According to a recent report by Transparency Market Research (TMR), the edge analytics market is foreseen to project a strong growth with a noticeable CAGR (Compound Annual Growth Rate) of 27.6% within the forecast period.
What is Edge Analytics?
Edge analytics is the advanced data analysis method that enables users to get access to real-time processing and extracting the unstructured data captured and stored on the edge of network devices. Edge analytics provides the automatic analytical computation of generated data in a real-time mode without sending the data back to the centralized data store or server.
In this technique, data is collected, processed, and analyzed at the sensor, device, or touchpoint itself.
Benefits of Edge Analytics:
- Reduce the latency of data analytics:
If we are performing predictive maintenance then it will be beneficial to analyze the data at that particular sensor and shut off that sensor.
- Scalability of data analytics:
As sensors and devices grow, the data collected by them will also grow exponentially so Edge analytics enables organizations to scale their processing and analytics capabilities by decentralizing to the sites where the data is actually collected.
- Edge analytics helps get around the problem of low bandwidth environments:
Edge analytics alleviates this problem by delivering analytics capabilities in these remote locations.
Edge analytics will probably reduce overall expenses by minimizing bandwidth, scaling of the operations, and reducing the latency of critical decisions.
- Increased security due to decentralization:
Absolute control over the IP protecting data transmission, since it’s harder to bring down an entire network of hidden devices with a single DDoS attack, than a centralized server.
Use Cases of Edge Analytics:
Retail customer behavior analysis: Retailers can leverage data from a range of sensors, including parking lot sensors, shopping cart tags, and store cameras. By applying analytics to the data collected from these devices, retailers can offer personalized solutions for everyone with the help of behavioral targeting.
Remote monitoring and maintenance for various industries: Industries such as energy and manufacturing may require instant response when any machine fails to work or needs maintenance. Without the need for centralized data analytics, organizations can identify signs of failure faster and take action before any bottleneck can arise within the system.
Smart Surveillance: Businesses can use the benefit of real-time intruder detection edge services for their security. By using raw images from security cameras, edge analytics can detect and track any suspicious activity.
Tools for Edge Analytics:
- AWS IoT Greengrass
- Cisco SmartAdvisor
- Dell Statistica
- HPE Edgeline
- IBM Watson IoT Edge Analytics
- Intel IoT Developer Kit
- Microsoft Azure IoT Edge
- Oracle Edge Analytics (OEA)
- PTC ThingWorx Analytics
- Streaming Lite by SAP HANA
Challenges for Edge Analytics:
Security: Cloud environments are designed with security in mind because breaches on the cloud are quite costly for the business. However, edge security is also important because some edge devices make decisions about the real-world behavior of machines. Breaches can result in the sabotage of equipment, other costly machine errors, or at least misinformation.
Maintenance: Some edge analytics systems share only their output with the cloud due to bandwidth or storage constraints. Then, businesses have no chance to review the raw inputs that led to the analyses that are shared with the cloud systems. Therefore, they need to make sure that inputs are processed with the latest analytics software, relying on outdated models can lead businesses to make decisions on wrong information.
The greatest value of a picture is when it forces us to notice what we never expected to see.
-John Tukey, American Mathematician
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