Edge Analytics Explained in 5 minutes or Less [+ 5 Tools]

Edge analytics helps smart and data-oriented businesses to go straight to data analysis after data collection by IoT devices.

Traditionally, businesses would collect data from various sources, store it in a cloud or on-premise storage, and analyze it later. However, this data analysis model is a vital bottleneck for the growth of the Internet of Things (IoT) and the Industrial Internet of Things (IIoT).

Edge analytics is the answer!

This article will take you through a concise journey of analytics on the edge so that you can develop solutions or transform digital businesses effortlessly.    

Introduction to Edge Analytics

As the name suggests, edge data analytics is the data analytics method at the edge. Edge means the source of data. For IoT, these are sensors, actuators, robotic arms, HVACs, conveyor controls, network switches, and smart devices.

Edge analytics applications perform data analysis closer to the IoT device that collects real-time data from manufacturing units, utility systems, etc. Thus, time-critical business processes can run smoothly without waiting for logical inputs from a central server.

In a nutshell, data collection, processing, analysis, and actions happening within a smart device result from edge data analytics. For example, Amazon Echo or Nest Home devices come with edge analytics. 

These devices listen to your commands. Analyses captured audio into machine language that searches the web for results. The device also presents the query result available on the internet.    

Need for Edge Analytics

The usage of smart devices in the industries like energy, retail, manufacturing, security, logistics, automobile, etc., is continually growing. But, the internet bandwidth is not growing at the same speed, or the bandwidth is always limited. 

Hence, collecting terabytes of data from IoT devices and transferring those to the cloud is time-consuming. Not to mention analyzing the data and sending back actionable insight to the smart device via the same network.

It will create a traffic jam and disable the IoT system network!

Here, businesses must use edge analytics applications and devices. The time-critical smart devices will be able to analyze the collected data on-site and take action instantly. 

For example, an autonomous vehicle must brake if it detects a sudden and unwanted obstacle on its path. 

It can not wait to collect the audio-visual data of the obstacle, send it to a cloud app, and wait for input. Instead, the vehicle makes a split-second decision to change direction or engage in emergency breaks.     

How Does Edge Analytics Work?

Analytics on edge usually monitor multiple arrays of edge or IoT devices. Primarily, an analytics app tracks the health and performance of all the connected smart devices. 

If it detects workflow issues, the analytics app tries to rectify the problem locally. If the problem persists, the edge application stops the faulty device. Then, it notifies the human technicians. 

During this orchestrated pathway, the following devices perform critical roles: 

• IoT sensors collect environmental data like pressure, temperature, humidity, RPM, etc.
• Edge devices could be dedicated edge appliances like Sony REA-C1000 for on-site data analytics or smartphones and tablet to control IoT devices.
• Edge gateways boast more power and memory than edge devices and function as an intermediary between the cloud server and IoT devices. 
• Smart actuators that perform the task edge data analytics suggest. For example, smart water valves, smart switches, smart robotic arms, smart conveyor controls, and computer commands.

The above image shows a schematic representation of IBM IoT Edge Analytics in the hospitality management sectors like hotels.   

Benefits

#1. Greater Security

In analytics on edge, there is no need to transfer the data to the cloud. The raw data stays on the device where it got generated. Since there is no chance of data getting hacked or infected in transit, it stays more secure.

#2. Latency Prevention and Near Real-Time Data Analysis

Certain business processes require immediate data analysis for operations. Edge Analytics helps them in autonomous decisions by identifying and collecting the insights at the source.

As this analysis happens near the data, it takes a little time. It involves no data transmission to remote servers, so you get instant results.

In scenarios like identifying criminals from live CCTV feeds or analyzing data from an aircraft or manufacturing plant, you only get split seconds to make the call. There, using this technology helps you make instant decisions.

#3. High Scalability

As companies scale up, the growing number of data puts more burden on central data analytics. Through decentralization of the process, edge analytics allows you to scale the processes providing better analytics capabilities.

#4. Less Bandwidth Usage

Data transfer from the source devices to the central server and vice versa uses a hefty amount of bandwidth. Many remote locations don’t have the necessary data bandwidth or network strength for transmission. In such cases, edge analytics spares you using bandwidth.

#5. Reduced Cost

Conventional big data analytics methods will cost you a lot of money. While companies may process the data in their cloud server or public cloud solutions, storage, processing, analytics, and bandwidth consumption are expensive.

This technology uses IoT devices or nearby hardware for data analytics. As a result, there will be less cost for analysis and internet network bandwidth.

Limitations

#1. Remote Devices Security

While analytics on edge protects your sensitive data from cybersecurity threats during the data transmission, it involves remote devices vulnerable to such risks. 

There have been several incidents of security camera hacking, and yours, too, can fall victim to such attacks. If your cybersecurity measures do not cover these remote devices, having strong security for your core system will not help.

#2. Lost Data

The design of edge analytics enables it to use the most relevant data for analysis. The rest of the data from the large raw dataset gets ignored.

As this technology only stores these relevant instances in the central server, it may not be the best approach for the companies that need to receive and store all your raw data.

#3. Device and Network Compatibility

Analytics on edge is a new technology, so there might be compatibility and data transmission issues if you use old devices and network technology. So, companies must purchase new devices to deploy this technology in their organization. 

Consequentially, this will increase the cost of edge analytics for that company. In addition, it might require a full system upgrade that can disrupt operations.

#4. Need for Developing Own Solution

There are various analytical platforms available for this task. However, some companies might need a personally-developed edge analytics platform depending on the devices they need to analyze.

#5. Choosing the Right Software

Some systems available in the market only share their output data on the cloud. Hence, companies fail to see the raw source data behind the analysis. To avoid this, you need to use the latest analysis software to get hold of all the necessary data.

#6. Needs Usability Assessment

It is most suitable for security, efficiency, and quick decision-making scenarios. So, companies should assess whether they need it before opting for the solution.

Use Cases

Analyzing Customer Behavior

Retailers collect data from their store cameras, parking sensors, and shopping cart tags through an array of sensors. With edge analytics, these companies can utilize this data to offer customized solutions to their customers according to their behavior.

Remote Monitoring and Maintenance

Manufacturing and energy industries need immediate responses or alerts when machines stop functioning or require maintenance. Instead of centralized data analytics, it is the right technology for faster identification of future bottlenecks.

Intelligent Surveillance

It is also useful for real-time intruder detection. Businesses can utilize this service to increase their security. This technology uses raw images from CCTV to locate and track any suspicious activity.

Failure Prediction

IoT hardware failure can turn out to be disastrous. Edge analytics of these IoT hardware devices can accurately predict such issues. With its help, organizations can take proactive measures and increase uptime.

Currently, analytics on edge mostly uses custom devices and apps for specific industrial use cases. Find below some tools and devices to know the trend: 

Sony Edge Analytics Appliance

The REA-C1000 from Sony is a full-functional edge analytics device in existence so far. You can connect Sony network cameras with it to capture and analyze live presentations for remote viewers.

It has high-tech features like Handwriting extraction, content overlay, autonomous content, tracking presenter, image splitting, audience gesture tracking, and more.      

AWS IoT GreenGrass

AWS IoT GreenGrass is an open-source cloud service and edge runtime to develop, deploy, and control IoT device software.

It brings logic and cloud data processing to the local IoT devices. Hence, devices can function in low or intermittent network bandwidths.   

HPE Edgeline

HPE Edgeline is suitable for the rugged usage of smart devices in manufacturing plants, oil rigs, etc. It brings edge software and operational technology (OT) hardware directly to the production floor. 

Hence, smart devices can quickly get input from an on-site data processing system rather than cloud servers.    

Intel IoT Developer Kit

You can use software and hardware from Intel to develop edge analytics-based smart devices for business use. The toolkit includes the following products: 

• Software stack with drivers, SDKs, OS, samples, and libraries
• Intel Distribution of OpenVINO
• Intel Movidius VPU
• Intel Arria 10 FPGA

Azure IoT Edge

Azure IoT Edge brings analytics and AI workloads to smart devices that operate at the edge. This edge analytics development platform includes the following features: 

• IoT edge hardware from trusted vendors
• Free edge runtime
• Business logic module to run software on the edge
• Azure cloud interface

Edge vs. Traditional Analytics

The primary difference between edge analytics and traditional/server analytics is the place of data analysis. 

On edge systems, data analytics takes place near or on the IoT device that collects data and executes commands. Contrarily, server analytics take place far from the smart device that collects data.

You can find other notable differences in the following table: 

FAQs

Final Words

So, now you know what edge analytics is, how it works, its benefits, tools, use cases, and more.

You can now confidently make business decisions to retrofit your IIoT systems with edge analytics appliances to control remote devices quickly. 

Alternatively, the article will help you design or develop novel IoT and IIoT solutions if you are an IoT engineer or developer.

Next, you can check out the popular IoT devices.