Computing Devices: As more businesses and consumers see the value of connectivity, IoT devices are experiencing widespread adoption. Consumers can enhance their daily lives, while businesses can gather and analyze valuable customer and business data to improve customer experience and product development.

With this innovation comes a new challenge – what to do with all that data. The volume of data is more than anyone could have anticipated, and businesses are overwhelmed. All data needs to be gathered, processed, analyzed, and filtered to get actionable insights. If there’s too much to do, businesses can’t get a full-picture view of their operations or use the most relevant data for decision-making.

Data collection outpaced traditional computing. Traditional computing methods are limited in their ability to process large volumes of data efficiently and accurately. In addition, the very process of transferring and analyzing data causes significant delays and network traffic and congestion that inhibit real-time insights.

The Role of Computing Devices with Data Processing

Data offers in-depth insights into businesses, products, sales, trends, and customers. No matter the industry or purpose, IoT devices can be used to collect data that businesses can use to optimize processes.

With the right data, businesses can make informed decisions with important processes in their organization. Whether it’s a flash sale sent to customers in the area, remote monitoring equipment in remote or inhospitable environments, or healthcare workers monitoring patient information, data matters.

The key is how quickly the data can be used, however. If the decision requires a rapid response, having the data delayed in processing and analysis can be detrimental. For IoT devices that are located in remote or dangerous places where humans shouldn’t be, missing out on data only reaffirms the need for human employees doing the work.

All this data comes with a price. Traditional computing processes can’t handle these high volumes of data from IoT devices, especially not to transfer, process, and analyze it in time to take relevant action. Any computer, no matter how sophisticated or advanced, can process an infinite amount of data quickly.

Until now, cloud computing has been the industry standard for data processing. There are a lot of good reasons for that, including the scalability of the data core, the high volumes of data it can store, and its access from anywhere.

These data cores are often located away from the source of the data, however. sometimes, the data core may be hundreds or thousands of miles from the data source. Data has to travel a long way for processing and analysis, then make the trip back to the device for action.

If the network is lagging from bandwidth limitations, latency, or security vulnerabilities, the delays can make the insights and action irrelevant.

Data Processing with Edge Computing

With millions of IoT devices collecting data from all over the world, an array of cloud storage cores, and only more opportunity looming in the near future, the demand for computing capability will only grow. The networks are getting congested and suffering delays, which ruins the user experience.

The answer is to leverage the power of the device itself and the network’s edge. IoT is innovative in that it can collect data without human intervention. It’s not always feasible to move devices closer to the core – and it’s nearly impossible to move the core in most cases.

Edge computing takes care of this by processing and analyzing the data on the network’s edge, close to the device and end user. With a shortened distance between the points, data doesn’t have to travel as far and is less susceptible to breaches, delays, and other performance issues.

With edge computing, such as the NXP i.MX 6, the data doesn’t need to travel hundreds of miles to get to the cloud storage core. It’s all processed on the edge, right by the data source, and can deliver faster insights. Only the important data goes to the cloud, reducing the overall traffic and congestion.

While IoT networks are distributed systems, they are usually managed in a centralized way. Edge computing is a distributed solution, which aligns better with IoT device networks and manages data more efficiently.

IoT Devices with AI

Edge computing is an innovative solution to the data processing problem, but it’s not a singular one. Though edge computing is improving and becoming more functional, capable, and high-performance, so are IoT devices and networks. Edge computing should be considered as a complement to other solutions, such as artificial intelligence (AI).

With AI, edge computing can handle the data processing and analysis, but instead of sending actionable insights to the cloud, it can be sent back to the device for rapid action. The decision-making process is then put on the device, saving time and traffic.

This is also known as edge intelligence. AI-enabled IoT devices are incredible for businesses looking to take a hands-off approach to analytics while still gaining insights. AI and edge computing allow the device to act on its own and implement the actions, and AI will continue to learn and grow with more patterns and experiences.

Ai-enabled IoT devices work with edge computing and are optimized to process larger and more sophisticated data sets near the network’s edge. The cloud-based storage core can be used to store and analyze mission-critical data, leaving the time-sensitive actions to the edge.

Plenty of industries are already using AI-enabled IoT devices and edge computing, but it’s not nearly as widespread as it could be. 5G is seeing increasing adoption, due to its ability to improve peak data speeds, reduce latency, and provide better reliability – important demands for the connected world.

With 5G, AI and edge computing can create a powerhouse for data processing and analytics. By leveraging the strengths of the three to mitigate their respective weaknesses, businesses can experience efficient, scalable, and intelligent IoT networks.

Edge Computing for the Future of IoT

Data is vital to all businesses, regardless of industry. The market is more competitive than ever, and the only way to get a serious edge is by leveraging business and customer data to provide better experiences. Traditional computing can also go so far, but edge computing can ensure that data is processed and analyzed quickly for rapid insights.

Author Bio: Jason Khoo

Jason is the Head of SEM at SolidRun which is a global leading developer of embedded systems and network solutions, focused on a wide range of energy-efficient, powerful and flexible products which help OEMs around the world simplify application development while overcoming deployment challenges