big data analytics, provides real-time analysis of data generated on the edge of network devices, which is in an unstructured form. Edge analytics performs the automatic analytical computation of collected data in real-time, instead of sending the data back to the centralized data store or server. Edge Analytics is increasing at significant force across the world, owing to the constant advancements of workplace performance enhancements and increase adoption of internet of things (IoT), which are driving the edge analytics market growth to the large extent. Moreover, its distinctive features like cost optimization and scalability are significantly boosting the growth of the market.

• Edge analytics help companies to get more advanced data faster by employing advanced analytics and machine learning at the point of data collection. Also, it further boosts yields, increases throughput, reduces downtime, and improves efficiency. It is gaining increasing demand owing to the emergence and the booming expansion of the Internet of Things (IoT) and the fast-paced growth in the availability of data through connected devices and via real-time intelligence.
• The manufacturing industry can make extensive use of Edge analytics, for instance, in a smart production line. Pointing out manufacturing defects or anomalies, badly printed stickers, packaging, etc., in real-time can be achieved using edge analytics. Further, by embedding computing capability in the form of complex event processing (CPE), edge devices can filter out noisy data and collect only information that is deemed useful.
• Healthcare is another domain where a massive surge in the number of connected devices can be witnessed. In the future, a hospital room on an average will have around 15 to 20 medical devices, a majority of which are expected to be networked. A large hospital can have as much as 85,000 connected medical and IoT devices, putting a significant strain on the cloud network. Edge analytics and computing can reduce this burden to a great extent.

Scope of the Report

Edge analytics is a way to data collection and interpretation in which a programmed analytical computation is performed on data at a sensor, network switch or another device instead of waiting for the data to be sent back to a centralized data store.

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Key Market Trends

Retail Industry to Drive the Edge Analytics Market

• With a traditional data warehouse model, it takes significant bandwidth, time, and cost to transmit all the data to a central repository and extract the insights required to improve operations or customer interactions. Using edge analytics, retailers will be able to analyze all types of data real-time and capture fleeting opportunities to deliver hyper-relevant customer experiences and optimize operations, such as streamlining the checkout or ensuring items are in stock.
• Retail is experiencing huge data that is generating from the video camera installed in the store, in-store Wi-Fi networks, sensors, and data generated from apps. Much of the produced data is unstructured in nature, which provides valuable information. Leading retailers are utilizing edge analytics to deliver better user experience and maximize store performance. From location analytics to drive engagement to understand shopper’s pattern, retail giants Target and Walmart, among others, are using analytics at the edge of the network to draw insights from terabytes of data.
• Retailers can leverage data from a range of sensors including parking lot sensors, shopping cart tags, and store cameras. By applying smart analytics to the data, they can predict checkout wait times and preemptively alert store service managers when more registers are required to be opened, or even automatically open registers before customers get there. It also helps retailers to change their business model and reform their strategies to main competitive edge. The idea is not limited to targeting a group of audiences but offering personalized solutions for everyone with the help of behavioral targeting.

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