Since the advent of the internet, the world has changed drastically. The World Wide Web has democratised information and made the entire world as accessible as one republic. Albeit one governed by the rules of internet companies instead of a government.
Today, most of our interactions are reliant on the internet, at least to a certain extent. Everything from communication, entertainment, and security to payments. The impact of the World Wide Web is undeniable.
While the internet and the services that are based on it have taken much of the limelight, a key piece of this environment often gets overlooked; the IoT device network.
In recent years, IoT devices have exploded in popularity. A recent study found that 55 billion IoT devices are expected to power our households and businesses. IoT devices take the form of smart light bulbs to complex sensors powering planetary research rovers.
Many believe that IoT devices are the driving force behind the internet. Without these devices, the internet as we know it may not exist. These devices power the products and services we love by collecting and transmitting information to companies who operate them.
That said, the amount of data collected by these devices has created a bottleneck in data analytics. This is because analytics platforms are not powerful enough to analyse and produce insights in real life.
This has led to the development of IoT edge analytics—an analytics framework that eliminates bottlenecks by moving data analytics closer to the IoT devices.
In this post, we explore what IoT edge analytics is and its real-world applications.
What is the role of data analytics in IoT applications?
IoT devices act as the data collection tools where they are deployed. These devices stream data to centralised data processing systems which clean, process, and analyse these data sets to produce actionable insights.
Using these insights, IoT devices can help real-time decision making in IoT applications. Edge analytics is the next step in data analytics in IoT applications.
How is big data analytics important for IoT systems?
Without big data analytics systems, it would be impossible for organisations to derive any meaningful value from their IoT systems.
IoT needs analytics platforms because they generate a large volume of data. In fact, by 2025 IoT devices will be generating 79.4 zettabytes of data in real-time. The immense volume of data generated makes it hard to analyse the data efficiently.
Big data analytics platforms can analyse data on such a large scale because they feature machine learning that can extract useful insights from the data in real-time and reframe the insights in a framework relevant to the organisation’s goals.
However, not every analytics platform is suited for IoT systems. The analytics platform must have the right infrastructure and performance capabilities to analyse IoT data in real-time. This is where IoT edge analytics systems become essential.
IoT edge analytics refers to a data analytics framework where analytics capabilities are moved to, or closer to, the devices that make up the fringes or edges of the data analytics pipelines.
Traditionally, data analytics involved collecting data from various sources and migrating it to a centralised data lake or data warehouse to initiate the data analytics process. This, however, has proved to be inefficient as the amount of data collected in the current environment far outweighs the analytics capabilities; which means businesses incur increasing costs for data transmission and storage.
Edge analytics eliminates this issue by conducting data analytics closer to the data source and only transmitting the final results to the warehouse, reducing data transmitting and data storage costs.
This also allows for real-time analytics, which has paved the way for some clever implementations in the real world.
The transportation industry is one of the pioneers in IoT edge computing adoption. Technologies like auto parking, lane correction, and automated driving found in modern cars are powered by IoT analytics. Sensors in these cars collect and send information to the computer unit, which analyses the data and sends instructions to these driver-assist systems.
NASA also used edge analytics to execute the landing of the Perseverance Rover. Without edge analytics, NASA would not have landed the rover due to data transmission latency from Mars to Earth.
Quick decision making and tight tolerances are key to an efficient manufacturing function. By nature, manufacturing needs continuous monitoring to spot errors and optimise the production process.
That said, achieving this level of production efficiency is a hard task using human resources or traditional analytics methods. Edge analytics can streamline this process using IoT sensors to power error identification systems and production optimisation processes.
One of the more widespread uses of IoT analytics, machine learning, uses on-device processing to perform complex tasks without connecting to the cloud or the internet.
Your smartphone, for example, uses a neural processing unit to power smart assistants like Siri or Google Assistant to understand your voice commands and execute them.
In the current context, IoT devices help us simplify data analytics by eliminating the bottlenecks of traditional methods. It’s fair to say IoT edge analytics is the future of data analytics, and the internet experience as a whole.
When we think about agriculture, utilities, and transport—we think about IoT devices. The Internet of Things is no longer an optional benefit for a tech firm. It is an essential component of operations for almost any company in any industry. With over 27.1 billion devices to be deployed across a range of industries, like healthcare, it is important to understand how IoT devices work.
IoT devices contain petabytes of data, which could contain valuable information. Given the growing importance of IoT, adopting technology that allows organisations to analyse data from the source as soon as it is generated is important. This is where edge analytics software can be a huge boon.
IoT devices make several operations, like preventative maintenance, possible. There is one drawback to them, however, and that is time. The value promised from IoT devices only converts into tangible ROI when the right analytics software can convert the data into useful findings, in real-time. Take, for example, healthcare IoT devices.
One of the benefits of these devices is their ability to detect a person’s heart rate in real-time. This information can inform doctors when patients are undergoing a serious condition, like a heart attack.
The catch is that these readings only work when there is an analytics system that can analyse and present the findings in real-time. In such cases, the conventional data analytic pipeline will not work. It won’t be possible to have data transferred along this data pipeline, stored in a data lake, and then analysed.
This is where edge analytics software can make transformative changes to the production process. Rather than transporting data to a separate storage facility and then analysing it, edge analytics allow businesses to analyse data much closer to the source.
Instead of storing data in one location, edge analytics can analyse data closer to the point of origination. This means organisations don’t have to worry about setting up a central point of storage. Instead, they can focus on building out the comprehensive IoT network they need. This also has positive implications on data collection and analysis because most organisations can eliminate latency in data collection and processing.
How will edge analytics software work? Machine learning will play a crucial role in this function. Conventionally, the analytics model collects, stores, and prepares data for analysis. After that, an algorithm is chosen. The parameters of the algorithm and the data output, however, can vary depending on the IoT network.
This is where machine learning becomes an integral component of edge analytics. Machine learning enables organisations to find the right combination of data output and algorithm because it learns as it feeds data.
This is quite transformative because it gives organisations the option to maximise efficiency in their data collection and analysis process. The development is especially crucial when you consider that most IoT networks are deployed across the cloud. Companies in different industries, like utilities and transport, will be relying on a cloud-based IoT network for most of their operations.
Most importantly, edge analytics allows for the evolution of IoT networks. By removing some of the existing constraints, we can see IoT devices pave the way for some transformative operations. For example, IoT building technology will be able to measure a variety of factors that go beyond worker productivity.
IoT devices can measure energy efficiency, worker health and safety, along with a host of other factors that can help executives determine the overall scope and scale of their employees’ well-being, both mentally and physically. While organisations are looking into their well-being by investing in various programmes, IoT devices can measure employee well-being in ways that weren’t previously possible and get a better understanding of how effective these programmes are.
As the world’s industries become more and more dependent on IoT devices, adopting the right data analytics software can give organisations the edge they need to maximise ROI on IoT devices. Edge analytics can provide the competitive advantage that organisations need.
Visit Selerity to know more about edge analytics software and other data analysis platforms.
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!
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.
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.
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.
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.
Visit our website for more information on how edge analytics is refining our approach to data analysis.