Edge analytics refining our data approach
Edge analytics is the new hot buzzword. With industries looking for any chance to optimise their systems for incremental improvements, any analytics platform that can refine the data analysis process is a huge boon and is bound to garner a lot of attention. The edge analytics market is expected to grow from $1.94 billion in 2016 to $7.96 billion in 2021, with a growth rate of 32.6 per cent.
With edge analytics generating this much interest and traction, I think we need to delve behind the curtains of this new entree and figure out what exactly is going on in there!
What exactly is Edge analytics?
According to Gartner’s report, edge analytics is the method that will enable users to leverage data analytics to go beyond conventional business insights and increase operating efficiency. The analytics platform aims to accomplish this by zooming into the smallest detail with precision to make analysis more accurate and relevant.
First, let us break down what this new buzzword even means. Edge analytics refers to the approach of capturing, monitoring, and analysing the data from edge network devices such as sensors, routers, gateways and switches. The analytical computation is done at the edge of these devices in real-time, without waiting for the data to be sent to a centralised storage system, then the system computes the analytical applications and sends commands back.
Edge analytics is an innovative addition to data collection and analysis because it reduces decision-making latency on connected devices, improves the rate of data processing and increases deployment scalability and effectiveness.
How does it help?
According to the International Data Corporation, the growing number of IoT devices will increase the amount of data available to 79.4ZB by 2025. This results in a massive accumulation of unmanageable data, 73% of which will not be used.
Edge analytics is believed to address these problems by running the data through an analytics algorithm as it’s created, at the edge of a corporate network. This allows organisations to set parameters on what information is worth sending to a cloud or an on-premise data store — and what data offers little value.
With edge analytics, we will see better data security due to decentralisation. Having devices on the edge gives absolute control over the IP protecting data transmission. It also ensures that applications are not disrupted in case of limited network connectivity. Furthermore, your expenses are driven down with edge analytics minimising bandwidth, scaling operations and reducing latency of critical decisions.
Without the need for centralised data analytics, organisations can identify signs of failure faster and take action before any bottleneck can arise within the system.
Where does edge analytics fit in?
The edge analytics model enables users to generate valuable and actionable insights in real-time, bringing order to unstructured content and feeding relevant data to cognitive-oriented systems.
Edge analytics is in demand and its features could be leveraged by most industries to supercharge their operations. For example, remote monitoring, maintenance and smart surveillance could be utilised for a diverse spectrum of industries. Industries, such as energy and manufacturing, may require instant response when any machine fails to work or needs maintenance.
Organisations can use smart surveillance and benefit from 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. Local and national governments have invested in edge analytics to boost public infrastructure effectiveness. For example, analysing data from sensors triggers real-time action, a function which can be used to improve security.
Sensors on trains can trigger stop signs in the event of an emergency without human intervention, send a message to the police or alert the fire department instantly. Edge analytics can go a long way in boosting security and improving the quality of public services using already existing resources, making them a worthwhile investment for local and national governments.
The future is edge analytics
Edge analytics is said to be the future of data analytics because of its ability to optimise data collection and analysis from network devices. In some cases, it is already preferred over conventional data analytics systems.
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