{Zoom in: Edge Analytics} Skipping data centralization & jumping directly to analysis

The increasing popularity of connected devices and the constant evolution that Internet of Things (IoT) is experiencing, has led to generating large amounts of data at the edge of the network in several industries including retail, manufacturing, transportation, and energy.

How is the data from these devices being processed? Is everything being stored? Won´t it be better if one could filter out the unwanted data bits and just store the significant parts right at the time that the data is produced?

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That´s where edge analytics comes in. Edge analytics is the process of gathering, processing, and analyzing data at the edge of a network, within close proximity or right on a connected device. Whether descriptive or predictive, edge analytics is always in real-time and in-situ or on-site where data is being gathered.

The Edge Analytics Market

According to Verified Market Research, the edge analytics market has been valued at US$ 4.90 billion in 2019 and is expected to reach US$ 46.91 billion by 2027, growing at a CAGR of 35.23% from 2020 to 2027.

This market is being driven by the emergence of IoT and rising ICT expenditure by the government of various developed and developing nations

This market is being driven by the emergence of IoT and rising ICT expenditure by the government of various developed and developing nations. At the same time, this growth could be affected by issues concerning safety and security and lack of universally accepted standards.

Edge Vs. Regular Analytics

The difference between edge and regular analytics is the location of the analysis. Though edge analytics applications do require working on edge devices that can have memory, processing power or communication constraints.

It is different from edge computing though. An IBM post says edge computing ´is akin to the if/then construct in software programming; edge analytics takes the what if approach.´

Jumping Directly to Analysis

Historically, businesses gather data from IoT devices and sensors, store them in a central point such as a data lake or data warehouse and then perform analysis on that data to gain insights. With edge analytics, organizations have removed the step of data centralization and jumped directly to the analysis phase.

Edge analytics enables organizations to achieve autonomous machine behavior, higher data security, and decreased data transfer costs. It also facilitates faster decision making, especially in for low bandwidth.

Won´t it be better if one could filter out the unwanted data bits and just store the significant parts right at the time that the data is produced?

Some of the use cases of edge analytics include retail customer behavior analysis, where retailers can use data from an array of sensors, such as parking lot sensors, shopping cart tags, and store cameras. This kind of analytics can help retailers in gaining behavioral targeting by offering customized solutions.

Another use case is remote monitoring and maintenance in any industry. Especially with industries like energy and manufacturing, immediate responses is often required with machine failure.

Smart surveillance is another area, where organizations can offer real-time intruder detection edge services. With the help of raw images from security cameras, edge analytics can help identify and pursue irregular activity.

The Major Players

Since businesses are increasingly relying on automated and data driven decision making, edge analytics is becoming an area of significant interest for tech giants. For example, January last year, Apple acquired an Xnor.ai, an edge focused AI startup. Apple´s plans are to run deep learning analytics models on edge devices such as phones, IoT devices, cameras, drones, and embedded CPUs.

In a world filled with connected devices, edge analytics can hasten the pace of decision making while saving on costs

In addition, Google Cloud and AWS have edge IoT focused products in their kitty.

Sectors like retail, energy, security, manufacturing, and logistics can use edge analytics for the beneficial fast decision making.

A survey notes that the current major key players in the edge analytics sectors includes names like, Cisco, Oracle, SAS Institute, SAP SE, Apigee Corporation, AGT International Inc., Predixion Software, Foghorn Systems, CGI Group Inc., Analytic Edge, and Prism Tech.

Cost Effective Analysis

The world is getting flooded with edge devices that are empowered with system-on-chip platforms and high-resolution integrated cameras. Some examples are intelligent cameras, smartphones, augmented reality/virtual reality headsets, and industrial and home robots.

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Just yesterday, Purdue University researchers received an Amazon Research Award for a method they have developed to perform analytics on streaming video running on small devices linked together in the IoT.

In a world filled with connected devices, edge analytics can hasten the pace of decision making while saving on costs.